topics for methodology research

Top 100 Research Methodology Project Topics

Research methodology might sound like a fancy term, but it’s simply the way researchers go about investigating a question or problem. Think of it as a roadmap for your project, guiding you through the steps to find answers. It’s crucial to pick the right methodology because it determines how you collect and analyze data, which affects the reliability of your findings. So, let’s check 100 research methodology project topics below.

Types of Research Methodologies

Table of Contents

There are mainly three types of research methodologies: quantitative, qualitative, and mixed-methods.

Quantitative Research Methodology

Quantitative research focuses on collecting numerical data and analyzing it statistically. It’s great for measuring things objectively.

For instance, if you’re studying how many people prefer coffee over tea, quantitative research can provide concrete numbers.

Qualitative Research Methodology

Qualitative research, on the other hand, dives deep into understanding people’s experiences, feelings, and behaviors. It’s like peeling an onion layer by layer to reveal the underlying emotions and motivations.

For example, if you want to explore why some students struggle with math, qualitative research can uncover personal stories and perspectives.

Mixed-Methods Research

Sometimes, researchers use a combination of quantitative and qualitative methods, known as mixed-methods research.

This approach offers a more comprehensive understanding of a topic by blending numerical data with rich narratives. It’s like having the best of both worlds.

Factors Influencing Choice of Research Methodology

Several factors influence the choice of research methodology:

• Nature of the research question: Is it about measuring something objectively or understanding complex human behaviors?
• Availability of resources: Do you have access to the tools and expertise needed for a particular methodology?
• Time constraints: How much time do you have to conduct the research?
• Ethical considerations: Are there any ethical concerns related to your research methods?

Steps Involved in Research Methodology for Project Topics

Regardless of the chosen methodology, research typically follows these steps:

• Problem Definition: Clearly define the research question or problem you want to address.
• Literature Review: Explore existing research and theories related to your topic to build a solid foundation.
• Selection of Research Design: Choose the appropriate methodology based on your research question and objectives.
• Data Collection: Gather relevant data using surveys, interviews, observations, or experiments.
• Data Analysis: Analyze the collected data using statistical tools (for quantitative research) or thematic analysis (for qualitative research).
• Interpretation of Results: Draw conclusions based on your analysis and discuss their implications.

Best Practices in Research Methodology for Project Topics

To ensure the quality and integrity of your research, follow these best practices:

• Ensuring validity and reliability of data: Use reliable measurement tools and sampling techniques to minimize errors.
• Ethical considerations in research: Obtain informed consent from participants, protect their privacy, and avoid any form of deception.
• Proper documentation and citation: Keep detailed records of your research process and cite all sources properly to avoid plagiarism.
• Peer review and feedback: Seek feedback from peers and experts in your field to improve the quality of your research.
• The impact of online surveys on response rates and data quality.
• Comparing the effectiveness of focus groups and individual interviews in marketing research.
• Analyzing the ethical considerations of using social media data for research.
• Exploring the potential of big data analytics in social science research.
• Evaluating the reliability and validity of mixed-methods research approaches.
• Examining the role of cultural sensitivity in international research projects.
• Investigating the challenges and opportunities of conducting research in conflict zones.
• Analyzing the effectiveness of different strategies for recruiting research participants.
• Exploring the use of action research methodologies in addressing real-world problems.
• Evaluating the impact of researcher bias on the research process and outcomes.
• Investigating the potential of citizen science for collecting and analyzing data.
• Exploring the use of virtual reality in conducting research studies.
• Analyzing the ethical considerations of conducting research with vulnerable populations.
• Evaluating the effectiveness of different strategies for disseminating research findings.
• Examining the role of storytelling in qualitative research.
• Investigating the use of visual methods in research, such as photography and video.
• Analyzing the challenges and opportunities of conducting longitudinal research studies.
• Exploring the use of case studies in research projects.
• Evaluating the effectiveness of different strategies for coding and analyzing qualitative data.
• Examining the role of theory in research design and analysis.
• Investigating the use of discourse analysis methodologies in research.
• Analyzing the strengths and limitations of quantitative research methods.
• Exploring the use of experimental research designs in social science research.
• Evaluating the effectiveness of different sampling techniques in research.
• Examining the role of research ethics committees in ensuring the ethical conduct of research.
• Investigating the challenges and opportunities of conducting research online.
• Analyzing the impact of social media on public perceptions of research.
• Exploring the use of gamification in research to increase participant engagement.
• Evaluating the effectiveness of different strategies for data visualization.
• Examining the role of open access in making research findings available to a wider audience.
• Investigating the challenges and opportunities of interdisciplinary research collaborations.
• Analyzing the impact of political and economic factors on research funding.
• Exploring the use of participatory action research methodologies to empower communities.
• Evaluating the effectiveness of different strategies for knowledge mobilization.
• Examining the role of research in informing policy and practice.
• Investigating the use of artificial intelligence in research methodologies.
• Analyzing the ethical considerations of using facial recognition technology in research.
• Exploring the potential of blockchain technology to improve data security and transparency in research.
• Evaluating the effectiveness of different strategies for engaging with stakeholders in research projects.
• Examining the role of reflexivity in qualitative research.
• Investigating the use of narrative inquiry methodologies in research.
• Analyzing the strengths and limitations of case studies as a research method.
• Exploring the use of secondary data analysis in research projects.
• Evaluating the effectiveness of different strategies for managing and storing research data.
• Examining the role of research assistants in the research process.
• Investigating the challenges and opportunities of conducting research in developing countries.
• Analyzing the impact of climate change on research methodologies.
• Exploring the use of citizen science for environmental monitoring.
• Evaluating the effectiveness of different strategies for conducting research with indigenous communities.
• Examining the role of research in promoting social justice.
• Investigating the historical development of research methodologies.
• Analyzing the impact of technological advancements on research practices.
• Exploring the use of mixed methods research approaches in different disciplines.
• Evaluating the effectiveness of different strategies for managing research projects.
• Examining the role of research funders in shaping research agendas.
• Investigating the challenges and opportunities of conducting research across different cultures.
• Analyzing the impact of language barriers on research communication.
• Exploring the use of collaborative online platforms for conducting research.
• Evaluating the effectiveness of different strategies for promoting research skills development.
• Examining the role of research misconduct in undermining public trust in research.
• Investigating the challenges and opportunities of conducting research with children.
• Analyzing the impact of research on mental health and well-being.
• Exploring the use of arts-based research methodologies.
• Evaluating the effectiveness of different strategies for recruiting and retaining research participants.
• Examining the role of research networks in supporting researchers.
• Investigating the challenges and opportunities of conducting research in the private sector.
• Exploring the use of open science practices to promote research transparency and reproducibility.
• Evaluating the effectiveness of different strategies for mentoring and supporting early-career researchers.
• Examining the role of research misconduct in retracting scientific articles.
• Investigating the challenges and opportunities of data sharing in research.
• Analyzing the impact of open data initiatives on scientific progress.
• Exploring the use of crowdsourcing in research to gather data and solve problems.
• Evaluating the effectiveness of different strategies for promoting research impact.
• Examining the role of alternative research metrics in evaluating the quality of research.
• Investigating the use of bibliometrics to analyze research trends and identify emerging areas.
• Analyzing the impact of research on public policy and decision-making.
• Exploring the use of participatory research methodologies to empower communities.
• Evaluating the effectiveness of different strategies for communicating research findings to the public.
• Examining the role of social media in disseminating research findings.
• Analyzing the impact of humanitarian aid on research practices in developing countries.
• Exploring the use of research methodologies to address global challenges, such as climate change and poverty.
• Evaluating the effectiveness of different strategies for building research capacity in developing countries.
• Examining the role of international research collaborations in promoting global research excellence.
• Investigating the challenges and opportunities of conducting research in the field of artificial intelligence.
• Analyzing the ethical considerations of using autonomous robots in research.
• Exploring the potential of artificial intelligence to automate research tasks.
• Evaluating the effectiveness of different strategies for mitigating the risks of bias in artificial intelligence-powered research.
• Examining the role of research in shaping the future of work.
• Investigating the impact of automation on research jobs.
• Exploring the use of new technologies to improve research efficiency and productivity.
• Evaluating the effectiveness of different strategies for developing transferable skills for researchers.
• Examining the role of lifelong learning in maintaining research expertise.
• Investigating the impact of research funding cuts on research quality and innovation.
• Exploring the use of alternative funding models, such as crowdfunding and philanthropy, to support research.
• Evaluating the effectiveness of different strategies for advocating for increased research funding.
• Examining the role of research universities in driving innovation and economic growth.
• Investigating the impact of research on social and cultural change.
• Exploring the future of research methodologies in an ever-changing world.

Examples of Research Methodology Project Topics

Here are some examples of project topics suited for different research methodologies:

Quantitative Research Topics

• The impact of social media usage on mental health among teenagers.
• Factors influencing customer satisfaction in the hospitality industry.

Qualitative Research Topics

• Exploring the experiences of first-generation college students.
• Understanding the challenges faced by small business owners during the COVID-19 pandemic.

Mixed-Methods Research Topics

• Assessing the effectiveness of a school bullying prevention program .
• Investigating the relationship between exercise habits and stress levels among working adults.

Research methodology is like a compass that guides you through the journey of inquiry. By understanding the different types of methodologies, factors influencing their choice, and best practices, you can embark on your research methodology project topics journey with confidence.

Remember, the key to successful research lies in asking the right questions and choosing the appropriate methodology to find the answers.

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111+ Best Research Methodology Project Topics for Students

Research methodology is the process of how we do research in academic studies, like how we ask questions and gather information. 

It’s like a roadmap that guides us through the research journey. A strong research methodology is essential because it helps students conduct their studies effectively, ensuring that their findings are reliable and valuable. 

This blog aims to help students, researchers, and teachers find interesting project ideas across different subjects by providing a curated list of research topics and guiding them on how to approach their research effectively. We want to make the process of choosing and conducting research projects easier and more enjoyable for everyone involved. 

Let’s dive into the world of research methodology project topics and explore the possibilities together!

What is the Research Methodology?

Table of Contents

Research methodology refers to the systematic process of planning, conducting, and analyzing research studies. 

It involves the techniques, procedures, and approaches used to gather, interpret, and evaluate data in order to answer research questions or test hypotheses. 

Research methodology encompasses various methods, such as experiments, surveys, interviews, observations, and literature reviews, among others. 

It provides a structured framework for researchers to ensure the reliability, validity, and ethical integrity of their studies, ultimately leading to credible and meaningful research outcomes. 

In essence, research methodology is the roadmap that guides researchers through the entire research process, from formulating research questions to drawing conclusions based on empirical evidence.

Importance of Research Methodology Project Topics in Academia

Here are the key points highlighting the importance of research methodology project topics in academia:

1. Hands-on Learning Experience

Engaging in research projects allows students to apply theoretical concepts learned in the classroom to real-world scenarios, enhancing their understanding and retention of course material.

2. Development of Critical Skills

Research methodology projects help students develop critical thinking, problem-solving, and analytical skills as they design studies, collect and analyze data, and draw conclusions based on evidence.

3. Contribution to Knowledge

By conducting research, students contribute to the advancement of knowledge within their respective fields, uncovering new insights, perspectives, and solutions to existing problems.

4. Promotion of Innovation

Research projects often lead to the discovery of new ideas or approaches, fostering innovation and creativity within academia and beyond.

5. Exploration of Interests

Research methodology project topics allow students to explore topics of personal interest, fostering a sense of curiosity and passion for learning.

6. Preparation for Future Careers

Engaging in research projects prepares students for future academic or professional endeavors by providing them with valuable research experience and skills highly sought after in various industries.

7. Enhancement of Academic Profile

Research projects can enhance students’ academic profiles by providing evidence of their research capabilities, potentially leading to opportunities for scholarships, internships, or graduate studies.

Interesting Research Methodology Project Topics for Students

Here are some interesting research methodology project topics for students across various disciplines:

Social Sciences

• Impact of social media on mental health among adolescents
• Gender inequality in the workplace: A comparative analysis
• Effects of globalization on cultural diversity
• The role of family structure in child development outcomes
• Political polarization and its implications for democracy
• Factors influencing voter turnout in elections
• Social determinants of health disparities among minority populations
• Impact of immigration policies on host countries’ economies
• Social stigma associated with mental illness: A cross-cultural perspective
• Community policing and its effectiveness in reducing crime rates
• Social media activism and its impact on social movements
• Cultural perceptions of beauty and body image across different societies
• The effectiveness of online learning platforms in higher education
• Factors influencing student engagement in STEM education
• Impact of parental involvement on children’s academic achievement
• Strategies for promoting inclusive education for students with disabilities
• The role of technology in transforming classroom learning experiences
• Cross-cultural differences in teaching methodologies and learning outcomes
• Assessment methods for measuring student learning outcomes
• School bullying prevention strategies: A comparative analysis
• Effects of standardized testing on curriculum design and instruction
• Teacher professional development and its impact on student achievement
• Multilingual education and its benefits for language acquisition
• Education policies and their influence on educational equity and access
• The relationship between personality traits and academic performance
• Effects of early childhood trauma on adult mental health outcomes
• Cognitive biases and decision-making processes in individuals
• Psychological factors influencing consumer behavior and marketing strategies
• The impact of social support on coping mechanisms for stress
• Cross-cultural differences in emotional expression and regulation
• Psychological effects of social isolation and loneliness
• Mental health stigma and its impact on help-seeking behaviors
• The role of mindfulness practices in promoting well-being
• Psychological effects of social media use on self-esteem and body image
• Attachment styles and their influence on romantic relationships
• Psychological interventions for managing anxiety and depression

Health Sciences

• Factors influencing healthcare disparities among marginalized communities
• The effectiveness of telemedicine in improving access to healthcare services
• Impact of lifestyle interventions on chronic disease prevention
• Barriers to healthcare access for undocumented immigrants
• The role of genetics in personalized medicine approaches
• Mental health implications of the COVID-19 pandemic
• Healthcare provider-patient communication and its impact on treatment adherence
• Strategies for promoting healthy aging and longevity
• Environmental factors influencing public health outcomes
• Access to reproductive healthcare services: A global perspective
• Healthcare workforce diversity and its implications for patient care
• The intersection of mental health and chronic illness management

Environmental Sciences

• Impact of climate change on biodiversity conservation efforts
• Sustainable agriculture practices for food security in developing countries
• Effects of pollution on aquatic ecosystems and biodiversity
• Renewable energy sources and their potential for mitigating climate change
• Urbanization and its effects on air quality and public health
• The role of ecosystem services in human well-being
• Environmental justice and its implications for marginalized communities
• Conservation strategies for endangered species preservation
• Waste management solutions for reducing environmental pollution
• Effects of deforestation on local and global climate patterns
• Environmental impacts of industrialization and urban development
• The importance of environmental education in promoting sustainability

Business and Economics

• Impact of corporate social responsibility on consumer behavior
• Factors influencing entrepreneurial success in emerging markets
• Effects of globalization on small and medium-sized enterprises (SMEs)
• Economic implications of automation and artificial intelligence
• The role of government policies in promoting economic development
• Financial literacy and its impact on personal financial management
• International trade agreements and their effects on global economies
• Strategies for reducing income inequality and poverty
• The gig economy: Challenges and opportunities for workers
• Corporate governance practices and their impact on firm performance
• Economic impacts of natural disasters and pandemics
• The role of central banks in managing inflation and monetary policy

Technology and Computing

• Ethical considerations in artificial intelligence and machine learning
• Cybersecurity threats and countermeasures in the digital age
• The impact of social media algorithms on information dissemination
• Trends in wearable technology and their implications for healthcare
• Big data analytics and its applications in business decision-making
• The future of autonomous vehicles: Challenges and opportunities
• Internet of Things (IoT) applications for smart cities
• Accessibility considerations in software design and development
• Green computing: Strategies for reducing energy consumption in IT
• Digital divide: Addressing disparities in internet access
• Blockchain technology and its potential for revolutionizing industries
• Human-computer interaction design principles for user-friendly interfaces

Arts and Humanities

• Cultural representations of gender and sexuality in literature and media
• The evolution of artistic movements and their socio-cultural impacts
• Preservation of cultural heritage sites in the face of urbanization
• The role of music therapy in mental health treatment
• Ethical considerations in documentary filmmaking
• Digital humanities: Exploring new avenues for scholarly research
• Cultural appropriation versus cultural appreciation in the arts
• The influence of religion on artistic expression throughout history
• Intersectionality in feminist literature and activism
• Indigenous knowledge systems and their contributions to sustainable development
• Cultural diplomacy and its role in international relations
• Representations of race and ethnicity in contemporary art forms

Science and Engineering

• Advancements in renewable energy technologies for sustainable development
• The role of nanotechnology in biomedical applications
• Artificial intelligence in drug discovery and development
• Biologically inspired engineering solutions for environmental challenges
• Space exploration: Challenges and opportunities for scientific discovery
• Innovations in materials science and their impact on industry
• The future of 3D printing: Implications for manufacturing and design
• Engineering solutions for mitigating natural disasters
• Sustainable transportation systems
• Biomedical engineering advancements in prosthetics and medical devices
• Green chemistry approaches for sustainable manufacturing processes
• Robotics and automation in enhancing workplace productivity and safety

Communication and Media Studies

• Effects of media portrayals on body image and self-esteem
• The role of social media influencers in shaping consumer behavior
• Media literacy education: Empowering critical thinking skills in the digital age
• Representation of marginalized communities in mainstream media
• The impact of fake news and misinformation on public discourse
• Cross-cultural communication challenges in a globalized world
• The evolution of advertising strategies in response to technological advancements
• Media censorship and freedom of expression: Balancing competing interests
• Journalism ethics in the era of citizen journalism and social media
• The influence of celebrity endorsements on brand perception and consumer behavior
• Media coverage of political events and its impact on public opinion
• The future of media consumption: Trends in streaming services and digital platforms

These research methodology project topics cover a wide range of disciplines and provide ample opportunities for students to explore, research, and contribute to their respective fields of study.

Factors to Consider When Selecting a Research Methodology Project Topic

When selecting a research methodology project topic, several factors should be taken into consideration to ensure a successful and meaningful research endeavor. Here are some key factors to consider:

• Interest and Passion: Choose a topic that genuinely interests you and aligns with your passions. Research projects require dedication and perseverance, so selecting a topic that you are enthusiastic about will keep you motivated throughout the process.
• Relevance: Ensure that your chosen topic is relevant to your field of study, academic program, or career goals. Consider current trends, pressing issues, or gaps in the existing literature that your research can address.
• Feasibility: Assess the feasibility of your research topic in terms of available resources, time constraints, and access to data or participants. Ensure that your chosen topic is realistic and manageable within the scope of your research project.
• Originality: Aim for originality in your research topic by exploring new perspectives, innovative methodologies, or unexplored areas within your field. Avoid topics that have been extensively researched unless you can offer a unique angle or contribution.
• Scope: Define the scope of your research topic to ensure that it is neither too broad nor too narrow. A well-defined scope will help you focus your research efforts and produce more meaningful results within the constraints of your project.
• Research Question: Formulate a clear and concise research question that guides your investigation and provides a framework for your research methodology. Your research question should be specific, measurable, achievable, relevant, and time-bound (SMART).
• Methodological Approach: Consider the most appropriate research methodology or approach for investigating your research question. Depending on your topic, qualitative, quantitative, or mixed-methods approaches may be suitable. Choose a methodology that aligns with your research objectives and data collection needs.
• Ethical Considerations: Take into account ethical considerations relevant to your research topic, including informed consent, confidentiality, privacy, and potential risks to participants. Ensure that your research adheres to ethical guidelines and regulations set forth by your institution or professional associations.
• Practical Significance: Assess the practical significance of your research topic by considering its potential impact on theory, practice, policy, or society. Aim to produce research findings that have relevance and applicability beyond academic circles.
• Support and Guidance: Seek support and guidance from mentors, advisors, or peers when selecting your research topic. Consult with experts in your field for feedback, suggestions, and advice to help refine your topic and ensure its suitability for your research project.

Research methodology project topics are pivotal in academia, offering students avenues for exploration, learning, and contribution to knowledge. Through careful selection and consideration of factors such as relevance, feasibility, and originality, students can embark on research endeavors that not only deepen their understanding of their chosen field but also foster critical thinking and problem-solving skills. 

By addressing pressing issues, exploring new perspectives, and adhering to ethical guidelines, research projects become catalysts for intellectual growth and innovation. 

Ultimately, research methodology project topics serve as vehicles for academic inquiry, shaping the future of scholarship and empowering students to make meaningful contributions to their disciplines.

1. What are some tips for narrowing down my topic?

Aim for specificity and feasibility. Consider the scope of your project and the resources available to you. Choose a topic that is both manageable and significant within your discipline.

2. What are some common ethical considerations in research methodology projects?

Ethical considerations include obtaining informed consent from participants, ensuring participant confidentiality, and obtaining approval from institutional review boards (IRBs).

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A tutorial on methodological studies: the what, when, how and why

Lawrence mbuagbaw.

1 Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON Canada

2 Biostatistics Unit/FSORC, 50 Charlton Avenue East, St Joseph’s Healthcare—Hamilton, 3rd Floor Martha Wing, Room H321, Hamilton, Ontario L8N 4A6 Canada

3 Centre for the Development of Best Practices in Health, Yaoundé, Cameroon

Daeria O. Lawson

Livia puljak.

4 Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000 Zagreb, Croatia

David B. Allison

5 Department of Epidemiology and Biostatistics, School of Public Health – Bloomington, Indiana University, Bloomington, IN 47405 USA

Lehana Thabane

6 Departments of Paediatrics and Anaesthesia, McMaster University, Hamilton, ON Canada

7 Centre for Evaluation of Medicine, St. Joseph’s Healthcare-Hamilton, Hamilton, ON Canada

8 Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON Canada

Associated Data

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 – 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 – 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig.  1 .

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 – 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

• Comparing two groups
• Determining a proportion, mean or another quantifier
• Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

• Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.
• Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].
• Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]
• Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 – 67 ].
• Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].
• Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].
• Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].
• Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n  = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n  = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n  = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

• What is the aim?

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

• 2. What is the design?

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

• 3. What is the sampling strategy?

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n  = 103) [ 30 ].

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

• 4. What is the unit of analysis?

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig.  2 .

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Acknowledgements

Abbreviations.

Authors’ contributions

LM conceived the idea and drafted the outline and paper. DOL and LT commented on the idea and draft outline. LM, LP and DOL performed literature searches and data extraction. All authors (LM, DOL, LT, LP, DBA) reviewed several draft versions of the manuscript and approved the final manuscript.

This work did not receive any dedicated funding.

Availability of data and materials

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

DOL, DBA, LM, LP and LT are involved in the development of a reporting guideline for methodological studies.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Here's What You Need to Understand About Research Methodology

Table of Contents

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.  

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if  you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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• Published: 07 September 2020

A tutorial on methodological studies: the what, when, how and why

• Lawrence Mbuagbaw   ORCID: orcid.org/0000-0001-5855-5461 1 , 2 , 3 ,
• Daeria O. Lawson 1 ,
• Livia Puljak 4 ,
• David B. Allison 5 &
• Lehana Thabane 1 , 2 , 6 , 7 , 8  

BMC Medical Research Methodology volume  20 , Article number:  226 ( 2020 ) Cite this article

42k Accesses

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Metrics details

Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

Peer Review reports

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 , 2 , 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 , 7 , 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig.  1 .

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 , 13 , 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

Comparing two groups

Determining a proportion, mean or another quantifier

Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.

Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].

Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]

Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 , 66 , 67 ].

Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].

Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].

Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].

Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n  = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n  = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n  = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

What is the aim?

Methodological studies that investigate bias

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies that investigate quality (or completeness) of reporting

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Methodological studies that investigate the consistency of reporting

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

Methodological studies that investigate factors associated with reporting

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies that investigate methods

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Methodological studies that summarize other methodological studies

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Methodological studies that investigate nomenclature and terminology

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

Other types of methodological studies

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

What is the design?

Methodological studies that are descriptive

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Methodological studies that are analytical

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

What is the sampling strategy?

Methodological studies that include the target population

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n  = 103) [ 30 ].

Methodological studies that include a sample of the target population

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

What is the unit of analysis?

Methodological studies with a research report as the unit of analysis

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Methodological studies with a design, analysis or reporting item as the unit of analysis

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig.  2 .

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Availability of data and materials

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Abbreviations

Consolidated Standards of Reporting Trials

Evidence, Participants, Intervention, Comparison, Outcome, Timeframe

Grading of Recommendations, Assessment, Development and Evaluations

Participants, Intervention, Comparison, Outcome, Timeframe

Preferred Reporting Items of Systematic reviews and Meta-Analyses

Studies Within a Review

Studies Within a Trial

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Lawrence Mbuagbaw, Daeria O. Lawson & Lehana Thabane

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Lawrence Mbuagbaw

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Livia Puljak

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LM conceived the idea and drafted the outline and paper. DOL and LT commented on the idea and draft outline. LM, LP and DOL performed literature searches and data extraction. All authors (LM, DOL, LT, LP, DBA) reviewed several draft versions of the manuscript and approved the final manuscript.

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Mbuagbaw, L., Lawson, D.O., Puljak, L. et al. A tutorial on methodological studies: the what, when, how and why. BMC Med Res Methodol 20 , 226 (2020). https://doi.org/10.1186/s12874-020-01107-7

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How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job. 

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

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Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon. 

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

Very useful and informative especially for beginners

Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.

I find the article very informative especially for my decitation it has been helpful and an eye opener.

Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I struggle so much with designs of the research for sure!

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

Thank you so much this site is such a life saver. How I wish 1-1 coaching is available in our country but sadly it’s not.

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What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

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Guide for Thesis Research

• Introduction to the Thesis Process
• Project Planning
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Basics of Methodology

Research is a process of inquiry that is carried out in a pondered, organized, and strategic manner. In order to obtain high quality results, it is important to understand methodology.

Research methodology refers to how your project will be designed, what you will observe or measure, and how you will collect and analyze data. The methods you choose must be appropriate for your field and for the specific research questions you are setting out to answer.

A strong understanding of methodology will help you:

• apply appropriate research techniques
• design effective data collection instruments
• analyze and interpret your data
• develop well-founded conclusions

Below, you will find resources that mostly cover general aspects of research methodology. In the left column, you will find resources that specifically cover qualitative, quantitative, and mixed methods research.

General Works on Methodology

Qualitative Research

Quantitative Research

Mixed Methods Research

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Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
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The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE:   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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Choosing the Right Research Methodology: A Guide for Researchers

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Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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113 Great Research Paper Topics

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

Are you also learning about dynamic equilibrium in your science class? We break this sometimes tricky concept down so it's easy to understand in our complete guide to dynamic equilibrium .

Thinking about becoming a nurse practitioner? Nurse practitioners have one of the fastest growing careers in the country, and we have all the information you need to know about what to expect from nurse practitioner school .

Want to know the fastest and easiest ways to convert between Fahrenheit and Celsius? We've got you covered! Check out our guide to the best ways to convert Celsius to Fahrenheit (or vice versa).

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.  

With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.  

• Generate an outline: Input some details about your research to instantly generate an outline for your methods section 
• Develop the section: Use the outline and suggested sentence templates to expand your ideas and develop the first draft.  
• P araph ras e and trim : Get clear, concise academic text with paraphrasing that conveys your work effectively and word reduction to fix redundancies. 
• Choose the right words: Enhance text by choosing contextual synonyms based on how the words have been used in previously published work.  
• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

You can repeat this process to develop each section of your research manuscript, including the title, abstract and keywords. Ready to write your research papers faster, better, and without the stress? Sign up for Paperpal and start writing today!

Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Home » 500+ Qualitative Research Titles and Topics

500+ Qualitative Research Titles and Topics

Table of Contents

Qualitative research is a methodological approach that involves gathering and analyzing non-numerical data to understand and interpret social phenomena. Unlike quantitative research , which emphasizes the collection of numerical data through surveys and experiments, qualitative research is concerned with exploring the subjective experiences, perspectives, and meanings of individuals and groups. As such, qualitative research topics can be diverse and encompass a wide range of social issues and phenomena. From exploring the impact of culture on identity formation to examining the experiences of marginalized communities, qualitative research offers a rich and nuanced perspective on complex social issues. In this post, we will explore some of the most compelling qualitative research topics and provide some tips on how to conduct effective qualitative research.

Qualitative Research Titles

Qualitative research titles often reflect the study’s focus on understanding the depth and complexity of human behavior, experiences, or social phenomena. Here are some examples across various fields:

• “Understanding the Impact of Project-Based Learning on Student Engagement in High School Classrooms: A Qualitative Study”
• “Navigating the Transition: Experiences of International Students in American Universities”
• “The Role of Parental Involvement in Early Childhood Education: Perspectives from Teachers and Parents”
• “Exploring the Effects of Teacher Feedback on Student Motivation and Self-Efficacy in Middle Schools”
• “Digital Literacy in the Classroom: Teacher Strategies for Integrating Technology in Elementary Education”
• “Culturally Responsive Teaching Practices: A Case Study in Diverse Urban Schools”
• “The Influence of Extracurricular Activities on Academic Achievement: Student Perspectives”
• “Barriers to Implementing Inclusive Education in Public Schools: A Qualitative Inquiry”
• “Teacher Professional Development and Its Impact on Classroom Practice: A Qualitative Exploration”
• “Student-Centered Learning Environments: A Qualitative Study of Classroom Dynamics and Outcomes”
• “The Experience of First-Year Teachers: Challenges, Support Systems, and Professional Growth”
• “Exploring the Role of School Leadership in Fostering a Positive School Culture”
• “Peer Relationships and Learning Outcomes in Cooperative Learning Settings: A Qualitative Analysis”
• “The Impact of Social Media on Student Learning and Engagement: Teacher and Student Perspectives”
• “Understanding Special Education Needs: Parent and Teacher Perceptions of Support Services in Schools

Health Science

• “Living with Chronic Pain: Patient Narratives and Coping Strategies in Managing Daily Life”
• “Healthcare Professionals’ Perspectives on the Challenges of Rural Healthcare Delivery”
• “Exploring the Mental Health Impacts of COVID-19 on Frontline Healthcare Workers: A Qualitative Study”
• “Patient and Family Experiences of Palliative Care: Understanding Needs and Preferences”
• “The Role of Community Health Workers in Improving Access to Maternal Healthcare in Rural Areas”
• “Barriers to Mental Health Services Among Ethnic Minorities: A Qualitative Exploration”
• “Understanding Patient Satisfaction in Telemedicine Services: A Qualitative Study of User Experiences”
• “The Impact of Cultural Competence Training on Healthcare Provider-Patient Communication”
• “Navigating the Transition to Adult Healthcare Services: Experiences of Adolescents with Chronic Conditions”
• “Exploring the Use of Alternative Medicine Among Patients with Chronic Diseases: A Qualitative Inquiry”
• “The Role of Social Support in the Rehabilitation Process of Stroke Survivors”
• “Healthcare Decision-Making Among Elderly Patients: A Qualitative Study of Preferences and Influences”
• “Nurse Perceptions of Patient Safety Culture in Hospital Settings: A Qualitative Analysis”
• “Experiences of Women with Postpartum Depression: Barriers to Seeking Help”
• “The Impact of Nutrition Education on Eating Behaviors Among College Students: A Qualitative Approach”
• “Understanding Resilience in Survivors of Childhood Trauma: A Narrative Inquiry”
• “The Role of Mindfulness in Managing Work-Related Stress Among Corporate Employees: A Qualitative Study”
• “Coping Mechanisms Among Parents of Children with Autism Spectrum Disorder”
• “Exploring the Psychological Impact of Social Isolation in the Elderly: A Phenomenological Study”
• “Identity Formation in Adolescence: The Influence of Social Media and Peer Groups”
• “The Experience of Forgiveness in Interpersonal Relationships: A Qualitative Exploration”
• “Perceptions of Happiness and Well-Being Among University Students: A Cultural Perspective”
• “The Impact of Art Therapy on Anxiety and Depression in Adult Cancer Patients”
• “Narratives of Recovery: A Qualitative Study on the Journey Through Addiction Rehabilitation”
• “Exploring the Psychological Effects of Long-Term Unemployment: A Grounded Theory Approach”
• “Attachment Styles and Their Influence on Adult Romantic Relationships: A Qualitative Analysis”
• “The Role of Personal Values in Career Decision-Making Among Young Adults”
• “Understanding the Stigma of Mental Illness in Rural Communities: A Qualitative Inquiry”
• “Exploring the Use of Digital Mental Health Interventions Among Adolescents: A Qualitative Study”
• “The Psychological Impact of Climate Change on Young Adults: An Exploration of Anxiety and Action”
• “Navigating Identity: The Role of Social Media in Shaping Youth Culture and Self-Perception”
• “Community Resilience in the Face of Urban Gentrification: A Case Study of Neighborhood Change”
• “The Dynamics of Intergenerational Relationships in Immigrant Families: A Qualitative Analysis”
• “Social Capital and Economic Mobility in Low-Income Neighborhoods: An Ethnographic Approach”
• “Gender Roles and Career Aspirations Among Young Adults in Conservative Societies”
• “The Stigma of Mental Health in the Workplace: Employee Narratives and Organizational Culture”
• “Exploring the Intersection of Race, Class, and Education in Urban School Systems”
• “The Impact of Digital Divide on Access to Healthcare Information in Rural Communities”
• “Social Movements and Political Engagement Among Millennials: A Qualitative Study”
• “Cultural Adaptation and Identity Among Second-Generation Immigrants: A Phenomenological Inquiry”
• “The Role of Religious Institutions in Providing Community Support and Social Services”
• “Negotiating Public Space: Experiences of LGBTQ+ Individuals in Urban Environments”
• “The Sociology of Food: Exploring Eating Habits and Food Practices Across Cultures”
• “Work-Life Balance Challenges Among Dual-Career Couples: A Qualitative Exploration”
• “The Influence of Peer Networks on Substance Use Among Adolescents: A Community Study”

Business and Management

• “Navigating Organizational Change: Employee Perceptions and Adaptation Strategies in Mergers and Acquisitions”
• “Corporate Social Responsibility: Consumer Perceptions and Brand Loyalty in the Retail Sector”
• “Leadership Styles and Organizational Culture: A Comparative Study of Tech Startups”
• “Workplace Diversity and Inclusion: Best Practices and Challenges in Multinational Corporations”
• “Consumer Trust in E-commerce: A Qualitative Study of Online Shopping Behaviors”
• “The Gig Economy and Worker Satisfaction: Exploring the Experiences of Freelance Professionals”
• “Entrepreneurial Resilience: Success Stories and Lessons Learned from Failed Startups”
• “Employee Engagement and Productivity in Remote Work Settings: A Post-Pandemic Analysis”
• “Brand Storytelling: How Narrative Strategies Influence Consumer Engagement”
• “Sustainable Business Practices: Stakeholder Perspectives in the Fashion Industry”
• “Cross-Cultural Communication Challenges in Global Teams: Strategies for Effective Collaboration”
• “Innovative Workspaces: The Impact of Office Design on Creativity and Collaboration”
• “Consumer Perceptions of Artificial Intelligence in Customer Service: A Qualitative Exploration”
• “The Role of Mentoring in Career Development: Insights from Women in Leadership Positions”
• “Agile Management Practices: Adoption and Impact in Traditional Industries”

Environmental Studies

• “Community-Based Conservation Efforts in Tropical Rainforests: A Qualitative Study of Local Perspectives and Practices”
• “Urban Sustainability Initiatives: Exploring Resident Participation and Impact in Green City Projects”
• “Perceptions of Climate Change Among Indigenous Populations: Insights from Traditional Ecological Knowledge”
• “Environmental Justice and Industrial Pollution: A Case Study of Community Advocacy and Response”
• “The Role of Eco-Tourism in Promoting Conservation Awareness: Perspectives from Tour Operators and Visitors”
• “Sustainable Agriculture Practices Among Smallholder Farmers: Challenges and Opportunities”
• “Youth Engagement in Climate Action Movements: Motivations, Perceptions, and Outcomes”
• “Corporate Environmental Responsibility: A Qualitative Analysis of Stakeholder Expectations and Company Practices”
• “The Impact of Plastic Pollution on Marine Ecosystems: Community Awareness and Behavioral Change”
• “Renewable Energy Adoption in Rural Communities: Barriers, Facilitators, and Social Implications”
• “Water Scarcity and Community Adaptation Strategies in Arid Regions: A Grounded Theory Approach”
• “Urban Green Spaces: Public Perceptions and Use Patterns in Megacities”
• “Environmental Education in Schools: Teachers’ Perspectives on Integrating Sustainability into Curricula”
• “The Influence of Environmental Activism on Policy Change: Case Studies of Grassroots Campaigns”
• “Cultural Practices and Natural Resource Management: A Qualitative Study of Indigenous Stewardship Models”

Anthropology

• “Kinship and Social Organization in Matrilineal Societies: An Ethnographic Study”
• “Rituals and Beliefs Surrounding Death and Mourning in Diverse Cultures: A Comparative Analysis”
• “The Impact of Globalization on Indigenous Languages and Cultural Identity”
• “Food Sovereignty and Traditional Agricultural Practices Among Indigenous Communities”
• “Navigating Modernity: The Integration of Traditional Healing Practices in Contemporary Healthcare Systems”
• “Gender Roles and Equality in Hunter-Gatherer Societies: An Anthropological Perspective”
• “Sacred Spaces and Religious Practices: An Ethnographic Study of Pilgrimage Sites”
• “Youth Subcultures and Resistance: An Exploration of Identity and Expression in Urban Environments”
• “Cultural Constructions of Disability and Inclusion: A Cross-Cultural Analysis”
• “Interethnic Marriages and Cultural Syncretism: Case Studies from Multicultural Societies”
• “The Role of Folklore and Storytelling in Preserving Cultural Heritage”
• “Economic Anthropology of Gift-Giving and Reciprocity in Tribal Communities”
• “Digital Anthropology: The Role of Social Media in Shaping Political Movements”
• “Migration and Diaspora: Maintaining Cultural Identity in Transnational Communities”
• “Cultural Adaptations to Climate Change Among Coastal Fishing Communities”

Communication Studies

• “The Dynamics of Family Communication in the Digital Age: A Qualitative Inquiry”
• “Narratives of Identity and Belonging in Diaspora Communities Through Social Media”
• “Organizational Communication and Employee Engagement: A Case Study in the Non-Profit Sector”
• “Cultural Influences on Communication Styles in Multinational Teams: An Ethnographic Approach”
• “Media Representation of Women in Politics: A Content Analysis and Audience Perception Study”
• “The Role of Communication in Building Sustainable Community Development Projects”
• “Interpersonal Communication in Online Dating: Strategies, Challenges, and Outcomes”
• “Public Health Messaging During Pandemics: A Qualitative Study of Community Responses”
• “The Impact of Mobile Technology on Parent-Child Communication in the Digital Era”
• “Crisis Communication Strategies in the Hospitality Industry: A Case Study of Reputation Management”
• “Narrative Analysis of Personal Stories Shared on Mental Health Blogs”
• “The Influence of Podcasts on Political Engagement Among Young Adults”
• “Visual Communication and Brand Identity: A Qualitative Study of Consumer Interpretations”
• “Communication Barriers in Cross-Cultural Healthcare Settings: Patient and Provider Perspectives”
• “The Role of Internal Communication in Managing Organizational Change: Employee Experiences”

Information Technology

• “User Experience Design in Augmented Reality Applications: A Qualitative Study of Best Practices”
• “The Human Factor in Cybersecurity: Understanding Employee Behaviors and Attitudes Towards Phishing”
• “Adoption of Cloud Computing in Small and Medium Enterprises: Challenges and Success Factors”
• “Blockchain Technology in Supply Chain Management: A Qualitative Exploration of Potential Impacts”
• “The Role of Artificial Intelligence in Personalizing User Experiences on E-commerce Platforms”
• “Digital Transformation in Traditional Industries: A Case Study of Technology Adoption Challenges”
• “Ethical Considerations in the Development of Smart Home Technologies: A Stakeholder Analysis”
• “The Impact of Social Media Algorithms on News Consumption and Public Opinion”
• “Collaborative Software Development: Practices and Challenges in Open Source Projects”
• “Understanding the Digital Divide: Access to Information Technology in Rural Communities”
• “Data Privacy Concerns and User Trust in Internet of Things (IoT) Devices”
• “The Effectiveness of Gamification in Educational Software: A Qualitative Study of Engagement and Motivation”
• “Virtual Teams and Remote Work: Communication Strategies and Tools for Effectiveness”
• “User-Centered Design in Mobile Health Applications: Evaluating Usability and Accessibility”
• “The Influence of Technology on Work-Life Balance: Perspectives from IT Professionals”

Tourism and Hospitality

• “Exploring the Authenticity of Cultural Heritage Tourism in Indigenous Communities”
• “Sustainable Tourism Practices: Perceptions and Implementations in Small Island Destinations”
• “The Impact of Social Media Influencers on Destination Choice Among Millennials”
• “Gastronomy Tourism: Exploring the Culinary Experiences of International Visitors in Rural Regions”
• “Eco-Tourism and Conservation: Stakeholder Perspectives on Balancing Tourism and Environmental Protection”
• “The Role of Hospitality in Enhancing the Cultural Exchange Experience of Exchange Students”
• “Dark Tourism: Visitor Motivations and Experiences at Historical Conflict Sites”
• “Customer Satisfaction in Luxury Hotels: A Qualitative Study of Service Excellence and Personalization”
• “Adventure Tourism: Understanding the Risk Perception and Safety Measures Among Thrill-Seekers”
• “The Influence of Local Communities on Tourist Experiences in Ecotourism Sites”
• “Event Tourism: Economic Impacts and Community Perspectives on Large-Scale Music Festivals”
• “Heritage Tourism and Identity: Exploring the Connections Between Historic Sites and National Identity”
• “Tourist Perceptions of Sustainable Accommodation Practices: A Study of Green Hotels”
• “The Role of Language in Shaping the Tourist Experience in Multilingual Destinations”
• “Health and Wellness Tourism: Motivations and Experiences of Visitors to Spa and Retreat Centers”

Qualitative Research Topics

Qualitative Research Topics are as follows:

• Understanding the lived experiences of first-generation college students
• Exploring the impact of social media on self-esteem among adolescents
• Investigating the effects of mindfulness meditation on stress reduction
• Analyzing the perceptions of employees regarding organizational culture
• Examining the impact of parental involvement on academic achievement of elementary school students
• Investigating the role of music therapy in managing symptoms of depression
• Understanding the experience of women in male-dominated industries
• Exploring the factors that contribute to successful leadership in non-profit organizations
• Analyzing the effects of peer pressure on substance abuse among adolescents
• Investigating the experiences of individuals with disabilities in the workplace
• Understanding the factors that contribute to burnout among healthcare professionals
• Examining the impact of social support on mental health outcomes
• Analyzing the perceptions of parents regarding sex education in schools
• Investigating the experiences of immigrant families in the education system
• Understanding the impact of trauma on mental health outcomes
• Exploring the effectiveness of animal-assisted therapy for individuals with anxiety
• Analyzing the factors that contribute to successful intergenerational relationships
• Investigating the experiences of LGBTQ+ individuals in the workplace
• Understanding the impact of online gaming on social skills development among adolescents
• Examining the perceptions of teachers regarding technology integration in the classroom
• Analyzing the experiences of women in leadership positions
• Investigating the factors that contribute to successful marriage and long-term relationships
• Understanding the impact of social media on political participation
• Exploring the experiences of individuals with mental health disorders in the criminal justice system
• Analyzing the factors that contribute to successful community-based programs for youth development
• Investigating the experiences of veterans in accessing mental health services
• Understanding the impact of the COVID-19 pandemic on mental health outcomes
• Examining the perceptions of parents regarding childhood obesity prevention
• Analyzing the factors that contribute to successful multicultural education programs
• Investigating the experiences of individuals with chronic illnesses in the workplace
• Understanding the impact of poverty on academic achievement
• Exploring the experiences of individuals with autism spectrum disorder in the workplace
• Analyzing the factors that contribute to successful employee retention strategies
• Investigating the experiences of caregivers of individuals with Alzheimer’s disease
• Understanding the impact of parent-child communication on adolescent sexual behavior
• Examining the perceptions of college students regarding mental health services on campus
• Analyzing the factors that contribute to successful team building in the workplace
• Investigating the experiences of individuals with eating disorders in treatment programs
• Understanding the impact of mentorship on career success
• Exploring the experiences of individuals with physical disabilities in the workplace
• Analyzing the factors that contribute to successful community-based programs for mental health
• Investigating the experiences of individuals with substance use disorders in treatment programs
• Understanding the impact of social media on romantic relationships
• Examining the perceptions of parents regarding child discipline strategies
• Analyzing the factors that contribute to successful cross-cultural communication in the workplace
• Investigating the experiences of individuals with anxiety disorders in treatment programs
• Understanding the impact of cultural differences on healthcare delivery
• Exploring the experiences of individuals with hearing loss in the workplace
• Analyzing the factors that contribute to successful parent-teacher communication
• Investigating the experiences of individuals with depression in treatment programs
• Understanding the impact of childhood trauma on adult mental health outcomes
• Examining the perceptions of college students regarding alcohol and drug use on campus
• Analyzing the factors that contribute to successful mentor-mentee relationships
• Investigating the experiences of individuals with intellectual disabilities in the workplace
• Understanding the impact of work-family balance on employee satisfaction and well-being
• Exploring the experiences of individuals with autism spectrum disorder in vocational rehabilitation programs
• Analyzing the factors that contribute to successful project management in the construction industry
• Investigating the experiences of individuals with substance use disorders in peer support groups
• Understanding the impact of mindfulness meditation on stress reduction and mental health
• Examining the perceptions of parents regarding childhood nutrition
• Analyzing the factors that contribute to successful environmental sustainability initiatives in organizations
• Investigating the experiences of individuals with bipolar disorder in treatment programs
• Understanding the impact of job stress on employee burnout and turnover
• Exploring the experiences of individuals with physical disabilities in recreational activities
• Analyzing the factors that contribute to successful strategic planning in nonprofit organizations
• Investigating the experiences of individuals with hoarding disorder in treatment programs
• Understanding the impact of culture on leadership styles and effectiveness
• Examining the perceptions of college students regarding sexual health education on campus
• Analyzing the factors that contribute to successful supply chain management in the retail industry
• Investigating the experiences of individuals with personality disorders in treatment programs
• Understanding the impact of multiculturalism on group dynamics in the workplace
• Exploring the experiences of individuals with chronic pain in mindfulness-based pain management programs
• Analyzing the factors that contribute to successful employee engagement strategies in organizations
• Investigating the experiences of individuals with internet addiction disorder in treatment programs
• Understanding the impact of social comparison on body dissatisfaction and self-esteem
• Examining the perceptions of parents regarding childhood sleep habits
• Analyzing the factors that contribute to successful diversity and inclusion initiatives in organizations
• Investigating the experiences of individuals with schizophrenia in treatment programs
• Understanding the impact of job crafting on employee motivation and job satisfaction
• Exploring the experiences of individuals with vision impairments in navigating public spaces
• Analyzing the factors that contribute to successful customer relationship management strategies in the service industry
• Investigating the experiences of individuals with dissociative amnesia in treatment programs
• Understanding the impact of cultural intelligence on intercultural communication and collaboration
• Examining the perceptions of college students regarding campus diversity and inclusion efforts
• Analyzing the factors that contribute to successful supply chain sustainability initiatives in organizations
• Investigating the experiences of individuals with obsessive-compulsive disorder in treatment programs
• Understanding the impact of transformational leadership on organizational performance and employee well-being
• Exploring the experiences of individuals with mobility impairments in public transportation
• Analyzing the factors that contribute to successful talent management strategies in organizations
• Investigating the experiences of individuals with substance use disorders in harm reduction programs
• Understanding the impact of gratitude practices on well-being and resilience
• Examining the perceptions of parents regarding childhood mental health and well-being
• Analyzing the factors that contribute to successful corporate social responsibility initiatives in organizations
• Investigating the experiences of individuals with borderline personality disorder in treatment programs
• Understanding the impact of emotional labor on job stress and burnout
• Exploring the experiences of individuals with hearing impairments in healthcare settings
• Analyzing the factors that contribute to successful customer experience strategies in the hospitality industry
• Investigating the experiences of individuals with gender dysphoria in gender-affirming healthcare
• Understanding the impact of cultural differences on cross-cultural negotiation in the global marketplace
• Examining the perceptions of college students regarding academic stress and mental health
• Analyzing the factors that contribute to successful supply chain agility in organizations
• Understanding the impact of music therapy on mental health and well-being
• Exploring the experiences of individuals with dyslexia in educational settings
• Analyzing the factors that contribute to successful leadership in nonprofit organizations
• Investigating the experiences of individuals with chronic illnesses in online support groups
• Understanding the impact of exercise on mental health and well-being
• Examining the perceptions of parents regarding childhood screen time
• Analyzing the factors that contribute to successful change management strategies in organizations
• Understanding the impact of cultural differences on international business negotiations
• Exploring the experiences of individuals with hearing impairments in the workplace
• Analyzing the factors that contribute to successful team building in corporate settings
• Understanding the impact of technology on communication in romantic relationships
• Analyzing the factors that contribute to successful community engagement strategies for local governments
• Investigating the experiences of individuals with attention deficit hyperactivity disorder (ADHD) in treatment programs
• Understanding the impact of financial stress on mental health and well-being
• Analyzing the factors that contribute to successful mentorship programs in organizations
• Investigating the experiences of individuals with gambling addictions in treatment programs
• Understanding the impact of social media on body image and self-esteem
• Examining the perceptions of parents regarding childhood education
• Analyzing the factors that contribute to successful virtual team management strategies
• Investigating the experiences of individuals with dissociative identity disorder in treatment programs
• Understanding the impact of cultural differences on cross-cultural communication in healthcare settings
• Exploring the experiences of individuals with chronic pain in cognitive-behavioral therapy programs
• Analyzing the factors that contribute to successful community-building strategies in urban neighborhoods
• Investigating the experiences of individuals with alcohol use disorders in treatment programs
• Understanding the impact of personality traits on romantic relationships
• Examining the perceptions of college students regarding mental health stigma on campus
• Analyzing the factors that contribute to successful fundraising strategies for political campaigns
• Investigating the experiences of individuals with traumatic brain injuries in rehabilitation programs
• Understanding the impact of social support on mental health and well-being among the elderly
• Exploring the experiences of individuals with chronic illnesses in medical treatment decision-making processes
• Analyzing the factors that contribute to successful innovation strategies in organizations
• Investigating the experiences of individuals with dissociative disorders in treatment programs
• Understanding the impact of cultural differences on cross-cultural communication in education settings
• Examining the perceptions of parents regarding childhood physical activity
• Analyzing the factors that contribute to successful conflict resolution in family relationships
• Investigating the experiences of individuals with opioid use disorders in treatment programs
• Understanding the impact of emotional intelligence on leadership effectiveness
• Exploring the experiences of individuals with learning disabilities in the workplace
• Analyzing the factors that contribute to successful change management in educational institutions
• Investigating the experiences of individuals with eating disorders in recovery support groups
• Understanding the impact of self-compassion on mental health and well-being
• Examining the perceptions of college students regarding campus safety and security measures
• Analyzing the factors that contribute to successful marketing strategies for nonprofit organizations
• Investigating the experiences of individuals with postpartum depression in treatment programs
• Understanding the impact of ageism in the workplace
• Exploring the experiences of individuals with dyslexia in the education system
• Investigating the experiences of individuals with anxiety disorders in cognitive-behavioral therapy programs
• Understanding the impact of socioeconomic status on access to healthcare
• Examining the perceptions of parents regarding childhood screen time usage
• Analyzing the factors that contribute to successful supply chain management strategies
• Understanding the impact of parenting styles on child development
• Exploring the experiences of individuals with addiction in harm reduction programs
• Analyzing the factors that contribute to successful crisis management strategies in organizations
• Investigating the experiences of individuals with trauma in trauma-focused therapy programs
• Examining the perceptions of healthcare providers regarding patient-centered care
• Analyzing the factors that contribute to successful product development strategies
• Investigating the experiences of individuals with autism spectrum disorder in employment programs
• Understanding the impact of cultural competence on healthcare outcomes
• Exploring the experiences of individuals with chronic illnesses in healthcare navigation
• Analyzing the factors that contribute to successful community engagement strategies for non-profit organizations
• Investigating the experiences of individuals with physical disabilities in the workplace
• Understanding the impact of childhood trauma on adult mental health
• Analyzing the factors that contribute to successful supply chain sustainability strategies
• Investigating the experiences of individuals with personality disorders in dialectical behavior therapy programs
• Understanding the impact of gender identity on mental health treatment seeking behaviors
• Exploring the experiences of individuals with schizophrenia in community-based treatment programs
• Analyzing the factors that contribute to successful project team management strategies
• Investigating the experiences of individuals with obsessive-compulsive disorder in exposure and response prevention therapy programs
• Understanding the impact of cultural competence on academic achievement and success
• Examining the perceptions of college students regarding academic integrity
• Analyzing the factors that contribute to successful social media marketing strategies
• Investigating the experiences of individuals with bipolar disorder in community-based treatment programs
• Understanding the impact of mindfulness on academic achievement and success
• Exploring the experiences of individuals with substance use disorders in medication-assisted treatment programs
• Investigating the experiences of individuals with anxiety disorders in exposure therapy programs
• Understanding the impact of healthcare disparities on health outcomes
• Analyzing the factors that contribute to successful supply chain optimization strategies
• Investigating the experiences of individuals with borderline personality disorder in schema therapy programs
• Understanding the impact of culture on perceptions of mental health stigma
• Exploring the experiences of individuals with trauma in art therapy programs
• Analyzing the factors that contribute to successful digital marketing strategies
• Investigating the experiences of individuals with eating disorders in online support groups
• Understanding the impact of workplace bullying on job satisfaction and performance
• Examining the perceptions of college students regarding mental health resources on campus
• Analyzing the factors that contribute to successful supply chain risk management strategies
• Investigating the experiences of individuals with chronic pain in mindfulness-based pain management programs
• Understanding the impact of cognitive-behavioral therapy on social anxiety disorder
• Understanding the impact of COVID-19 on mental health and well-being
• Exploring the experiences of individuals with eating disorders in treatment programs
• Analyzing the factors that contribute to successful leadership in business organizations
• Investigating the experiences of individuals with chronic pain in cognitive-behavioral therapy programs
• Understanding the impact of cultural differences on intercultural communication
• Examining the perceptions of teachers regarding inclusive education for students with disabilities
• Investigating the experiences of individuals with depression in therapy programs
• Understanding the impact of workplace culture on employee retention and turnover
• Exploring the experiences of individuals with traumatic brain injuries in rehabilitation programs
• Analyzing the factors that contribute to successful crisis communication strategies in organizations
• Investigating the experiences of individuals with anxiety disorders in mindfulness-based interventions
• Investigating the experiences of individuals with chronic illnesses in healthcare settings
• Understanding the impact of technology on work-life balance
• Exploring the experiences of individuals with learning disabilities in academic settings
• Analyzing the factors that contribute to successful entrepreneurship in small businesses
• Understanding the impact of gender identity on mental health and well-being
• Examining the perceptions of individuals with disabilities regarding accessibility in public spaces
• Understanding the impact of religion on coping strategies for stress and anxiety
• Exploring the experiences of individuals with chronic illnesses in complementary and alternative medicine treatments
• Analyzing the factors that contribute to successful customer retention strategies in business organizations
• Investigating the experiences of individuals with postpartum depression in therapy programs
• Understanding the impact of ageism on older adults in healthcare settings
• Examining the perceptions of students regarding online learning during the COVID-19 pandemic
• Analyzing the factors that contribute to successful team building in virtual work environments
• Investigating the experiences of individuals with gambling disorders in treatment programs
• Exploring the experiences of individuals with chronic illnesses in peer support groups
• Analyzing the factors that contribute to successful social media marketing strategies for businesses
• Investigating the experiences of individuals with ADHD in treatment programs
• Understanding the impact of sleep on cognitive and emotional functioning
• Examining the perceptions of individuals with chronic illnesses regarding healthcare access and affordability
• Investigating the experiences of individuals with borderline personality disorder in dialectical behavior therapy programs
• Understanding the impact of social support on caregiver well-being
• Exploring the experiences of individuals with chronic illnesses in disability activism
• Analyzing the factors that contribute to successful cultural competency training programs in healthcare settings
• Understanding the impact of personality disorders on interpersonal relationships
• Examining the perceptions of healthcare providers regarding the use of telehealth services
• Investigating the experiences of individuals with dissociative disorders in therapy programs
• Understanding the impact of gender bias in hiring practices
• Exploring the experiences of individuals with visual impairments in the workplace
• Analyzing the factors that contribute to successful diversity and inclusion programs in the workplace
• Understanding the impact of online dating on romantic relationships
• Examining the perceptions of parents regarding childhood vaccination
• Analyzing the factors that contribute to successful communication in healthcare settings
• Understanding the impact of cultural stereotypes on academic achievement
• Exploring the experiences of individuals with substance use disorders in sober living programs
• Analyzing the factors that contribute to successful classroom management strategies
• Understanding the impact of social support on addiction recovery
• Examining the perceptions of college students regarding mental health stigma
• Analyzing the factors that contribute to successful conflict resolution in the workplace
• Understanding the impact of race and ethnicity on healthcare access and outcomes
• Exploring the experiences of individuals with post-traumatic stress disorder in treatment programs
• Analyzing the factors that contribute to successful project management strategies
• Understanding the impact of teacher-student relationships on academic achievement
• Analyzing the factors that contribute to successful customer service strategies
• Investigating the experiences of individuals with social anxiety disorder in treatment programs
• Understanding the impact of workplace stress on job satisfaction and performance
• Exploring the experiences of individuals with disabilities in sports and recreation
• Analyzing the factors that contribute to successful marketing strategies for small businesses
• Investigating the experiences of individuals with phobias in treatment programs
• Understanding the impact of culture on attitudes towards mental health and illness
• Examining the perceptions of college students regarding sexual assault prevention
• Analyzing the factors that contribute to successful time management strategies
• Investigating the experiences of individuals with addiction in recovery support groups
• Understanding the impact of mindfulness on emotional regulation and well-being
• Exploring the experiences of individuals with chronic pain in treatment programs
• Analyzing the factors that contribute to successful conflict resolution in romantic relationships
• Investigating the experiences of individuals with autism spectrum disorder in social skills training programs
• Understanding the impact of parent-child communication on adolescent substance use
• Examining the perceptions of parents regarding childhood mental health services
• Analyzing the factors that contribute to successful fundraising strategies for non-profit organizations
• Investigating the experiences of individuals with chronic illnesses in support groups
• Understanding the impact of personality traits on career success and satisfaction
• Exploring the experiences of individuals with disabilities in accessing public transportation
• Analyzing the factors that contribute to successful team building in sports teams
• Investigating the experiences of individuals with chronic pain in alternative medicine treatments
• Understanding the impact of stigma on mental health treatment seeking behaviors
• Examining the perceptions of college students regarding diversity and inclusion on campus.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Knowledge Base
• Starting the research process

A Beginner's Guide to Starting the Research Process

When you have to write a thesis or dissertation , it can be hard to know where to begin, but there are some clear steps you can follow.

The research process often begins with a very broad idea for a topic you’d like to know more about. You do some preliminary research to identify a  problem . After refining your research questions , you can lay out the foundations of your research design , leading to a proposal that outlines your ideas and plans.

This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project.

Table of contents

Step 1: choose your topic, step 2: identify a problem, step 3: formulate research questions, step 4: create a research design, step 5: write a research proposal, other interesting articles.

First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you’re interested in—maybe you already have specific research interests based on classes you’ve taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose .

Even if you already have a good sense of your topic, you’ll need to read widely to build background knowledge and begin narrowing down your ideas. Conduct an initial literature review to begin gathering relevant sources. As you read, take notes and try to identify problems, questions, debates, contradictions and gaps. Your aim is to narrow down from a broad area of interest to a specific niche.

Make sure to consider the practicalities: the requirements of your programme, the amount of time you have to complete the research, and how difficult it will be to access sources and data on the topic. Before moving onto the next stage, it’s a good idea to discuss the topic with your thesis supervisor.

>>Read more about narrowing down a research topic

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So you’ve settled on a topic and found a niche—but what exactly will your research investigate, and why does it matter? To give your project focus and purpose, you have to define a research problem .

The problem might be a practical issue—for example, a process or practice that isn’t working well, an area of concern in an organization’s performance, or a difficulty faced by a specific group of people in society.

Alternatively, you might choose to investigate a theoretical problem—for example, an underexplored phenomenon or relationship, a contradiction between different models or theories, or an unresolved debate among scholars.

To put the problem in context and set your objectives, you can write a problem statement . This describes who the problem affects, why research is needed, and how your research project will contribute to solving it.

>>Read more about defining a research problem

Next, based on the problem statement, you need to write one or more research questions . These target exactly what you want to find out. They might focus on describing, comparing, evaluating, or explaining the research problem.

A strong research question should be specific enough that you can answer it thoroughly using appropriate qualitative or quantitative research methods. It should also be complex enough to require in-depth investigation, analysis, and argument. Questions that can be answered with “yes/no” or with easily available facts are not complex enough for a thesis or dissertation.

In some types of research, at this stage you might also have to develop a conceptual framework and testable hypotheses .

>>See research question examples

The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you’ll use to collect and analyze it, and the location and timescale of your research.

There are often many possible paths you can take to answering your questions. The decisions you make will partly be based on your priorities. For example, do you want to determine causes and effects, draw generalizable conclusions, or understand the details of a specific context?

You need to decide whether you will use primary or secondary data and qualitative or quantitative methods . You also need to determine the specific tools, procedures, and materials you’ll use to collect and analyze your data, as well as your criteria for selecting participants or sources.

>>Read more about creating a research design

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Finally, after completing these steps, you are ready to complete a research proposal . The proposal outlines the context, relevance, purpose, and plan of your research.

As well as outlining the background, problem statement, and research questions, the proposal should also include a literature review that shows how your project will fit into existing work on the topic. The research design section describes your approach and explains exactly what you will do.

You might have to get the proposal approved by your supervisor before you get started, and it will guide the process of writing your thesis or dissertation.

>>Read more about writing a research proposal

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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• How to Choose a Dissertation Topic | 8 Steps to Follow
• How to Define a Research Problem | Ideas & Examples
• How to Write a Problem Statement | Guide & Examples
• Relevance of Your Dissertation Topic | Criteria & Tips
• Research Objectives | Definition & Examples
• What Is a Fishbone Diagram? | Templates & Examples
• What Is Root Cause Analysis? | Definition & Examples

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How to Choose and Develop a Research Topic: Ideas and Examples

Discover strategies for choosing and developing a compelling research topic. Generate ideas, refine your topic, and conduct effective research.

Kate Windsor

Jun 26, 2024

Selecting the right research paper topic is a crucial step in the research process. A well-chosen topic can lay the foundation for a successful research project, while a poorly chosen one can lead to frustration and wasted effort. Choosing an interesting research topic can be challenging, especially for those new to the research field. 

This article aims to provide guidance and inspiration for researchers seeking to choose and develop a compelling research topic and/or topics to write. 

Understanding the Characteristics of a Good Research Topic

A good research topic should possess several key characteristics:

• Originality and novelty: The topic should contribute new knowledge or insights to the field, rather than simply rehashing existing research.
• **Feasibility and relevance: **The topic should be feasible to research within the given timeframe and resources, and relevant to the researcher's field of study.
• **Significance and impact: **The topic should have the potential to make a significant impact on the field and contribute to the advancement of knowledge.

Strategies for Generating Research Topic Ideas

Generating research topic ideas or thinking of topics to write a research on can be a daunting task, but there are several strategies that can help:

Brainstorming Techniques

• Mind mapping: Create a visual representation of your ideas and how they connect to each other.
• Freewriting: Write down your thoughts and ideas without censoring yourself, and then review what you've written to identify potential topics.
• Questioning: Ask yourself questions about your field of study, such as "What are the current gaps in knowledge?" or "What are the most pressing issues facing the field?".

Exploring Personal Interests and Experiences

Your personal interests and experiences can be a rich source of inspiration for research topics. Consider what you are passionate about and how it intersects with your field of study for your research paper ideas. Choose a topic that interests you.

Keeping Up with Current Trends and Developments

• Reading academic journals and publications: Stay up-to-date with the latest research in your field by regularly reading academic journals and publications.
• Attending conferences and seminars: Attend conferences and seminars to learn about current trends and developments in your field, and to network with other researchers.

Seeking Inspiration from AI for Research

AI for research can be a valuable tool for generating research topic ideas. AI algorithms can analyze vast amounts of data and identify patterns and trends that may not be immediately apparent to human researchers.

Easily pronounces technical words in any field

Narrowing Down and Refining Your Research Topic

Once you have generated some potential research topics, the next step is to narrow down and refine your topic:

• Identifying a broad area of interest: Start by identifying a broad area of interest within your field of study.
• Conducting preliminary research: Conduct preliminary research to gain a better understanding of the existing research in your area of interest.
• Formulating a specific research question: Formulate a specific research question that addresses a gap in the existing research or explores a new angle on a familiar topic. This research question will serve as the basis for your thesis or thesis statement.
• Considering the scope and feasibility of the topic: Consider the scope and feasibility of your topic, taking into account the timeframe and resources available to you.
• Ensuring the topic aligns with the requirements of your research paper or scientific paper: Make sure your topic aligns with the requirements of your research paper or scientific paper, such as word count, formatting, and citation style.

Developing Your Research Topic

Once you have narrowed down and refined your research topic, the next step is to develop it further:

Conducting a Literature Review

• Identifying key sources and references: Identify the key sources and references in your field of study that are relevant to your research topic.
• Synthesizing and analyzing existing research: Synthesize and analyze the existing research to identify gaps in knowledge and potential areas for further exploration.

Formulating Hypotheses or Research Objectives

Formulate hypotheses or research objectives based on your analysis of the existing research and your own insights and observations.

Defining Key Concepts and Variables

Define the key concepts and variables that are central to your research topic, and operationalize them in a way that is measurable and testable.

Outlining the Research Methodology

Outline the research methodology you will use to investigate your research topic, including data collection methods, sampling strategies, and data analysis techniques.

Tips on How to Write Faster and Efficiently

Writing a research paper can be a time-consuming process, but there are several tips and strategies that can help you  write faster and more efficiently:

• Break your writing into manageable chunks and set achievable goals for each writing session.
• Use outlines and mind maps to organize your thoughts and ideas before you start writing.
• Minimize distractions by finding a quiet workspace and turning off notifications on your devices.
• Take regular breaks to recharge and avoid burnout.
• Utilize writing tools and software, such as Grammarly or Scrivener , to streamline your writing process and improve the quality of your work.

Research Topic Ideas and Examples

Here are some examples of research topics in various fields of study:

Social Sciences

• The impact of social media on interpersonal relationships
• The role of education in reducing income inequality

Natural Sciences

• Exploring the potential of renewable energy sources
• Investigating the effects of climate change on biodiversity
• Analyzing the influence of popular culture on literature
• Examining the evolution of language in the digital age

Business and Economics

• The impact of remote work on employee productivity and job satisfaction
• Investigating the role of corporate social responsibility in consumer decision-making

Common Mistakes to Avoid When Choosing a Research Topic

When choosing a research topic, there are several common mistakes to avoid:

• Choosing a topic that is too broad or too narrow : A topic that is too broad may lack focus and depth, while a topic that is too narrow may limit the potential impact and significance of the research.
• Failing to consider the relevance and significance of the topic: A topic that is not relevant or significant to the field may not be worth researching, even if it is personally interesting to the researcher.
• Neglecting to conduct sufficient preliminary research : Failing to conduct sufficient preliminary research can lead to a lack of understanding of the existing research in the field, and may result in a topic that has already been thoroughly explored by other researchers. This can lead to wasted time and effort, as well as a lack of originality in the research.
• Ignoring the importance of a well-crafted research paper title : A well-crafted  research paper title can help to attract readers and convey the significance of the research. Ignoring the importance of the title can lead to a lack of engagement with the research.  A strong title should be concise, informative, and engaging, accurately reflecting the content and purpose of the research.

Choosing and developing a research topic is a crucial step in the research process, and one that requires careful consideration and planning. By understanding the characteristics of a good research topic, employing strategies for generating ideas, narrowing down and refining your topic, and developing it further through a literature review and research methodology, you can set yourself up for success in your research endeavors.

While the process of topic selection can be challenging, it is also an opportunity to explore your passions and interests, and to contribute new knowledge and insights to your field of study. By investing time and effort in selecting a compelling and feasible research topic, you can lay the foundation for a good research paper and a successful and impactful research project. 

Thesis Development

Literature Review

Academic Writing

Research Methodology

Research Idea Generation

Research Topic Selection

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Best Education Research Topics | Inspiration & Ideas

Introduction

What is education research, how do you choose a research topic in education, research topics for education research.

Education research plays a vital role in shaping the future of teaching and learning by exploring new methods, policies, and practices that can improve educational outcomes. Whether you are an educator, a student, or a researcher, selecting the right research topic in education is crucial for contributing meaningful insights to the field. This article provides inspiration and ideas for choosing compelling education research topics, covering a range of areas such as early childhood education, educational leadership, academic performance, and more. By exploring various educational research topics, you can address current challenges in education and help shape the policies and practices that impact learners at all levels.

Education research is the systematic study of teaching and learning processes, educational policies, and the factors that influence educational outcomes. It encompasses a wide range of topics, from the effectiveness of different teaching methods to the impact of social, economic, and cultural factors on student achievement. The goal of education research is to generate evidence-based insights that can inform educational practice, guide policy decisions, and ultimately improve the quality of education for all learners.

Researchers in the field of education use various methodologies to explore their topics, including qualitative methods like interviews and case studies , and quantitative methods such as surveys and experiments. These methods allow researchers to collect and analyze data that can provide a deeper understanding of how education systems work and how they can be improved. For example, a study might examine the impact of early childhood education on long-term academic success, or investigate the effectiveness of professional development programs for teachers.

Education research is critical not only for advancing theoretical knowledge but also for addressing practical challenges in the classroom. By understanding what works, for whom, and under what conditions, educators and policymakers can make more informed decisions that benefit students. Furthermore, education research often highlights the disparities and inequities in educational opportunities and outcomes, prompting efforts to create more inclusive and equitable learning environments. Whether the focus is on curriculum development, teacher training, student assessment, or policy reform, education research provides the foundation for continuous improvement in education.

Choosing a research topic in education involves careful consideration of your interests, the relevance of the topic, and its feasibility. Here are three key factors to guide you in selecting an effective research topic in the field of education.

Identify your interests and passions

The first step in choosing a research topic is to reflect on your own interests and passions. What aspects of education do you find most compelling? Whether it's early childhood development, educational technology, or inclusive education, starting with a topic that genuinely interests you will help keep you motivated throughout the research process. Your personal experiences in the field—whether as a teacher, student, or parent—can also provide valuable insights and inspiration for your research. By focusing on a topic that resonates with you, you're more likely to engage deeply with the material, leading to more meaningful and insightful research.

Consider the relevance and impact of the topic

Once you've identified areas of interest, it's important to consider the relevance and potential impact of the topic. Ask yourself whether the topic addresses current challenges or gaps in the field of education. For instance, with the increasing integration of technology in classrooms, a research topic that examines the effects of digital tools on student learning could be highly relevant. Similarly, topics that explore issues like educational equity, teacher retention, or the effectiveness of remote learning have significant implications for policy and practice. Selecting a topic with clear relevance ensures that your research will contribute to ongoing discussions in the field and have a tangible impact on educational outcomes.

Assess feasibility and resources

Feasibility is another critical factor to consider when choosing a research topic. Before committing to a topic, evaluate the resources available to you, including access to data, research materials, and time. Consider whether the topic can be explored within the scope of your project, whether it's a dissertation, thesis, or a smaller research paper . For example, a topic that requires extensive fieldwork or access to specific populations might be challenging if you have limited time or resources. It's also important to think about the availability of literature and previous studies on the topic, as these will form the basis of your literature review and provide context for your research. Choosing a topic that is feasible ensures that you can conduct thorough and rigorous research without becoming overwhelmed by practical constraints.

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Education is a broad and multifaceted field that offers a wealth of research opportunities across various areas of study. This section provides an in-depth exploration of potential research topics in education within seven key areas: early childhood education, educational leadership, academic performance, college students, educational psychology, multicultural education, and student motivation. Each of these areas presents unique challenges and questions, making them rich grounds for research that can contribute to the improvement of educational practices and policies.

Early childhood education

Early childhood education is a critical phase in a child's development, setting the foundation for future learning and growth. Research in this area can address various aspects of early education, from curriculum design to the impact of early intervention programs.

One promising research topic in early childhood education is the role of play-based learning in cognitive and social development. Play in physical education and in casual classroom settings is often viewed as a natural and essential part of childhood, and many educators advocate for its inclusion in early education programs. However, there is ongoing debate about the most effective ways to integrate play with formal learning objectives. Research could explore how different types of play, such as free play, guided play, and structured play, influence children's cognitive abilities, social skills, and emotional well-being. Additionally, studies could examine the long-term benefits of play-based learning, comparing outcomes for children who participate in play-focused programs with those in more traditional, academically focused settings.

Another important area of research is the impact of early childhood education on later academic achievement. There is substantial evidence that high-quality early education programs can lead to better academic outcomes in later years, particularly for children from disadvantaged backgrounds. Researchers could investigate the specific elements of early childhood programs that contribute to these positive outcomes, such as teacher qualifications, class size, parental involvement, and the use of evidence-based curricula. This research could also examine how early education programs can be tailored to meet the needs of diverse populations, including children with disabilities and those from different cultural and linguistic backgrounds.

Finally, the transition from early childhood education to primary school is a critical period that can have lasting effects on a child's academic trajectory. Research could explore strategies for smoothing this transition, such as the alignment of curricula between preschool and primary school, the role of family engagement, and the effectiveness of transition programs designed to prepare children for the shift to more structured, formal education. Studies could also investigate the emotional and social challenges children face during this transition and how schools and families can support children through these changes.

Educational leadership

Educational leadership is a key factor in the success of schools and educational institutions. Effective leadership can inspire teachers, improve student outcomes, and drive innovation in education. Research in this area can explore various aspects of leadership, from the characteristics of successful leaders to the strategies they use to achieve their goals.

One important topic in educational leadership is the impact of leadership styles on school performance. Different leadership styles, such as transformational, transactional, and instructional leadership, have been shown to influence various aspects of school culture and effectiveness. Researchers could examine how these leadership styles affect teacher motivation, student achievement, and school climate. For example, a study could compare schools led by transformational leaders, who focus on inspiring and motivating staff, with those led by instructional leaders, who emphasize curriculum and teaching practices. This research could provide insights into which leadership approaches are most effective in different educational contexts.

Another critical area of research is the role of school principals in promoting equity and inclusion. Principals play a crucial role in shaping the culture of their schools and ensuring that all students, regardless of their background, have access to a high-quality education. Research could explore how principals can foster an inclusive school environment, support diverse learners, and address disparities in academic achievement. This could include studies on the strategies principals use to implement inclusive practices, the challenges they face in promoting equity, and the impact of their efforts on student outcomes.

Educational leadership also involves decision-making and the ability to manage change effectively. As schools face increasing pressure to adapt to new technologies, policies, and societal expectations, the ability of leaders to guide their institutions through these changes is more important than ever. Research could investigate how school leaders make decisions in complex, dynamic environments, and how they manage the process of change. This could include studies on the decision-making processes of successful leaders, the factors that influence their decisions, and the outcomes of their decisions for students, teachers, and the broader school community.

Academic performance

Student academic performance is a central concern in education research, as it is often used as a measure of both student success and the effectiveness of educational systems. Understanding the factors that influence academic performance can help educators develop strategies to support all students in reaching their full potential.

One key area of research is the impact of socio-economic status (SES) on academic performance. Numerous studies have shown that students from lower SES backgrounds tend to perform worse academically compared to their more affluent peers. Researchers could explore the specific mechanisms through which SES affects academic outcomes, such as access to resources, parental involvement, and exposure to stressors. Additionally, research could investigate interventions that aim to mitigate the effects of SES on academic performance, such as tutoring programs, after-school activities, and school-based support services.

Another important topic is the role of teacher quality in student achievement. Research has consistently shown that teachers are one of the most significant factors influencing student performance. Studies could examine what specific teacher characteristics, such as qualifications, experience, and instructional practices, have the greatest impact on student outcomes. Furthermore, researchers could investigate how professional development programs for teachers can enhance their effectiveness in the classroom, leading to better academic results for students.

The use of technology in education is another area that has significant implications for academic performance. With the increasing integration of digital tools and platforms into the classroom, research could explore how technology affects student learning. This could include studies on the effectiveness of online learning compared to traditional face-to-face instruction, the impact of educational apps and games on student engagement and achievement, and the challenges and opportunities of using technology to support diverse learners. Additionally, research could examine how teachers can effectively integrate technology into their teaching practices to enhance student learning.

College students

The college years are a critical period of personal and academic development, making them a rich area for education research. Research on college students can explore a wide range of topics, from factors that influence college choice to strategies for supporting student success and well-being.

One important research topic is the impact of financial aid on college access and retention. The rising cost of higher education has made financial aid an essential resource for many students. Researchers could investigate how different types of financial aid, such as grants, scholarships, and loans, affect students' decisions to enroll in and persist through college. This research could also examine the barriers that prevent students from accessing financial aid and how institutions can better support students in navigating the financial aid process.

Another key area of research is the factors that contribute to college student retention and success. While many students start college, not all complete their degrees. Research could explore the reasons why some students struggle to stay enrolled, such as academic challenges, student mental health issues, and financial pressures. Additionally, studies could investigate the effectiveness of programs and services designed to support student retention, such as academic advising, tutoring centers, and mental health resources. Understanding these factors can help colleges develop strategies to support students throughout their college journey.

The mental health of college students is another critical issue that has gained increasing attention in recent years. College students face a range of stressors, including academic pressures, social challenges, and the transition to independence. Research could explore the prevalence of mental health issues among college students, the factors that contribute to these issues, and the effectiveness of campus mental health services. Additionally, studies could examine how colleges can create supportive environments that promote student well-being and reduce the stigma associated with seeking help for mental health concerns.

Educational psychology

Educational psychology is the study of how people learn and develop in educational settings. This field of research can provide valuable insights into the cognitive, emotional, and social processes that underlie learning, as well as the factors that influence educational outcomes.

One important area of research in educational psychology is the role of motivation in learning. Motivation is a key factor that drives student engagement and academic achievement. Researchers could explore the different types of motivation, such as intrinsic and extrinsic motivation, and how they impact learning outcomes. For example, studies could examine how intrinsic motivation, or the desire to learn for its own sake, affects students' persistence and performance in challenging subjects. Additionally, research could investigate how teachers can foster motivation in the classroom, such as through the use of praise, rewards, and goal-setting strategies.

Another critical topic in educational psychology is the impact of cognitive development on learning. Cognitive development refers to the changes in thinking, reasoning, and problem-solving abilities that occur as children grow. Research could explore how different stages of cognitive development affect students' ability to learn and process information. For example, studies could examine how younger students' limited working memory capacity impacts their ability to solve complex math problems, or how older students' advanced reasoning skills allow them to engage in abstract thinking. Understanding these developmental differences can help educators design instruction that is appropriate for students' cognitive abilities.

The role of social and emotional learning (SEL) in education is another important area of research in educational psychology. SEL refers to the process through which students develop the skills to manage their emotions, build healthy relationships, and make responsible decisions. Research could explore how SEL programs impact students' academic performance, behavior, and overall well-being. Additionally, studies could investigate the best practices for implementing SEL in schools, such as integrating SEL into the curriculum, providing professional development for teachers, and creating a supportive school climate that promotes social and emotional growth.

Multicultural education

Multicultural education is an approach to teaching and learning that seeks to promote equity, respect for diversity, and inclusion in the classroom. Research in this area can explore how educators can create learning environments that reflect and honor the diverse cultural backgrounds of their students.

One important research topic in multicultural education is the development and implementation of culturally responsive teaching practices. Culturally responsive teaching involves recognizing and valuing students' cultural identities and incorporating their cultural experiences into the curriculum and instructional practices. Researchers could explore how teachers can develop culturally responsive teaching practices and the impact of these practices on student engagement and achievement. For example, studies could examine how incorporating students' cultural traditions, languages, and perspectives into the classroom can enhance their sense of belonging and motivation to learn.

Another key area of research is the role of multicultural education in reducing achievement gaps. Achievement gaps between students of different racial, ethnic, and socioeconomic backgrounds are a persistent issue in education. Research could explore how multicultural education can address these gaps by promoting equity and inclusion in the classroom. For example, studies could examine how culturally responsive teaching practices can help close achievement gaps by providing all students with access to high-quality, culturally relevant instruction. Additionally, research could investigate the impact of multicultural education programs on students' attitudes toward diversity and their ability to interact effectively with people from different cultural backgrounds.

The integration of multicultural education into teacher preparation programs is another important research topic. Preparing teachers to work in diverse classrooms is essential for promoting equity and inclusion in education. Research could explore how teacher preparation programs can equip future educators with the knowledge, skills, and attitudes needed to implement multicultural education in their classrooms. For example, studies could examine the effectiveness of coursework, field experiences, and professional development opportunities that focus on multicultural education. Additionally, research could investigate how teacher preparation programs can address the biases and stereotypes that educators may bring to the classroom and how they can foster a commitment to social justice and equity in education.

Student motivation

Student motivation is a critical factor in academic success and is influenced by a range of internal and external factors. Understanding what drives students to engage in learning can help educators design more effective instructional strategies and support student achievement.

One important research topic in student motivation is the impact of goal setting on academic performance. Goal setting is a powerful motivational tool that can help students focus their efforts and persist in the face of challenges. Research could explore how different types of goals, such as short-term versus long-term goals or mastery-oriented versus performance-oriented goals, affect students' motivation and academic outcomes. For example, studies could examine how setting specific, challenging, and achievable goals can enhance students' motivation to succeed in difficult subjects. Additionally, research could investigate teachers' roles in preparing students in setting and achieving their goals, such as through the use of goal-setting frameworks, feedback, and reflection activities.

Another key area of research is the role of self-efficacy in student motivation. Self-efficacy refers to a student's belief in their ability to succeed in specific tasks or situations. Research has shown that students with high self-efficacy are more likely to take on challenging tasks, persist in the face of difficulties, and achieve higher academic outcomes. Researchers could explore how self-efficacy develops and how it can be enhanced through instructional practices, such as providing opportunities for success, offering constructive feedback, and modeling effective problem-solving strategies. Additionally, studies could examine how self-efficacy interacts with other motivational factors, such as interest, effort, and resilience, to influence student performance.

The impact of classroom environment on student motivation is another important research topic. The classroom environment, including the physical space, social dynamics, and instructional practices, plays a significant role in shaping students' motivation to learn. Research could explore how different aspects of the classroom environment, such as the presence of supportive relationships, the availability of resources, and the use of engaging instructional strategies, influence students' motivation and engagement. For example, studies could examine how a positive classroom climate, characterized by mutual respect, collaboration, and high expectations, fosters students' motivation to participate and succeed in learning activities. Additionally, research could investigate how teachers can create a motivating classroom environment by using strategies such as differentiation, student-centered learning, and the incorporation of students' interests and preferences into the curriculum.

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Research Methodologies

• What are research designs?

What are research methodologies?

Quantitative research methodologies, qualitative research methodologies, mixed method methodologies, selecting a methodology.

• What are research methods?
• Additional Sources

According to Dawson (2019),a research methodology is the primary principle that will guide your research.  It becomes the general approach in conducting research on your topic and determines what research method you will use. A research methodology is different from a research method because research methods are the tools you use to gather your data (Dawson, 2019).  You must consider several issues when it comes to selecting the most appropriate methodology for your topic.  Issues might include research limitations and ethical dilemmas that might impact the quality of your research.  Descriptions of each type of methodology are included below.

Quantitative research methodologies are meant to create numeric statistics by using survey research to gather data (Dawson, 2019).  This approach tends to reach a larger amount of people in a shorter amount of time.  According to Labaree (2020), there are three parts that make up a quantitative research methodology:

• Sample population
• How you will collect your data (this is the research method)
• How you will analyze your data

Once you decide on a methodology, you can consider the method to which you will apply your methodology.

Qualitative research methodologies examine the behaviors, opinions, and experiences of individuals through methods of examination (Dawson, 2019).  This type of approach typically requires less participants, but more time with each participant.  It gives research subjects the opportunity to provide their own opinion on a certain topic.

Examples of Qualitative Research Methodologies

• Action research:  This is when the researcher works with a group of people to improve something in a certain environment.  It is a common approach for research in organizational management, community development, education, and agriculture (Dawson, 2019).
• Ethnography:  The process of organizing and describing cultural behaviors (Dawson, 2019).  Researchers may immerse themselves into another culture to receive in "inside look" into the group they are studying.  It is often a time consuming process because the researcher will do this for a long period of time.  This can also be called "participant observation" (Dawson, 2019).
• Feminist research:  The goal of this methodology is to study topics that have been dominated by male test subjects.  It aims to study females and compare the results to previous studies that used male participants (Dawson, 2019).
• Grounded theory:  The process of developing a theory to describe a phenomenon strictly through the data results collected in a study.  It is different from other research methodologies where the researcher attempts to prove a hypothesis that they create before collecting data.  Popular research methods for this approach include focus groups and interviews (Dawson, 2019).

A mixed methodology allows you to implement the strengths of both qualitative and quantitative research methods.  In some cases, you may find that your research project would benefit from this.  This approach is beneficial because it allows each methodology to counteract the weaknesses of the other (Dawson, 2019).  You should consider this option carefully, as it can make your research complicated if not planned correctly.

What should you do to decide on a research methodology?  The most logical way to determine your methodology is to decide whether you plan on conducting qualitative or qualitative research.  You also have the option to implement a mixed methods approach.  Looking back on Dawson's (2019) five "W's" on the previous page , may help you with this process.  You should also look for key words that indicate a specific type of research methodology in your hypothesis or proposal.  Some words may lean more towards one methodology over another.

Quantitative Research Key Words

• How satisfied

Qualitative Research Key Words

• Experiences
• Thoughts/Think
• Relationship
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• Next: What are research methods? >>
• Last Updated: Aug 2, 2022 2:36 PM
• URL: https://library.tiffin.edu/researchmethodologies

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Why Many Parents and Teens Think It’s Harder Being a Teen Today

Is it harder these days to be a teen? Or do today’s teenagers have it easier than those of past generations? We asked the following question of 1,453 U.S. parents and teens: Compared with 20 years ago, do you think being a teenager today is harder, easier or about the same?

Parents and teens most often say it’s harder to be a teen today. Though parents are far more likely to say this.

Far fewer say it’s easier now …

… or that it’s about the same.

Teens, though, are more likely than parents to say they are unsure.

But why? We asked those who say teen life has gotten harder or easier to explain in their own words why they think so.

Why parents say it’s harder being a teen today

There are big debates about how teenagers are faring these days. And technology’s impact is often at the center of these conversations.

Prominent figures, including the U.S. Surgeon General, have been vocal about the harmful effects technology may be having on young people.

These concerns ring true for the parents in our survey. A majority blame technology – and especially social media – for making teen life more difficult.

Among parents who say it’s harder being a teen today, about two-thirds cite technology in some way. This includes 41% who specifically name social media.

While some mention social media in broad terms, others bring up specific experiences that teens may have on these platforms, such as feeling pressure to act or look a certain way or having negative interactions there. Parents also call out the downsides of being constantly connected through social media.

Pew Research Center has a long history of studying the attitudes and experiences of U.S. teens and parents, especially when it comes to their relationships with technology.

For this analysis, the Center conducted an online survey of 1,453 U.S. teens and parents from Sept. 26 to Oct. 23, 2023, through Ipsos. Ipsos invited one parent from each of a representative set of households with parents of teens in the desired age range from its  KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. Parents were asked to think about one teen in their household. (If there were multiple teens ages 13 to 17 in the household, one was randomly chosen.) After completing their section, the parent was asked to have this chosen teen come to the computer and complete the survey in private.

The survey is weighted to be representative of two different populations: 1) parents with teens ages 13 to 17, and 2) teens ages 13 to 17 who live with parents. For each of these populations, they survey is weighted to be representative by age, gender, race and ethnicity, household income and other categories.

Parents and teens were first asked whether they think it is harder, easier, or about the same to be a teen now than it was 20 years ago. Those who answered that it was easier or harder were then asked an open-ended question to explain why they answered the way they did. Center researchers developed a coding scheme categorizing the written responses, coded all responses, then grouped them into the themes explored in this data essay. Quotations may have been lightly edited for grammar, spelling and clarity.

Here are the questions among parents and among teens used in this analysis, along with responses, and its methodology .

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

“Social media is a scourge for society, especially for teens. They can’t escape social pressures and are constantly bombarded by images and content that makes them feel insecure and less than perfect, which creates undue stress that they can’t escape.” FATHER, 40s

“Kids are being told what to think and how to feel based on social media.” MOTHER, 40s

Parents name other forms of technology, but at much lower rates. Roughly one-in-ten parents who think being a teen is harder today specifically say the internet (11%) or smartphones (7%) contribute to this.

“Teens are online and they are going to encounter everything offered – positive and negative. Unfortunately, the negative can do major damage, as in cyberbullying, for example.” MOTHER, 30s

Another 26% say technology in general or some other specific type of technology (e.g., video games or television) make teens’ lives harder today.

“Technology has changed the way people communicate. I can see how kids feel very isolated.” FATHER, 40s

Parents also raise a range of reasons that do not specifically reference technology, with two that stand out: more pressures placed on teens and the country or world being worse off than in the past. Among parents who think it’s harder to be a teen today, 16% say it’s because of the pressures and expectations young people face. These include teens feeling like they have to look or act a certain way or perform at a certain level.

“The competition is more fierce in sports and academics and the bar seems to be higher. Everything is more over-the-top for social activities too. It’s not simple as it was.” MOTHER, 50s

A similar share (15%) says teen life is harder because the country or world has changed in a bad way, whether due to political issues or to shifts in morals and values.

“Now it is more difficult to instill values, principles, good customs and good behavior, since many bad vices are seen in some schools and public places.” MOTHER, 50s

Other reasons that do not mention technology are less common. For example, roughly one-in-ten of these parents or fewer mention violence and drugs, bullying, and exposure to bad influences.

Why parents say it’s easier being a teen today

Teens today have a seemingly endless choice of technologies at their disposal, whether it be smartphones , video games or generative AI . And while relatively few parents say teen’s lives are easier today, those who do largely point to technology.

Among parents who say it is easier being a teen today, roughly six-in-ten mention technology as a reason.

Some reference a specific type of technology, like the internet (14%). Another 8% cite smartphones, and 3% cite social media.

“Although the internet can be toxic, it also opens up so many avenues for connection, learning and engagement.” MOTHER, 50s

“We didn’t have smartphones when I was a teenager. Nowadays, teenagers have all the answers in the palm of their hand.” FATHER, 40s

A fair portion (47%) mention technology broadly or name another specific type of technology.

“Technology has improved exponentially, giving access to the whole world at your fingertips.” FATHER, 30s

Some other reasons that emerge do not mention technology specifically. For instance, 18% of parents who say it’s easier being a teen today think this is because there are fewer pressures and expectations on teenagers than in the past.

“Teens today have been shown more leniency; they barely hold themselves responsible.” MOTHER, 40s

And one-in-ten say it’s easier because teens have access to more resources and information.

 “When I was a teen, I had to carry so many books and binders everywhere while my daughter can just have her school laptop. She can complete research easily with internet access on her school device.” MOTHER, 30s

Why teens say it’s harder being a teen today

Most teens use social media , and some do so almost constantly. But they also see these sites as a reason teens’ lives are harder today than 20 years ago.

In addition, teens point to the pressures and expectations that are placed on them.

Among teens who say it’s harder to be a teenager today than in the past, roughly four-in-ten mention technology as a reason. This includes a quarter who specifically name social media. Some mention these sites broadly; others link them to harmful experiences like increased pressures to look a certain way or negative interactions with others.

“Social media tells kids what to do and say. And if you aren’t up on it, you look like the fool and become like an outcast from lots of people.” TEEN GIRL

“Social media was not a part of my parents’ teenage lives and I feel that they did not have to ‘curate’ themselves and be a certain way in order to fit [in] as it is today.” TEEN GIRL

Few specifically mention the internet (6%) or smartphones (3%) as reasons. About one-in-ten (11%) cite technology broadly or another type of technology.

“For one thing, my phone is a huge distraction. It takes up so much of my time just looking at stuff that doesn’t even mean anything to me.” TEEN GIRL

Teens name several reasons that do not specifically mention technology – most prominently, the increased pressures and expectations placed on them. Roughly three-in-ten of those who say teen life is harder today (31%) say it’s because of these pressures and expectations.  

“We have so much more homework and pressure from other kids. We are always being looked at by everyone. We can’t escape.” TEEN GIRL

“Adults expect too much from us. We need to get good grades, do extracurricular activities, have a social life, and work part time – all at the same time.” TEEN BOY

Another 15% say it’s harder because the world is worse off today, due to such things as political issues, values being different or the country having declined in some way.

“Teenagers are less able to afford vehicles, rent, etc. and basic living necessities, and are therefore not able to move out for years after they graduate high school and even college.” TEEN BOY

Other reasons that don’t mention technology – including violence and drugs, bullying, and mental health problems – are named by 8% of these teens or fewer.

Why teens say it’s easier being a teen today

Teens also see ways that technology makes life better, whether that’s helping them pursue hobbies , express their creativity or build skills . Overall, few think teens’ lives are easier today than 20 years ago, but those who do largely say technology is a reason. 

Six-in-ten teens who say teen life is easier today reference technology in some way. This includes 14% who mention the internet and 12% who mention phones. Just 3% name social media.

“[Teens 20 years ago] didn’t have internet available anywhere and they also didn’t have smartphones to be able to use whenever needed.” TEEN BOY

This also includes 46% who reference technology in general or some other specific type of technology.

“Tech has made it easier to connect with friends.” TEEN BOY

These teens also name reasons that don’t specifically mention technology, including 14% who say life is easier because there are fewer pressures and expectations for people their age.

“Twenty years ago there was probably more pressure to become an adult sooner and get things like a job, a learner’s permit, etc.” TEEN GIRL

And a same share says having more resources available to them has made life easier.

“Nowadays, we have help to deal with your physical and mental well-being, and we have specialists/therapists that we can talk to about our feelings and emotions.” TEEN GIRL

Smaller shares say it’s due to the country and world being better off today (4%) or people being nicer to each other (3%).

How parents and teens compare

Parents and teens are mostly in agreement on what makes growing up today harder than in the past.

But the rate at which they cite certain factors like social media or facing pressures differ.

Among those who say being a teen today is harder , 65% of parents believe it’s because of technology in some way. This drops to 39% among teens.

This divide also stands out when it comes to social media specifically (41% vs. 25%).

Teens, on the other hand, are more likely than parents to describe issues related to overachieving or having to look a certain way. Among those who say teen life is harder today, 31% of teens cite pressures and expectations as a reason, compared with 16% of parents.

Still, there are areas in which parents and teens are in sync. For example, similar shares cite the country or world being worse today (15% each) and violence and drugs (8% each) as reasons life today for teens is harder.

And among those who say being a teen today is easier , roughly six-in-ten parents (59%) and teens (60%) mention technology in some way.

Why parents and teens think it’s harder or easier to be a teen today than 20 years ago

Read the quotes below showing how parents and teens think teenagers’ experiences today differ from before.

Find out more

This project benefited greatly from the contributions of Director of Internet and Technology Research Monica Anderson , Research Assistants Eugenie Park and Olivia Sidoti . This project also benefited from Communications Manager Haley Nolan, Editorial Assistant Anna Jackson and Copy Editor Rebecca Leppert .

Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder.

Follow these links for more of our work on teens and technology:

• Teens, social media and technology
• Screen time among teens and parents
• Views of social media policies for minors
• Teens’ use of ChatGPT for schoolwork
• Teens and video games
• Cellphone distraction in the classroom
• Parents’ worries about explicit content, time-wasting on social media

Find more reports and blog posts related to  internet and technology on our topic page.

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Day Two: Placebo Workshop: Translational Research Domains and Key Questions

July 11, 2024

July 12, 2024

Day 1 Recap and Day 2 Overview

ERIN KING: All right. It is 12:01 so we'll go ahead and get started. And so on behalf of the Co-Chairs and the NIMH Planning Committee, I'd like to welcome you back to day two of the NIMH Placebo Workshop, Translational Research Domains and Key Questions. Before we begin, I will just go over our housekeeping items again. So attendees have been entered into the workshop in listen-only mode with cameras disabled. You can submit your questions via the Q&A box at any time during the presentation. And be sure to address your question to the speaker that you would like to respond.

For more information on today's speakers, their biographies can be found on the event registration website. If you have technical difficulties hearing or viewing the workshop, please note these in the Q&A box and our technicians will work to fix the problem. And you can also send an e-mail to [email protected]. And we'll put that e-mail address in the chat box for you. This workshop will be recorded and posted to the NIMH event web page for later viewing.

Now I would like to turn it over to our workshop Co-Chair, Dr. Cristina Cusin, for today's introduction.

CRISTINA CUSIN: Thank you so much, Erin. Welcome, everybody. It's very exciting to be here for this event.

My job is to provide you a brief recap of day one and to introduce you to the speakers of day two. Let me share my slides.

Again, thank you to the amazing Planning Committee. Thanks to their effort, we think this is going to be a success. I learned a lot of new information and a lot of ideas for research proposals and research projects from day one. Very briefly, please go and watch the videos. They are going to be uploaded in a couple of weeks if you missed them.

But we had an introduction from Tor, my Co-Chair. We had an historic perspective on clinical trials from the industry regulatory perspective. We had the current state from the FDA on placebo.

We had an overview of how hard it is to sham, to provide the right sham for device-based trials, and the challenges for TMS. We have seen some new data on the current state of placebo in psychosocial trials and what is the equivalent of a placebo pill for psychosocial trials. And some social neuroscience approach to placebo analgesia. We have come a long way from snake oil and we are trying to figure out what is placebo.

Tor, my Co-Chair, presented some data on the neurocircuitry underlying placebo effect and the questions that how placebo is a mixture of different elements including regression to the mean, sampling bias, selective attrition for human studies, the natural history of illness, the placebo effect per se that can be related to expectations, context, learning, interpretation.

We have seen a little bit of how is the impact on clinical trial design and how do we know that something, it really works. Or whatever this "it" is. And why do even placebo effect exists? It's fascinating idea that placebo exists as a predictive control to anticipate threats and the opportunity to respond in advance and to provide causal inference, a construct perception to infer the underlying state of body and of world.

We have seen historical perspective. And Ni Aye Khin and Mike Detke provided some overview of 25 years of randomized control trials from the data mining in major depressive disorders, schizophrenia trials and the lessons we have learned.

We have seen some strategies, both historical strategies and novel strategies to decrease placebo response in clinical trials and the results. Start from trial design, SPCD, lead-in, placebo phase and flexible dosing. Use of different scales. The use of statistical approaches like last observation carried forward or MMRM. Centralized ratings, self-rating, computer rating for different assessments. And more issues in clinical trials related to patient selection and professional patients.

Last, but not least, the dream of finding biomarkers for psychiatric conditions and tying response, clinical response to biomarkers. And we have seen how difficult it is to compare more recent studies with studies that were started in the '90s.

We have the FDA perspective with Tiffany Farchione in this placebo being a huge issue from the FDA. Especially the discussion towards the end of the day was on how to blind psychedelics.

We have seen an increasing placebo response rate in randomized controlled trials, also in adolescents, that is. And the considerations from the FDA of novel design models in collaboration with industry. We had examples of drugs approved for other disorders, not psychiatric condition, and realized -- made me realize how little we know about the true pathophysiology of psychiatric disorders, likely also heterogeneous conditions.

It made me very jealous of other fields because they have objective measures. They have biology, they have histology, they have imaging, they have lab values. While we are -- we are far behind, and we are not really able to explain to our patients why our mitigations are supposed to work or how they really work.

We heard from Holly Lisanby and Zhi-De Deng. The sham, the difficulty in producing the right sham for each type of device because most of them have auxiliary effects that are separate from the clinical effect like the noise or the scalp stimulation for TMS.

And it's critical to obtain a true blinding and separating sham from verum. We have seen how in clinical trials for devices expectancy from the patient, high tech environment and prolonged contact with clinicians and staff may play a role. And we have seen how difficult it is to develop the best possible sham for TMS studies in tDCS. It's really complicated and it's so difficult also to compare published studies in meta-analysis because they've used very different type of sham. Not all sham are created equal. And some of them could have been biologically active, so therefore invalidating the result or making the study uninformative.

Then we moved on to another fascinating topic with Dr. Rief and Dr. Atlas. What is the impact of psychological factors when you're studying a psychological intervention. Expectations, specific or nonspecific factors in clinical trials and what is interaction between those factors.

More, we learned about the potential nocebo effect of standard medical care or being on a wait list versus being in the active arm of a psychotherapy trial. And the fact that we are not accurately measuring the side effect of psychotherapy trial itself. And we heard more a fascinating talk about the neurocircuitry mediating placebo effect -- salience, affective value, cognitive control. And how perception of provider, perception of his or her warmth and competence and other social factors can affect response and placebo response, induce bias in evaluation of acute pain of others. Another very interesting field of study.

From a clinician perspective, this is -- and from someone who conduct clinical trials, all this was extremely informative because in many case in our patient situation no matter how good the treatment is, they have severe psychosocial stressors. They have difficulties to accessing food, to access treatment, transportation, or they live in an extremely stressful environment. So to disentangle other psychosocial factors from the treatment, from the biology is going to be critical to figure out how to treat best our patients.

And there is so much more work to do. Each of us approach the placebo topic for research from a different perspective. And like the blind man trying to understand what is an elephant, we have to endure it, we have to talk to each other, we have to collaborate and understand better the underlying biology, understand different aspect of the placebo phenomena.

And this lead us to the overview for day two. We are going to hear more about other topic that are so exciting. The placebo, the nocebo effect and other predictive factors in laboratory setting. We are going to hear about genetic of the placebo response to clinical trials. More physiological and psychological and neuromechanism for analgesia. And after a brief break around 1:30, we are going to hear about novel biological and behavioral approaches for the placebo effect.

We are going to hear about brain mapping. We are going to hear about other findings from imaging. And we're going to hear about preclinical modeling. There were some questions yesterday about animal models of placebo. And last, but not least, please stay around because in the panel discussion, we are going to tackle some of your questions. And we are going to have two wonderful moderators, Ted Kaptchuk and Matthew Rudorfer. So please stay with us and ask questions. We love to see more challenges for our speakers. And we're going to be all of the panelists from yesterday, from today are going to be present. Thank you so much.

Now we're going to move on to our first speaker of the day. If I am correct according to the last -- Luana.

Measuring & Mitigating the Placebo Effect

LUANA COLLOCA: Thank you very much, Cristina. First, I would love to thank the organizer. This is a very exciting opportunity to place our awareness about this important phenomenon for clinical trials and the clinical practice.

And today, I wish to give you a very brief overview of the psychoneurobiological mechanism of a placebo and nocebo, the description of some pharmacological studies, and a little bit of information on social learning. That is a topic that has been mentioned a little bit yesterday. And finally, the translational part. Can we translate what we learn from mechanistic approach to placebo and nocebo in terms of a disease and symptomatology and eventually predictors is the bigger question.

So we learned yesterday that placebo effects are generated by verbal suggestion, this medication has strong antidepressant effects. Therapeutic prior experience, merely taking a medication weeks, days before being substitute with a simulation of placebo sham treatment. Observation of a benefit in other people, contextual and treatment cue, and interpersonal interactions.

Especially in the fields of pain where we can simulate nociception, painful experience in laboratory setting, we learn a lot about the modulation related to placebo. In particular, expectation can provide a reaction and activation of parts of the brain like frontal area, nucleus accumbens, ventral striatum. And this kind of mechanism can generate a descending stimulation to make the painful nociceptive stimulus less intense.

The experience of analgesia at the level of a pain mechanism translate into a modulation reduction of a pain intensity. But most important, pain unpleasantness and the effective components of the pain. I will show today some information about the psychological factor, the demographic factor as well as genetic factors that can be predictive of placebo effects in the context of a pain.

On the other hand, a growing interest is related to nocebo effects, the negative counter sides of this phenomenon. When we talk about nocebo effects, we refer to increase in worsening of outcome in symptoms related to negative expectation, prior negative therapeutic experience, observing a negative outcome in others, or even mass psychogenic modeling such as some nocebo-related response during the pandemic. Treatment leaflets, the description of all side effects related to a medication. Patient-clinician communication. The informed consent where we list all of the side effects of a procedure or medication as well as contextual cues in clinical encounters.

And importantly, internal factor like emotion, mood, maladaptive cognitive appraisal, negative valence, personality traits, somatosensory features and omics can be predictive of negative worsening of symptom and outcome related to placebo and nocebo effects. In terms of a nocebo very briefly, there is a lot of attention again related to brain imaging with beautiful data show that the brainstem, the spinal cord, the hippocampus play a critical role during nocebo hyperalgesic effects.

And importantly, we learn that about placebo and nocebo through different approach including brain imaging, as we saw yesterday, but also pharmacological approach. We start from realizing that placebo effects are really neurobiological effects with the use of agonist or antagonist.

In other words, we can use a drug to mimic the action of that drug when we replace the drug with a saline solution, for example. In the cartoon here, you can see a brief pharmacological conditioning with apomorphine. Apomorphine is a dopamine agonist. And after three days of administration, apomorphine was replaced with saline solution in the intraoperative room to allow us to understand if we can mimic at the level of neuronal response the effects of apomorphine.

So in brief these are patients undergoing subthalamic EEG installation of deep brain stimulation. You can see here reaching the subthalamic nucleus. So after crossing the thalamus, the zona incerta, the STN, and the substantia nigra, the surgeon localized the area of stimulation. Because we have two subthalamic nuclei, we can use one as control and the other one as target to study in this case the effects of saline solution given after three days of apomorphine.

What we found was in those people who respond, there was consistency in reduction of clinical symptoms. As you can see here, the UPDRS, a common scale to measure rigidity in Parkinson, the frequency of a discharge at the level of neurons and the self-perception, patients with sentences like I feel like after Levodopa, I feel good. This feeling good translate in less rigidity, less tremor in the surgical room.

On the other hand, some participants didn't respond. Consistently we found no clinical improvement, no difference in preference over this drug at the level of a single unit and no set perception of a benefit. This kind of effects started to trigger the questions what is the reason why some people who responded to placebo and pharmacological conditioning and some other people don't. I will try to address this question in the second part of my talk.

On the other hand, we learn a lot about the endogenous modulation of pain and true placebo effects by using in this case an antagonist. The goal in this experiment was to create a painful sensation through a tourniquet. Week one with no treatment. Week two we pre-inject healthy participant with morphine. Week three the same morphine. And week four we replace morphine with placebo.

And you can see that a placebo increase the pain tolerance in terms of imminent. And this was not carryover effects. In fact, the control at week five showed no differences. Part of the participants were pre-injected with an antagonist Naloxone that when we use Naloxone at high dose, we can block the opioids delta and K receptors. You can see that by pre-injecting Naloxone there is a blockage of placebo analgesia, and I would say this morphine-like effects related to placebo given after morphine.

We start to then consider this phenomenon. Is this a way for tapering opioids. And we called this sort of drug-like effects as dose-extending placebo. The idea is that if we use a pharmacological treatment, morphine, apomorphine, as I showed to you, and then we replace the treatment with a placebo, we can create a pharmacological memory, and this can translate into a clinical benefit. Therefore, the dose-extending placebo can be used to extend the benefit of the drug, but also to reduce side effects related to the active drug.

In particular for placebo given after morphine, you can see on this graph, the effects is similarly strong if we do the repetition of a morphine one day apart or one week apart. Interestingly, this is the best model to be used in animal research.

Here at University of Maryland in collaboration with Todd Degotte, we create a model of anhedonia in mice and we condition animals with Ketamine. The goal was to replace Ketamine with a placebo. There are several control as you can see. But what is important for us, we condition animal with Ketamine week one, three and five. And then we substitute Ketamine with saline along with the CS. The condition of the stimulus was a light, a low light. And we want to compare this with an injection of Ketamine given at week seven.

So as you can see here, of course Ketamine was inducing a benefit as compared to saline and the Ketamine. But what is seen testing when we compare Ketamine week seven with saline replacing the Ketamine, we found no difference; suggesting that even in animals, in mice we were able to create drug-related effects. In this case, a Ketamine antidepressant-like placebo effects. These effects also add dimorphic effects in the sense that we observed this is in males but not in females.

So another approach to use agonist, like I mentioned for apomorphine in Parkinson patient, was to use vasopressin and oxytocin to increase placebo effects. In this case, we used verbal suggestion that in our experience especially with healthy participants tended to create very small sample size in terms of placebo analgesic effects. So we knew that from the literature that there is a dimorphic effects for this hormone. So we inject people with intranasal vasopressin, saline, oxytocin in low dose and no treatment. You can see there was a drug effects in women whereby vasopressin boost placebo analgesic effects, but not in men where yet we found many effects of manipulation but not drug effects.

Importantly, vasopressin affect dispositional anxiety as well as cortisol. And there is a negative correlation between anxiety and cortisol in relationship to vasopressin-induced placebo analgesia.

Another was, can we use medication to study placebo in laboratory setting or can we study placebo and nocebo without any medication? One example is to use a manipulation of the intensity of the painful stimulations. We use a thermal stimulation tailored at three different levels. 80 out of 100 with a visual analog scale, 50 or 20, as you can see from the thermometer.

We also combined the level of pain with a face. So first to emphasize there is three level of pain, participants will see an anticipatory cue just before the thermal stimulation. Ten seconds of the thermal stimulation to provide the experience of analgesia with the green and the hyperalgesia with the red as compared to the control, the yellow condition.

Therefore, the next day we move in the fMRI. And the goal was to try to understand to what extent expectation is relevant in placebo and nocebo effects. We mismatch what they anticipate, and they learn the day before. But also you can see we tailored the intensity at the same identical level. 50 for each participant.

We found that when expectation matched the level of the cues, anticipatory cue and face, we found a strong nocebo effects and placebo effects. You can see in red that despite the level of pain were identical, the perceived red-related stimuli as higher in terms of intensity, and the green related the stimuli as lower when compared to the control. By mismatching what they expect with what they saw, we blocked completely placebo effects and still nocebo persist.

So then I showed to you that we can use conditioning in animals and in humans to create placebo effects. But also by suggestion, the example of vasopressin. Another important model to study placebo effects in laboratory setting is social observation. We see something in other people, we are not told what we are seeing and we don't experience the thermal stimulation. That is the setting. A demonstrator receiving painful or no painful stimulation and someone observing this stimulation.

When we tested the observers, you can see the level of pain were tailored at the same identical intensity. And these were the effects. In 2009, when we first launched this line of research, this was quite surprising. We didn't anticipate that merely observing someone else could boost the expectations and probably creating this long-lasting analgesic effect. This drove our attention to the brain mechanism of what is so important during this transfer of placebo analgesia.

So we scanned participants when they were observing a video this time. And a demonstrator receiving control and placebo cream. We counterbalance the color. We controlled for many variables. So during the observation of another person when they were not stimulated, they didn't receive the cream, there is an activation of the left and right temporoparietal junction and a different activation of the amygdala with the two creams. And importantly, an activation of the periaqueductal gray that I show to you is critical in modulating placebo analgesia.

Afterwards we put both the placebo creams with the two different color. We tailored the level of pain at the identical level of intensity. And we saw how placebo effects through observation are generated. They create strong different expectation and anxiety. And importantly, we found that the functional connectivity between the dorsolateral prefrontal cortex and temporoparietal junction that was active during the observation mediate the behavior results. Suggesting that there is some mechanism here that may be relevant to exploit in clinical trials and clinical practice.

From this, I wish to switch to a more translational approach. Can we replicate these results observed in health participant for nociception in people suffering from chronic pain. So we chose as population of facial pain that is an orphan disease that has no consensus on how to treat it, but also it affects the youngest including children.

So participants were coming to the lab. And thus you can see we used the same identical thermal stimulation, the same electrodes, the same conditioning that I showed to you. We measured expectation before and after the manipulation. The very first question was can we achieve similar monitored distribution of placebo analgesia in people suffering chronically from pain and comorbidities. You can see that we found no difference between temporo parenthala, between TMD and controls. Also, we observed that some people responded to the placebo manipulation with hyperalgesia. We call this nocebo effect.

Importantly, these affects are less relevant than the benefit that sometime can be extremely strong show that both health control and TMD. Because we run experiment in a very beautiful ecological environment where we are diverse, the lab, the experimenters as well as the population we recruit in the lab has a very good distribution of race, ethnicity.

So the very first question was we need to control for this factor. And this turned out to be a beautiful model to study race, ethnicity in the lab. So when chronic pain patient were studied by same experimenter race, dark blue, we observe a larger placebo effect. And this tell us about the disparity in medicine. In fact, we didn't see these effects in our controls.

In chronic pain patient, we also saw a sex concordance influence. But in the opposite sense in women studied by a man experimenter placebo effects are larger. Such an effect was not seen in men.

The other question that we had was what about the contribution of psychological factors. At that stage, there were many different survey used by different labs. Some are based on the different area of, you know, the states of the world, there were trends where in some people in some study they observe an effects of neurodisease, more positive and negative set, that refer to the words. Instead of progressing on single survey, and now we have a beautiful meta-analysis today that is not worth in the sense that it is not predictive of placebo effects.

We use the rogue model suggested by the NIMH. And by doing a sophisticated approach we were able to combine this into four balances. Emotional distress, reward-seeking, pain related fear catastrophizing, empathy and openness. These four valences then were interrelated to predict placebo effects. And you can see that emotional distress is associated with lower magnitude of placebo effects extinguishing over time and lower proportion of placebo responsivity.

Also people who tend to catastrophizing display lower magnitude of placebo effects. In terms of expectation, it is also interesting patients expect to benefit, they have this desire for a reward. But also those people who are more open and characterized by empathy tend for the larger expectations. But this doesn't translate necessarily in larger placebo effects, somehow hinting that the two phenomenon can be not necessarily linked.

Because we study chronic pain patients they come with their own baggage of disease comorbidities. And Dr. Wang in his department look at insomnia. Those people suffering from insomnia tends to have lower placebo analgesic effects along with those who have a poor pattern of sleep, suggesting that clinical factor can be relevant when we wish to predict placebo effects.

Another question that we address how simple SNPs, single nucleotide polymorphism variants in three regions that have been published can be predictive of placebo effects. In particular, I'm referring to OPRM1 that is linked to the gene for endogenous opioids. COMT linked to endogenous dopamine. And FAAH linked to endogenous cannabinoids. And we will learn about that more with the next talk.

And you can see that there is a prediction. These are rogue codes that can be interesting. We model all participants with verbal suggestion alone, the conditioning. There isn't really a huge difference between using one SNP versus two or three. What is truly impact and was stronger in terms of prediction was accounting for the procedure we used to study placebo. Whether by suggestion alone versus condition. When we added the manipulation, the prediction becomes stronger.

More recently, we started gene expression transcriptomic profile associated with placebo effects. We select from the 402 participants randomly 54. And we extract their transcriptomic profiles. Also we select a validation cohort to see if we can't replicate what we discover in terms of mRNA sequencing. But we found over 600 genes associated with the discovered cohort. In blue are the genes downregulated and in red upregulated.

We chose the top 20 genes and did the PCA to validate the top 20. And we found that six of them were replicated and they include all these genes that you see here. The Selenom for us was particularly interesting, as well as the PI3, the CCDC85B, FBXL15, HAGHL and the TNFRSF4. So with this --

LUANA COLLOCA: Yes, I'm done. With this, that is the goal probably one day with AI and other approach to combine clinical psychological brain imaging and so on, characteristic and behavior to predict a level of transitory response to placebo. That may guide us in clinical trials and clinical path to tailor the treatment. Therefore, the placebo and nocebo biological response can be to some extent predicted. And identify those who responded to placebo can help tailoring drug development and symptom management.

Thank you to my lab. All of you, the funding agencies. And finally, for those who like to read more about placebo, this book is available for free to be downloaded. And they include many of the speakers from this two-day event as contributors to this book. Thank you very much.

CRISTINA CUSIN: Thank you so much, Luana. It was a wonderful presentation. We have one question in the Q&A.

Elegant studies demonstrating powerful phenomena. Two questions. Is it possible to extend or sustain placebo-boosting effect? And what is the dose response relationship with placebo or nocebo?

LUANA COLLOCA: Great questions. The goal is to boost a placebo effects. And one way, as I showed was, for example, using intranasal vasopressin. But also extending relationship with placebo we know that we need the minimum of a three or four other administration before boosting this sort of pharmacological memory. And the longer is the administration of the active drug before we replace with placebo, the larger the placebo effects.

For nocebo, we show similar relationship with the collaborators. So again, the longer we condition, the stronger the placebo or nocebo effects. Thank you so much.

CRISTINA CUSIN: I wanted to ask, do you have any theory or interpretation about the potential for transmit to person a placebo response between the observer or such, do you have any interpretation of this phenomenon?

LUANA COLLOCA: It is not completely new in the literature. There is a lot of studies show that we can transfer pain in both animal models and humans.

So transfer analgesia is a natural continuation of that line of research. And the fact that we mimic things that we see in some other people, this is the very most basic form of learning when we grow up. But also from a revolutionary point of view protect us from predators and animals and us as human beings observing is a very good mechanism to boost behaviors and in this case placebo effects. Thank you.

CRISTINA CUSIN: Okay. We will have more time to ask questions.

We are going to move on to the next speaker. Dr. Kathryn Hall.

KATHRYN HALL: Thank you. Can you see my screen okay? Great.

So I'm going to build on Dr. Colloca's talk to really kind of give us a deeper dive into the genetics of the placebo response in clinical trials.

So I have no disclosures. So as we heard and as we have been hearing over the last two days, there is -- there are physiological drivers of placebo effects, whether they are opioid signaling or dopamine signaling. And these are potentiated by the administration or can be potentiated by saline pills, saline injections, sugar pills. And what's really interesting here, I think, is this discussion about how drugs impact the drivers of placebo response. In particular we heard about Naloxone yesterday and proglumide.

What I really want to do today is think about the next layer. Like how do the genes that shape our biology and really drive or influence that -- those physiological drivers of placebo response, how do the genes, A, modify our placebo response? But also, how are they modifying the effect of the drugs and the placebos on this basic -- this network?

And if you think about it, we really don't know much about all of the many interactions that are happening here. And I would actually argue that it goes even beyond genetic variation to other factors that lead to heterogeneity in clinical trials. Today I'm going to really focus on genes and variations in the genome.

So let's go back so we have the same terminology. I'm going to be talking about placebo-responsing trials. And so we saw this graph or a version of this graph yesterday where in clinical trials when we want to assess the effect of a drug, we subtract the outcomes in the placebo arm from the outcomes in the drug treatment arm. And there is a basic assumption here that the placebo response is additive to the drug response.

And what I want to do today is to really challenge that assumption. I want to challenge that expectation. Because I think we have enough literature and enough studies that have already been done that demonstrate that things are not as simple as that and that we might be missing a lot from this basic averaging and subtracting that we are doing.

So the placebo response is that -- is the bold lines there which includes placebo effects which we have been focusing on here. But it also includes a natural history of the disease or the condition, phenomenon such as statistical regression not mean, blinding and bias and Hawthorn effects. So we lump all of those together in the placebo arm of the trial and subtract the placebo response from the drug response to really understand the drug effect.

So one way to ask about, well, how do genes affect this is to look at candidate genes. And as Dr. Colloca pointed out and has done some very elegant studies in this area, genes like COMT, opioid receptors, genes like OPRM1, the FAAH endocannabinoid signaling genes are all candidate genes that we can look at in clinical trials and ask did these genes modify what we see in the placebo arm of trials?

We did some studies in COMT. And I want to just show you those to get a -- so you can get a sense of how genes can influence placebo outcomes. So COMT is catacholamethyl transferase. And it's a protein, an enzyme that metabolizes dopamine which as you saw is important in mediating the placebo response. COMT also metabolizes epinephrin, norepinephrine and catecholest estrogen. So the fact that COMT might be involved in placebo response is really interesting because it might be doing more than just metabolizing dopamine.

So we asked the question what happens if we look at COMT genetic variation in clinical trials of irritable bowel syndrome? And working with Ted Kaptchuk and Tony Lembo at Beth Israel Deaconess Medical Center, we did just that. We looked at COMT effects in a randomized clinical trial of irritable bowel syndrome. And what we did see was that for the gene polymorphism RS46AD we saw that people who had the weak version of the COMT enzyme actually had more placebo response. These are the met/met people here shown on this, in this -- by this arrow. And that the people who had less dopamine because that enzyme didn't work as well for this polymorphism, they had less of a placebo response in one of the treatment arms. And we would later replicate this study in another clinical trial that was recently concluded in 2021.

So to get a sense, as you can see, we are somewhat -- we started off being somewhat limited by what was available in the literature. And so we wanted to expand on that to say more about genes that might be associated with placebo response. So we went back, and we found 48 studies in the literature where there was a gene that was looked at that modified the placebo response.

And when we mapped those to the interactome, which is this constellation of all gene products and their interactions, their physical interactions, we saw that the placebome or the placebo module had certain very interesting characteristics. Two of those characteristics that I think are relevant here today are that they overlapped with the targets of drugs, whether they were analgesics, antidepressive drugs, anti-Parkinson's agents, placebo genes putatively overlapped with drug treatment genes or targets.

They also overlapped with disease-related genes. And so what that suggests is that when we were looking at the outcomes of clinical trial there might be a lot more going on that we are missing.

And let's just think about that for a minute. On the left is what we expect. We expect that we are going to see an effect in the drug, it's going to be greater than the effect of the placebo and that difference is what we want, that drug effect. But what we often see is on the right here where there is really no difference between drug and placebo. And so we are left to scratch our heads. Many companies go out of business. Many sections of companies close. And, quite frankly, patients are left in need. Money is left on the table because we can't discern between drug and placebo.

And I think what is interesting is that's been a theme that's kind of arisen since yesterday where oh, if only we had better physiological markers or better genes that targeted physiology then maybe we could see a difference and we can, you know, move forward with our clinical trials.

But what I'm going to argue today is actually what we need to do is to think about what is happening in the placebo arm, what is contributing to the heterogeneity in the placebo arm, and I'm going to argue that when we start to look at that compared to what is happening in the drug treatment arm, oftentimes -- and I'm going to give you demonstration after demonstration. And believe me, this is just the tip of the iceberg.

What we are seeing is there are differential effects by genotype in the drug treatment arm and the placebo treatment arm such that if you average out what's happening in these -- in these drug and placebo arms, you would basically see that there is no difference. But actually there's some people that are benefiting from the drug but not placebo. And conversely, benefiting from placebo but not drug. Average out to no difference.

Let me give you some examples. We had this hypothesis and we started to look around to see if we could get partners who had already done clinical trials that had happened to have genotyped COMT. And what we saw in this clinical trial for chronic fatigue syndrome where adolescents were treated with clonidine was that when we looked in the placebo arm, we saw that the val/val patients, so this is the COMT genotype. The low activity -- sorry, that is high activity genotype. They had the largest number increase in the number of steps they were taking per week. In contrast, the met/met people, the people with the weaker COMT had fewer, almost no change in the number of steps they were taking per week.

So you would look at this and you would say, oh, the val/val people were the placebo responders and the met/met people didn't respond to placebo. But what we saw when we looked into the drug treatment arm was very surprising. We saw that clonidine literally erased the effect that we were seeing in placebo for the val/val participants in this trial. And clonidine basically was having no effect on the heterozygotes, the val/mets or on the met/mets. And so this trial rightly concluded that there was no benefit for clonidine.

But if they hadn't taken this deeper look at what was happening, they would have missed that clonidine may potentially be harmful to people with chronic fatigue in this particular situation. What we really need to do I think is look not just in the placebo or not just in the drug treatment arm but in both arms to understand what is happening there.

And I'm going to give you another example. And, like I said, the literature is replete with these examples. On the left is an example from a drug that was used to test cognitive -- in cognitive scales, Tolcupone, which actually targets COMT. And what you can see here again on the left is differential outcomes in the placebo arm and in the drug treatment arm that if you were to just average these two you would not see the differences.

On the right is a really interesting study looking at alcohol among people with alcohol disorder, number of percent drinking days. And they looked at both COMT and OPRM1. And this is what Dr. Colloca was just talking about there seemed to be not just gene-placebo drug interactions but gene-gene drug placebo interactions. This is a complicated space. And I know we like things to be very simple. But I think what these data are showing is we need to pay more attention.

So let me give you another example because these -- you know, you could argue, okay, those are objective outcomes -- sorry, subjective outcomes. Let's take a look at the Women's Health Study. Arguably, one of the largest studies on aspirin versus placebo in history. 30,000 women were randomized to aspirin or placebo. And lo and behold, after 10 years of following them the p value was nonsignificant. There was no difference between drug and placebo.

So we went to this team, and we asked them, could we look at COMT because we had a hypothesis that COMT might modify the outcomes in the placebo arm and potentially differentially modify the treatments in the drug treatment arm. You might be saying that can't have anything to do with the placebo effect and we completely agree. This if we did find it would suggest that there might be something to do with the placebo response that is related to natural history. And I'm going to show you the data that -- what we found.

So when we compared the outcomes in the placebo arm to the aspirin arm, what we found was the met/met women randomized to placebo had the highest of everybody rates of cardiovascular disease. Which means the highest rates of myocardial infarction, stroke, revascularization and death from a cardiovascular disease cause. In contrast, the met/met women on aspirin had benefit, had a statistically significant reduction in these rates.

Conversely, the val/val women on placebo did the best, but the val/val women on aspirin had the highest rates, had significantly higher rates than the val/val women on placebo. What does this tell us? Well, we can't argue that this is a placebo effect because we don't have the control for placebo effects, which is a no treatment control.

But we can say that these are striking differences that, like I said before, if you don't pay attention to them, you miss the point that there are subpopulations for benefit or harm because of differential outcomes in the drug and placebo arms of the trial.

And so I'm going to keep going. There are other examples of this. We also partnered with a group at Brigham and Women's Hospital that had done the CAMP study, the Childhood Asthma Management Study. And in this study, they randomized patients to placebo, Budesonide or Nedocromil for five years and study asthma outcomes.

Now what I was showing you previously was candidate gene analyses. What this was, was a GWAS. We wanted to be agnostic and ask are there genes that modify the placebo outcomes and are these outcomes different in the -- when we look in the drug treatment arm. And so that little inset is a picture of all of the genes that were looked at in the GWAS. And we had a borderline genome Y significant hit called BBS9. And when we looked at BBS9 in the placebo arm, those white boxes at the top are the baseline levels of coughing and wheezing among these children. And in the gray are at the end of the treatment their level of coughing and wheezing.

And what you can see here is that participants with the AA genotype were the ones that benefited from the Bedenoside -- from placebo, whereas the GG, the patients with the GG genotype really there was no significant change.

Now, when we looked in the drug treatment arms, we were surprised to see that the outcomes were the same, of course, at baseline. There is no -- everybody is kind of the same. But you can see the differential responses depending on the genotype. And so, again, not paying attention to these gene drug/placebo interactions we miss another story that is happening here among our patients.

Now, I just want to -- I added this one because it is important just to realize that this is not just about gene-drug placebo. But these are also about epigenetic effects. And so here is the same study that I showed earlier on alcohol use disorder. They didn't just stop at looking at the polymorphisms or the genetic variants. This team also went so far as to look at methylation of OPRM1 and COMT.

So methylation is basically when the promoter region of a gene is basically blocked because it has a methyl group. It has methylation on some of the nucleotides in that region. So you can't make the protein as efficiently. And if you look on the right, what you can see in the three models that they looked at, they looked at other genes. They also looked at SLC6A3 that's involved in dopamine transport. And what you can see here is that there is significant gene by group by time interactions for all these three genes, these are candidate genes that they looked at.

And even more fascinating is their gene-by-gene interactions. Basically it is saying that you cannot say what the outcome is going to be unless you know the patient's or the participant's COMT or OPRM genotype A and also how methylated the promoter region of that -- of these genes are. So this makes for a very complicated story. And I know we like very simple stories.

But I want to say that I'm just adding to that picture that we had before to say that it's not just in terms of the gene's polymorphisms, but as Dr. Colloca just elegantly showed it is transcription as well as methylation that might be modifying what is happening in the drug treatment arm and the placebo treatment arm. And to add to this it might also be about the natural history of the condition.

So BBS9 is actually a gene that is involved in the cilia, the activity of the formation of the cilia which is really important in breathing in the nasal canal. And so, you can see that it is not just about what's happening in the moment when you are doing the placebo or drug or the clinical trial, it also might -- the genes might also be modifying where the patient starts out and how the patient might develop over time. So, in essence, we have a very complicated playground here.

But I think I have shown you that genetic variation, whether it is polymorphisms in the gene, gene-gene interactions or epigenetics or all of the above can modify the outcomes in placebo arms of clinical trials. And that this might be due to the genetic effects on placebo effects or the genetic effects on natural history. And this is something I think we need to understand and really pay attention to.

And I also think I've showed you, and these are just a few examples, there are many more. But genetic variation can differentially modify drugs and placebos and that these potential interactive effects really challenge this basic assumption of additivity that I would argue we have had for far too long and we really need to rethink.

TED KAPTCHUK: (Laughing) Very cool.

KATHRYN HALL: Hi, Ted.

TED KAPTCHUK: Oh, I didn't know I was on.

KATHRYN HALL: Yeah, that was great. That's great.

So in summary, can we use these gene-placebo drug interactions to improve clinical trials. Can we change our expectations about what is happening. And perhaps as we have been saying for the last two days, we don't need new drugs with clear physiological effects, what we need is to understand drug and placebo interactions and how they impact subpopulations and can reveal who benefits or is harmed by therapies.

And finally, as we started to talk about in the last talk, can we use drugs to boost placebo responses? Perhaps some drugs already do. Conversely, can we use drugs to block placebo responses? And perhaps some drugs already do.

So I just want to thank my collaborators. There was Ted Kaptchuk, one of my very close mentors and collaborators. And really, thank you for your time.

CRISTINA CUSIN: Thank you so much. It was a terrific presentation. And definitely Ted's captured laugh, it was just one of the best spontaneous laughs.

We have a couple of questions coming through the chat. One is about the heterogeneity of response in placebo arms. It is not uncommon to see quite a dispersion of responses at trials. Was that thought experiment, if one looks at the fraction of high responders in the placebo arms, would one expect to see, enrich for some of the genetic marker for and as placebo response?

KATHRYN HALL: I absolutely think so. We haven't done that. And I would argue that, you know, we have been having kind of quiet conversation here about Naloxone because I think as Lauren said yesterday that the findings of Naloxone is variable. Sometimes it looks like Naloxone is blocking placebo response and sometimes it isn't.

We need to know more about who is in that trial, right? Is this -- I could have gone on and showed you that there is differences by gender, right. And so this heterogeneity that is coming into clinical trials is not just coming from the genetics. It's coming from race, ethnicity, gender, population. Like are you in Russia or are you in China or are you in the U.S. when you're conducting your clinical trial? We really need to start unpacking this and paying attention to it. I think because we are not paying attention to it, we are wasting a lot of money.

CRISTINA CUSIN: And epigenetic is another way to consider traumatic experiences, adverse event learning. There is another component that we are not tracking accurately in clinical trials. I don't think this is a one of the elements routinely collected. Especially in antidepressant clinical trials it is just now coming to the surface.

KATHRYN HALL: Thank you.

CRISTINA CUSIN: Another question comes, it says the different approaches, one is GWAS versus candidate gene approach.

How do you start to think about genes that have a potential implication in neurophysiological pathways and choosing candidates to test versus a more agnostic U.S. approach?

KATHRYN HALL: I believe you have to do both because you don't know what you're going to find if you do a GWAS and it's important to know what is there.

At the same time, I think it's also good to test our assumptions and to replicate our findings, right? So once you do the GWAS and you have a finding -- for instance, our BBS9 finding would be amazing to replicate or to try and test in another cohort. But, of course, it is really difficult to do a whole clinical trial again. These are very expensive, and they last many years.

And so, you know, I think replication is something that is tough to do in this space, but it is really important. And I would do both.

CRISTINA CUSIN: Thank you. We got a little short on time. We are going to move on to the next speaker. Thank you so much.

FADEL ZEIDAN: Good morning. It's me, I imagine. Or good afternoon.

Let me share my screen. Yeah, so good morning. This is going to be a tough act to follow. Dr. Colloca and Dr. Hall's presentations were really elegant. So manage your expectations for mine. And, Ted, please feel free to unmute yourself because I think your laugh is incredibly contagious, and I think we were all were laughing as well.

So my name is Fadel Zeidan, I'm at UC San Diego. And I'll be discussing mostly unpublished data that we have that's under review examining if and how mindfulness meditation assuages pain and if the mechanism supporting mindfulness meditation-based analgesia are distinct from placebo.

And so, you know, this is kind of like a household slide that we all are here because we all appreciate how much of an epidemic chronic pain is and, you know, how significant it is, how much it impacts our society and the world. And it is considered a silent epidemic because of the catastrophic and staggering cost to our society. And that is largely due to the fact that the subjective experience of pain is modulated and constructed by a constellation of interactions between sensory, cognitive, emotional dimensions, genetics, I mean I can -- the list can go on.

And so what we've been really focused on for the last 20 years or so is to appreciate if there is a non-pharmacological approach, a self-regulated approach that can be used to directly assuage the experience of pain to acutely modify exacerbated pain.

And to that extent, we've been studying meditation, mindfulness-based meditation. And mindfulness is a very nebulous construct. If you go from one lab to another lab to another lab, you are going to get a different definition of what it is. But obviously my lab's definition is the correct one. And so the way that we define it is awareness of arising sensory events without reaction, without judgment.

And we could develop this construct, this disposition by practicing mindfulness-based meditation, which I'll talk about here in a minute. And we've seen a lot of -- and this is an old slide -- a lot of new evidence, converging evidence demonstrating that eight weeks of manualized mindfulness-based interventions can produce pretty robust improvements in chronic pain and opiate misuse. These are mindfulness-based stress reduction programs, mindfulness-oriented recovery enhancement, mindfulness-based cognitive therapy which are about eight weeks long, two hours of formalized didactics a week, 45 minutes a day of homework.

There is yoga, there is mental imagery, breathing meditation, walking meditation, a silent retreat and about a $600 tab. Which may not be -- I mean although they are incredibly effective, may not be targeting demographics and folks that may not have the time and resources to participate in such an intense program.

And to that extent and, you know, as an immigrant to this country I've noticed that we are kind of like this drive-thru society where, you know, we have a tendency to eat our lunches and our dinners in our cars. We're attracted to really brief interventions for exercise or anything really, pharmaceuticals, like ":08 Abs" and "Buns of Steel." And we even have things called like the military diet that promise that you'll lose ten pounds in three days without dying.

So we seemingly are attracted to these fast-acting interventions. And so to this extent we've worked for quite some time to develop a very user friendly, very brief mindfulness-based intervention. So this is an intervention that is about four sessions, 20 minutes each session. And participants are -- we remove all religious aspects, all spiritual aspects. And we really don't even call it meditation, we call it mindfulness-based mental training.

And our participants are taught to sit in a straight posture, close their eyes, and to focus on the changing sensations of the breath as they arise. And what we've seen is this repetitive practice enhances cognitive flexibility and the ability to -- flexibility and the ability to sustain attention. And when individual's minds drift away from focusing on the breath, they are taught to acknowledge distractive thoughts, feelings, emotions without judging themselves or the experience. Doing so by returning their attention back to the breath.

So there is really a one-two punch here where, A, you're focusing on the breath and enhancing cognitive flexibility; and, B, you're training yourself to not judge discursive events. And that we believe enhances emotion regulation. So quite malleable to physical training we would say mental training. Now that we have the advent of imaging, we can actually see that there are changes in the brain related to this.

But as many of you know, mindfulness is kind of like a household term now. It's all over our mainstream media. You know, we have, you know, Lebron meditating courtside. Oprah meditating with her Oprah blanket. Anderson Cooper is meditating on TV. And Time Magazine puts, you know, people on the cover meditating. And it's just all over the place.

And so these types of images and these types of, I guess, insinuations could elicit nonspecific effects related to meditation. And for quite some time I've been trying to really appreciate not is meditation more effective than placebo, although that's interesting, but does mindfulness meditation engage mechanisms that also are shared by placebo? So beliefs that you are meditating could elicit analgesic responses.

The majority of the manualized interventions in their manuals they use terms like the power of meditation, which I guarantee you is analgesic. To focus on the breath, we need to slow the breath down. Not implicit -- not explicitly, but it just happens naturally. And slow breathing can also reduce pain. Facilitator attention, social support, conditioning, all factors that are shared with other therapies and interventions but in particular are also part of meditation training.

So the question is because of all this, is mindfulness meditation merely -- or not merely after these two rich days of dialogue -- but is mindfulness meditation engaging processes that are also shared by placebo.

So if I apply a placebo cream to someone's calf and then throw them in the scanner versus asking someone to meditate, the chances are very high that the brain processes are going to be distinct. So we wanted to create a -- and validate an operationally matched mindfulness meditation intervention that we coined as sham mindfulness meditation. It's not sham meditation because it is meditation. It's a type of meditative practice called Pranayama.

But here in this intervention we randomize folks, we tell folks that they've been randomized to a genuine mindfulness meditation intervention. Straight posture, eyes closed. And every two to three minutes they are instructed to, quote-unquote, take a deep breath as we sit here in mindfulness meditation. We even match the time giving instructions between the genuine and the sham mindfulness meditation intervention.

So the only difference between the sham mindfulness and the genuine mindfulness is that the genuine mindfulness is taught to explicitly focus on the changing sensations of the breath without judgment. The sham mindfulness group is just taking repetitive deep, slow breaths. So if the magic part of mindfulness, if the active component of mindfulness is this nonjudgmental awareness, then we should be able to see disparate mechanisms between these.

And we also use a third arm, a book listening control group called the "Natural History of Selborne" where it's a very boring, arguably emotionally pain-evocating book for four days. And this is meant to control for facilitator time and -- sorry, facilitator attention and the time elapsed in the other group's interventions.

So we use a very high level of noxious heat to the back of the calf. And we do so because imaging is quite expensive, and we want to ensure that we can see pain-related processing within the brain. Here and across all of our studies, we use ten 12-second plateaus of 49 degrees to the calf, which is pretty painful.

And then we assess pain intensity and pain unpleasantness using a visual analog scale, where here the participants just see red the more they pull on the algometer the more in pain they are. But on the back, the numbers fluoresce where 0 is no pain and 10 is the worst pain imaginable.

So pain intensity can be considered like sensory dimension of pain, and pain unpleasantness could be more like I don't want to say pain affect but more like the bothersome component of pain, pain unpleasantness. So what we did was we combined all of our studies that have used the mindfulness, sham mindfulness in this book listing control, to see does mindfulness meditation engage is mindfulness meditation more effective than sham mindfulness meditation at reducing pain.

We also combined two different fMRI techniques: Blood oxygen dependent level signalling, bold, which allows us a higher temporal resolution and signal to noise ratio than, say, perfusion imaging technique and allows us to look at connectivity. However, meditation is also predicated on changes in respiration rate which could elicit pretty dramatic artifacts in the brain, breathing related artifacts explicitly related to CO2 output.

So using the perfusion based fMRI technique like arterial spin labeling is really advantageous as well, although it's not as temporally resolute as bold, it provides us a direct quantifiable measurement of cerebral blood flow.

So straight to the results. On the Y axis we have the pain ratings, and on the X axis are book listening controls sham mindfulness meditation, mindfulness meditation. Here are large sample sizes. Blue is intensity and red is unpleasantness. This is the post intervention fMRI scans where we see the first half of the scan to the second half of the scan our controlled participants are simply resting and pain just increases because of pain sensitization and being in a claustrophobic MRI environment.

And you can see here that sham mindfulness meditation does produce pretty significant reduction in pain intensity and unpleasantness, more than the control book. But mindfulness meditation is more effective than sham mindfulness and the controls at reducing pain intensity and pain unpleasantness.

There does seem to be some kind of additive component to the genuine intervention, although this is a really easy practice, the sham techniques.

So for folks that have maybe fatigue or cognitive deficits or just aren't into doing mindfulness technique, I highly recommend this technique, which is just a slow breathing approach, and it's dead easy to do.

Anyone that's practiced mindfulness for the first time or a few times can state that it can be quite difficult and what's the word? -- involving, right?

So what happened in the brain? These are our CBF maps from two studies that we replicated in 2011 and '15 where we found that higher activity, higher CBF in the right anterior insula, which is ipsilateral to the stimulation site and higher rostral anterior cingulate cortex subgenual ACC was associated with greater pain relief, pain intensity, and in the context of pain unpleasantness, higher over the frontal cortical activity was associated with lower pain, and this is very reproducible where we see greater thalamic deactivation predicts greater analgesia on the unpleasantness side.

These areas, obviously right entry insula in conjunction with other areas is associated with interoceptive processing awareness of somatic sensations. And then the ACC and the OFC are associated with higher order cognitive flexibility, emotional regulation processes. And the thalamus is really the gatekeeper from the brain -- I'm sorry, from the body to the brain. Nothing can enter the brain except unless it goes through the thalamus, except if it's the sense of smell.

So it's really like this gatekeeper of arising nociceptive information.

So the takehome here is that mindfulness is engaging multiple neural processes to assuage pain. It's not just one singular pathway.

Our gold studies were also pretty insightful. Here we ran a PPI analysis, psychophysiologic interaction analysis and this was whole brain to see what brain regions are associated with pain relief on the context of using the bold technique, and we find that greater ventral medial prefrontal cortical activity deactivation I'm sorry is associated with lower pain, and the vmPFC is a super evolved area that's associated with, like, higher order processes relating to self. It's one of the central nodes of the so called default mode network, a network supporting self referential processing. But in the context of the vmPFC, I like the way that Tor and Mathieu reflect the vmPFC as being more related to affective meaning and has a really nice paper showing that vmPFC is uniquely involved in, quote/unquote, self ownership or subjective value, which is particularly interesting for the context of pain because pain is a very personal experience that's directly related to the interpretation of arising sensations and what they mean to us.

And seemingly -- I apologize for the reverse inferencing here -- but seemingly mindfulness meditation based on our qualitative assessments as well is reducing the ownership or the intrinsic value, the contextual value of those painful sensations, i.e., they don't feel like they bother -- that pain is there but it doesn't bother our participants as much, which is quite interesting as a manipulation.

We also ran our connectivity analysis between the contralateral thalamus and the whole brain, and we found that greater decoupling between the contralateral thalamus and the precuneus, another central node of the default mode network predicted greater analgesia.

This is a really cool, I think, together mechanism showing that two separate analyses are indicating that the default mode network could be an analgesic system which we haven't seen before. We have seen the DMN involved in chronic pain and pain related exacerbations, but I don't think we've seen it as being a part of an analgesic, like being a pain relieving mechanism. Interestingly, the thalamus and precuneus together are the first two nodes to go offline when we lose consciousness, and they're the first two nodes to come back online when we recover from consciousness, suggesting that these two -- that the thalamus and precuneus are involved in self referential awareness, consciousness of self, things of this nature.

Again, multiple processes involved in meditation based pain relief which maybe gives rise to why we are seeing consistently that meditation could elicit long lasting improvements in pain unpleasantness, in particular, as compared to sensory pain. Although it does that as well.

And also the data gods were quite kind on this because these mechanisms are also quite consistent with the primary premises of Buddhist and contemplative scriptures saying that the primary principle is that your experiences are not you.

Not that there is no self, but that the processes that arise in our moment to moment experience are merely reflections and interpretations in judgments, and that may not be the true inherent nature of mind.

And so before I get into more philosophical discourse, I'm going to keep going for the sake of time. Okay.

So what happened with the sham mindfulness meditation intervention?

We did not find any neural processes predicted analgesia significantly and during sham mindfulness meditation. What did predict analgesia during sham mindfulness was slower breathing rate, which we've never seen before with mindfulness. We've never seen a significant or even close to significant relationship between mindfulness based meditation analgesia and slow breathing. But over and over we see that sham mindfulness based analgesia is related to slower breathing which provides us this really cool distinct process where kind of this perspective where mindfulness is engaging higher order top down type processes to assuage pain while sham mindfulness may be engaging this more bottom up type response to assuage pain.

I'm going to move on to some other new work, and this is in great collaboration with the lovely Tor Wager, and he's developed, with Marta and Woo, these wonderful signatures, these machine learned multivariate pattern signatures that are remarkably accurate at predicting pain over I think like 98, 99 percent.

His seminal paper, the Neurological Pain Signature, was published in the New England Journal of Medicine that showed that these signatures can predict nociceptive specific, in particular, for this particular, thermal heat pain with incredible accuracy.

And it's not modulated by placebo or affective components, per se. And then the SIIPS is a machine learned signature that is, as they put it, associated with cerebral contributions to pain. But if you look at it closely, these are markers that are highly responsive to the placebo response.

So the SIIPS can be used -- he has this beautiful pre print out, showing that it does respond with incredible accuracy to placebo, varieties of placebo.

So we used this MVPA to see if meditation engages signature supporting placebo responses.

And then Marta Ceko's latest paper with Tor published in Nature and Neuro found that the negative affect of signature predicts pain responses above and beyond nociceptive related processes. So this is pain related to negative affect, which again contributes to the multimodal processing of pain and how now we could use these elegant signatures to kind of disentangle which components of pain meditation and other techniques assuage. Here's the design.

We had 40 -- we combined two studies. One with bold and one with ASL. So this would be the first ASL study with signatures, with these MVPA signatures.

And we had the mindfulness interventions that I described before, the book listing interventions I described before and a placebo cream intervention which I'll describe now, all in response to 49 degrees thermal stimuli.

So across again all of our studies we use the same methods. And the placebo group -- I'll try to be quick about this -- this is kind of a combination of Luana Colloca, Don Price and Tor's placebo conditioning interventions where we administer 49 degrees -- we tell our participants that we're testing a new form of lidocaine, and the reason that it's new is that the more applications of this cream, the stronger the analgesia.

And so in the conditioning sessions, they come in, administer 49 degrees, apply and remove this cream, which is just petroleum jelly after 10 minutes, and then we covertly reduce the temperature to 48.

And then they come back in in session two and three, after 49 degrees and removing the cream, we lower the temperature to 47. And then on the last conditioning session, after we remove the cream, we lower the temperature to 46.5, which is a qualitatively completely different experience than 49.

And we do this to lead our participants to believe that the cream is actually working.

And then in a post intervention MRI session, after we remove the cream, we don't modulate the temperature, we just keep it at 49, and that's how we measured placebo in these studies. And then so here, again -- oops -- John Dean and Gabe are coleading this project.

Here, pain intensity on this axis, pain unpleasantness on that axis, controls from the beginning of the scan to the end of the scan significantly go up in pain.

Placebo cream was effective at reducing intensity and unpleasantness, but we see mindfulness meditation was more effective than all the conditions at reducing pain. The signatures, we see that the nociceptive specific signature, the controls go up in pain here.

No change in the placebo and mindfulness meditation you can see here produces a pretty dramatic reduction in the nociceptive specific signature.

The same is true for the negative affective pain signature. Mindfulness meditation uniquely modifies this signature as well which I believe this is one of the first studies to show something like this.

But it does not modulate the placebo signature. What does modulate the placebo signature is our placebo cream, which is a really nice manipulation check for these signatures.

So here, taken together, we show that mindfulness meditation, again, is engaging multiple processes and is reducing pain by directly assuaging nociceptive specific markers as well as markers supporting negative affect but not modulating placebo related signatures, providing further credence that it's not a placebo type response, and we're also demonstrating this granularity between a placebo mechanism that's not being shared by another active mechanism. While we all assume that active therapies and techniques are using a shared subset of mechanisms or processes with placebo, here we're providing accruing evidence that mindfulness is separate from a placebo.

I'll try to be very quick on this last part. This is all not technically related placebo, but I would love to hear everyone's thoughts on these new data we have.

So as we've seen elegantly that pain relief by placebo, distraction, acupuncture, transcranial magnetic stimulation, prayer, are largely driven by endogenous opioidergic release. And, yes, there are other systems. A prime other system is the (indiscernible) system, serotonergic system, dopamine. The list can go on. But it's considered by most of us that the endogenous opioidergic system is this central pain modulatory system.

And the way we do this is by antagonizing endogenous opioids by employing incredibly high administration dosage of naloxone.

And I think this wonderful paper by Ciril Etnes's (phonetic) group provides a nice primer on the appropriate dosages for naloxone to antagonize opiates. And I think a lot of the discussions here where we see differences in naloxone responses are really actually reflective of differences in dosages of naloxone.

It metabolizes so quickly that I would highly recommend a super large bolus with a maintenance infusion IV.

And we've seen this to be a quite effective way to block endogenous opioids. And across four studies now, we've seen that mindfulness based pain relief is not mediated by endogenous opioids. It's something else. We don't know what that something else is but we don't think it's endogenous opioids. But what if it's sex differences that could be driving these opioidergic versus non opioid opioidergic differences?

We've seen that females require -- exhibit higher rates of chronic pain than males. They are prescribed opiates at a higher rate than men. And when you control for weight, they require higher dosages than men. Why?

Well, there's excellent literature in rodent models and preclinical models that demonstrate that male rodents versus female -- male rodents engage endogenous opioids to reduce pain but female rodents do not.

And this is a wonderful study by Ann Murphy that basically shows that males, in response to morphine, have a greater latency and paw withdrawal when coupled with morphine and not so much with females.

But when you add naloxone to the picture, with morphine, the latency goes down. It basically blocks the analgesia in male rodents but enhances analgesia in female rodents.

We basically asked -- we basically -- Michaela, an undergraduate student doing an odyssey thesis asked this question: Are males and females in humans engaging in distinct systems to assuage pain?

She really took off with this and here's the design. We had heat, noxious heat in the baseline.

CRISTINA CUSIN: Doctor, you have one minute left. Can you wrap up?

FADEL ZEIDAN: Yep. Basically we asked, are there sex differences between males and females during meditation in response to noxious heat? And there are.

Baseline, just change in pain. Green is saline. Red is naloxone. You can see that with naloxone onboard, there's greater analgesia in females, and we reversed the analgesia. Largely, there's no differences between baseline in naloxone in males, and the males are reducing pain during saline.

We believe this is the first study to show something like this in humans. Super exciting. It also blocked the stress reduction response in males but not so much in females. Let me just acknowledge our funders. Some of our team. And I apologize for the fast presentation. Thank you.

CRISTINA CUSIN: Thank you so much. That was awesome.

We're a little bit on short on time.

I suggest we go into a short break, ten minute, until 1:40. Please continue to add your questions in Q&A. Our speakers are going to answer or we'll bring some of those questions directly to the discussion panel at the end of the session today. Thank you so much.

Measuring & Mitigating the Placebo Effect (continued)

CRISTINA CUSIN: Hello, welcome back. I'm really honored to introduce our next speaker, Dr. Marta Pecina. And she's going to talk about mapping expectancy-mood interactions in antidepressant placebo effects. Thank you so much.

MARTA PECINA: Thank you, Cristina. It is my great pleasure to be here. And just I'm going to switch gears a little bit to talk about antidepressant placebo effects. And in particular, I'm going to talk about the relationship between acute expectancy-mood neural dynamics and long-term antidepressant placebo effects.

So while we all know that depression is a very prevalent disorder, and just in 2020, Major Depressive Disorder affected 21 million adults in the U.S. and 280 million adults worldwide. And current projections indicate that by the year 2030 it will be the leading cause of disease burden globally.

Now, response rates to first-line treatments, antidepressant treatments are approximately 50%. And complete remission is only achieved in 30 to 35% of individuals. Also, depression tends to be a chronic disorder with 50% of those recovering from a first episode having an additional episode. And 80% of those with two or more episodes having another recurrence.

And so for patients who are nonresponsive to two intervention, remission rates with subsequent therapy drop significantly to 10 to 25%. And so, in summary, we're facing a disorder that is very resistant or becomes resistant very easily. And in this context, one would expect that antidepressant placebo effects would actually be low. But we all know that this is not the case. The response rate to placebos is approximately 40% compared to 50% response rates to antidepressants. And obviously this varies across studies.

But what we do know and learned yesterday as well is that response rates to placebos have increased approximately 7% over the last 40 years. And so these high prevalence of placebo response in depressions have significantly contributed to the current psychopharmacology crisis where large pharma companies have reduced at least in half the number of clinical trials devoted to CNS disorders.

Now, antidepressant placebo response rates among individuals with depression are higher than in any other psychiatric condition. And this was recently published again in this meta-analysis of approximately 10,000 psychiatric patients. Now, other disorders where placebo response rates are also prevalent are generalized anxiety disorder, panic disorders, HDHC or PTSD. And maybe less frequent, although still there, in schizophrenia or OCD.

Now, importantly, placebo effects appear not only in response to pills but also surgical interventions or devices, as it was also mentioned yesterday. And this is particularly important today where there is a very large development of device-based interventions for psychiatric conditions. So, for example, in this study that also was mentioned yesterday of deep brain stimulation, patients with resistant depression were assigned to six months of either active or some pseudo level DBS. And this was followed by open level DBS.

As you can see here in this table, patients from both groups improved significantly compared to baseline, but there were no significant differences between the two groups. And for this reason, DBS has not yet been approved by the FDA for depression, even though it's been approved for OCD or Parkinson's disease as we all know.

Now what is a placebo effect, that's one of the main questions of this workshop, and how does it work from a clinical neuroscience perspective? Well, as it's been mentioned already, most of what we know about the placebo effect comes from the field of placebo analgesia. And in summary, classical theories of the placebo effect have consistently argued that placebo effects results from either positive expectancies regarding the potential beneficial effects of a drug or classical conditioning where the pairing of a neutral stimulus, in this case the placebo pill, with an unconditioned stimulus, in this case the active drug, results in a conditioned response.

Now more recently, theories of the placebo effect have used computational models to predict placebo effects. And these theories posit that individuals update their expectancies as new sensory evidence is accumulated by signaling the response between what is expected and what is perceived. And this information is then used to refine future expectancies. Now these conceptual models have been incorporated into a trial-by-trial manipulation of both expectancies of pain relief and pain sensory experience. And this has rapidly advanced our understanding of the neural and molecular mechanisms of placebo analgesia.

And so, for example, in these meta analytic studies using these experiments they have revealed really two patterns of distinct activations with decreases in brain activity in regions involving brain processing such as the dorsal medial prefrontal cortex, the amygdala and the thalamus; and increases in brain activity in regions involving effective appraisal, such as the vmDFC, the nucleus accumbens, and the PAG.

Now what happens in depression? Well, in the field of antidepressant placebo effects, the long-term dynamics of mood and antidepressant responses have not allowed us to have such trial-by-trial manipulation of expectancies. And so instead researchers have used broad brain changes in the context of a randomized control trial or a placebo lead-in phase which has, to some extent, limited the progress of the field.

Now despite these methodological limitations of these studies, they provide important insights about the neural correlates of antidepressant placebo effects. In particular, from studies -- two early on studies we can see the placebo was associated with increased activations broadly in cortical regions and decreased activations in subcortical regions. And these deactivations in subcortical regions were actually larger in patients who were assigned to an SSRI drug treatment.

We also demonstrated that there is similar to pain, antidepressant placebo effects were associated with enhanced endogenous opiate release during placebo administration, predicting the response to open label treatment after ten weeks. And we have also -- we and others have demonstrated that increased connectivity between the salience network and the rostral anterior cingulate during antidepressant placebo effects can actually predict short-term and long-term placebo effects.

Now an important limitation, and as I already mentioned, is that this study is basically the delay mechanism of action of common antidepressant and this low dynamics of mood which really limit the possibility of actively manipulating antidepressant expectancies.

So to address this important gap, we develop a trial-by-trial manipulation of antidepressant expectancies to be used inside of the scanner. And the purpose was really to be able to further disassociate expectancy and mood dynamics during antidepressant placebo effects.

And so the basic structure of this test involved an expectancy condition where subjects are presented with a four-second infusion cue followed by an expectancy rating cue, and a reinforcement condition which consist of 20 seconds of some neurofeedback followed by a mood rating cue. Now the expectancy and the reinforcement condition map onto the classical theories of the placebo effect that I explained earlier.

During the expectancy condition, the antidepressant infusions are compared to periods of calibration where no drug is administered. And during the reinforcement condition, on the other hand, some neurofeedback of positive sign 80% of the time as compared to some neurofeedback of baseline sign 80% of the time. And so this two-by-two study design results in four different conditions. The antidepressant reinforced, the antidepressant not reinforced, the calibration reinforced, and the calibration not reinforced.

And so the cover story is that we tell participants that we are testing the effects of a new fast-acting antidepressant compared to a conventional antidepressant, but in reality, they are both saline. And then we tell them that they will receive multiple infusions of these drugs inside of the scanner while we record their brain activity which we call neurofeedback. So then patients learn that positive neurofeedback compared to baseline is more likely to cause mood improvement. But they are not told that the neurofeedback is simulated.

Then we place an intravenous line for the administration of the saline infusion, and we bring them inside of the scanner. For these kind of experiments we recruit individuals who are 18 through 55 with or without anxiety disorders and have a HAMD depression rating scale greater than 16, consistent with moderate depression. They're antidepressant medication free for at least 25 -- 21 days and then we use consenting procedures that involve authorized deception.

Now, as suspected, behavioral results during this test consistently show that antidepressant expectancies are higher during the antidepressant infusions compared to the calibration, especially when they are reinforced by positive sham neurofeedback. Now mood responses also are significantly higher during positive sham neurofeedback compared to baseline. But this is also enhanced during the administration of the antidepressant infusions.

Now interestingly, these effects are moderated by the present severity such that the effects of the test conditions and the expectancies and mood ratings are weaker in more severe depression even though their overall expectancies are higher, and their overall mood are lower.

Now at a neuron level, what we see is that the presentation of the infusion cue is associated with an increased activation in the occipital cortex and the dorsal attention network suggesting greater attention processing engaged during the presentation of the treatment cue. And similarly, the reinforcement condition revealed increased activations in the dorsal attention network with additional responses in the ventral striatum suggesting that individuals processed the sham positive neurofeedback cue as rewarding.

Now an important question for us was now that we can manipulate acute placebo -- antidepressant placebo responses, can we use this experiment to understand the mechanisms implicated in short-term and long-term antidepressant placebo effects. And so as I mentioned earlier, there was emerging evidence suggesting that placebo analgesic could be explained by computational models, in particular reinforcement learning.

And so we tested the hypothesis that antidepressant placebo effects could be explained by similar models. So as you know, under these theories, learning occurs when an experienced outcome differs from what is expected. And this is called the prediction error. And then the expected value of the next possible outcome is updated with a portion of this prediction error as reflected in this cue learning rule.

Now in the context of our experiment, model predicted expectancies for each of the four trial conditions would be updated every time the antidepressant or the calibration infusion cue is presented and an outcome, whether positive or baseline neurofeedback, is observed based on a similar learning rule.

Now this basic model was then compared against two alternative models. One which included differential learning rates to account for the possibility that learning would depend on whether participants were updating expectancies for the placebo or the calibration. And then an additional model to account for the possibility that subjects were incorporating positive mood responses as mood rewards.

And then finally, we constructed this additional model to allow the possibility of the combination of models two and three. And so using patient model comparison, we found that the model -- the fourth model, model four which included a placebo bias learning in our reinforcement by mood dominated all the other alternatives after correction for patient omnibus risk.

Now we then map the expected value and reward predictions error signals from our reinforcement learning models into our raw data. And what we found was that expected value signals map into the salience network raw responses; whereas reward prediction errors map onto the dorsal attention network raw responses. And so all together, the combination of our model-free and model-based results reveal that the processing of the antidepressant in patient cue increase activation in the dorsal attention network; whereas, the encoding of the expectancies took place in the salience network once salience had been attributed to the cue.

And then furthermore, we demonstrated that the reinforcement learning model predicted expectancies in coding the salience network triggered mood changes that are perceived as reward signals. And then these mood reward signals further reinforce antidepressant expectancies through the information of expectancy mood dynamics defined by models of reinforcement learning, an idea that could possibly contribute to the formation of long-lasting antidepressant placebo effects.

And so the second aim was really -- was going to look at these in particular how to use behavioral neuroresponses of placebo effects to predict long-term placebo effects in the context of a clinical trial. And so our hypothesis was that during placebo administration greater salient attribution to the contextual cue in the salience network would transfer to regions involved in mood regulation to induce mood changes. So in particular we hypothesized that the DMN would play a key role in belief-induced mood regulation.

And why the DMN? Well, we knew that activity in the rostral anterior cingulate, which is a key node of the DMN, is a robust predictor of mood responses to both active antidepressant and placebos, implying its involvement in nonspecific treatment response mechanisms. We also knew that the rostral anterior cingulate is a robust predicter of placebo analgesia consistent with its role in cognitive appraisals, predictions and evaluation. And we also had evidence that the SN to DMN functional connectivity appears to be a predictor of placebo and antidepressant responses over ten weeks of treatment.

And so in our clinical trial, which you can see the cartoon diagram here, we randomized six individuals to placebo or escitalopram 20 milligrams. And this table is just to say there were no significant differences between the two groups in regard to the gender, race, age, or depression severity. But what we found interesting is that there were also no significant differences in the correct belief assignment with 60% of subjects in each group approximately guessing that they were receiving escitalopram.

Now as you can see here, participants showed lower MADR scores at eight weeks in both groups. But there was no significant differences between the two groups. However, when split in the two groups by the last drug assignment belief, subjects with the drug assignment belief improved significantly compared to those with a placebo assignment belief.

And so the next question was can we use neuroimaging to predict these responses? And what we found was at a neural level during expectancy process the salience network had an increased pattern of functional connectivity with the DMN as well as with other regions of the brainstem including the thalamus. Now at the end -- we also found that increased SN to DMN functional connectivity predicted expectancy ratings during the antidepressant placebo fMRI task such that higher connectivity was associated with greater modulation of the task conditions on expectancy ratings.

Now we also found that enhanced functional connectivity between the SN and the DMN predicted the response to eight weeks of treatment, especially on individuals who believed that they were of the antidepressant group. Now this data supports that during placebo administration, greater salient attributions to the contextual cue is encoded in the salience network; whereas belief-induced mood regulation is associated with an increased functional connectivity between the SN and DMN and altogether this data suggest that enhancements to DMN connectivity enables the switch from greater salient attribution to the treatment cue to DMN-mediated mood regulation.

And so finally, and this is going to be brief, but the next question for us was can we modulate these networks to actually enhance placebo-related activity. And in particular, we decided to use theta burst stimulation which can potentiate or depotentiate brain activity in response to brief periods of stimulation. And so in this study participants undergo three counterbalance sessions of TBS with either continuous, intermittent, or sham known to depotentiate, potentiate, and have no effect.

So each TBS is followed by an fMRI session during the antidepressant placebo effect task which happens approximately an hour after stimulation. The inclusive criteria are very similar to all of our other studies. And our pattern of stimulation is pretty straightforward. We do two blocks of TBS. And during the first block stimulation intensity is gradually escalated in 5% increments in order to enhance tolerability. And during the second session the stimulation is maintained constant at 80% of the moderate threshold.

Then we use the modified cTBS session consisting of three stimuli applied at intervals of 30 hertz. We first repeat it at 6 hertz for a total of 600 stimuli in a continuous train of 33.3 seconds. Then we did the iTBS session consist of a burst of three stimuli applied at intervals of 50 hertz with bursts repeated at 5 hertz for a total of 600 stimulus during 192 seconds. We also use a sham condition where 50% of subjects are assigned to sham TBS simulating the iTBS stimulus pattern, and 50% are assigned to sham TBS simulating the cTBS pattern.

Now our target is the DMN which is the cortical target for the dorsal medial -- the cortical target for the DMN -- sorry, the dmPFC which is the cortical target for the DMN. And this corresponds to the -- and we found these effects based on the results from the antidepressant placebo fMRI task.

And so this target corresponds to our neurosynth scalp which is located 30% of the distance from the nasion-to-inion forward from the vertex and 5% left which corresponds to an EEG location of F1. And the connectivity map of these regions actually result in activation of the DMN. Now we can also show here the E-Field map of this target which basically demonstrates supports a nice coverage of the DMN.

And so what we found here is that the iTBS compared to sham and cTBS enhances the effect of the reinforcement condition of mood responses. And we also found that at a neural level iTBS compared to cTBS shows significant greater bold responses during expectancy processing within the DMN with sham responses in the middle but really not significantly different from iTBS. Now, increased bold responses in the ventral medial prefrontal cortex were associated with a greater effect of the task conditions of mood responses.

And so all together our results suggest that first trial-by-trial modulation of antidepressant expectancies effectively disassociates expectancy mood dynamics. Antidepressant expectancies are predicted by models of reinforcement learning and they're encoded in the salience network. We also showed that enhanced SN to DMN connectivity enables the switch from greater salient attribution to treatment cues to DMN-mediated mood regulation, contributing to the formation of acute expectancy-mood interactions and long-term antidepressant placebo effects. And iTBS potentiation of the DMN enhances placebo-induced mood responses and expectancy processing.

With this, I would just like to thank my collaborators that started this work with me at the University of Michigan and mostly the people in my lab and collaborators at the University of Pittsburgh as well as the funding agencies.

CRISTINA CUSIN: Wonderful presentation. Really terrific way of trying to untangle different mechanism in placebo response in depression, which is not an easy feat.

There are no specific questions in the Q&A. I would encourage everybody attending the workshop to please post your question to the Q&A. Every panelist can answer in writing. And then we will answer more questions during the discussion, but please don't hesitate.

I think I will move on to the next speaker. We have only a couple of minutes so we're just going to move on to Dr. Schmidt. Thank you so much. We can see your slides. We cannot hear you.

LIANE SCHMIDT: Can you hear me now?

CRISTINA CUSIN: Yes, thank you.

LIANE SCHMIDT: Thank you. So I'm Liane Schmidt. I'm an INSERM researcher and team leader at the Paris Brain Institute. And I'm working on placebo effects but understanding the appetitive side of placebo effects. And what I mean by that I will try to explain to you in the next couple of slides.

NIMH Staff: Can you turn on your video?

LIANE SCHMIDT: Sorry?

NIMH Staff: Can you please turn on your video, Dr. Schmidt?

LIANE SCHMIDT: Yeah, yes, yes, sorry about that.

So it's about the appetitive side of placebo effects because actually placebo effects on cognitive processes such as motivation and biases and belief updating because these processes actually play also a role when patients respond to treatment. And when we measure placebo effects, basically when placebo effects matter in the clinical setting.

And this is done at the Paris Brain Institute. And I'm working also in collaboration with the Pitie-Salpetriere Hospital Psychiatry department to get access to patients with depression, for example.

So my talk will be organized around three parts. On the first part, I will show you some data about appetitive placebo effects on taste pleasantness, hunger sensations and reward learning. And this will make the bridge to the second part where I will show you some evidence for asymmetrical learning biases that are more tied to reward learning and that could contribute actually or can emerge after fast-acting antidepressant treatment effects in depression.

And why is this important? I will try to link these two different parts, the first and second part, in the third part to elaborate some perspectives on the synergies between expectations, expectation updating through learning mechanisms, motivational mechanisms, motivational processes and drug experiences and which we can -- might harness actually by using computational models such as, for example, risk-reward Wagner models as Marta just showed you all the evidence for this in her work.

The appetitive side of placebo effects is actually known very well from the field of research in consumer psychology and marketing research where price labels, for example, or quality labels can affect decision-making processes and also experiences like taste pleasantness experience. And since we are in France, one of the most salient examples for these kind of effects comes from wine tasting. And many people have shown -- many studies have shown that basically the price of wine can influence how pleasant it tastes.

And we and other people have shown that this is mediated by activation in what is called the brain valuation systems or regions that encode expected and experienced reward. And one of the most prominent hubs in this brain valuation system is the ventral medial prefrontal cortex, what you see here on the SPM on the slide. That can explain, that basically translates these price label effects on taste pleasantness liking. And what is interesting is also that its sensitivity to monetary reward, for example, obtaining by surprise a monetary reward. It activates, basically the vmPFC activates when you obtain such a reward surprisingly.

And the more in participants who activate the vmPFC more in these kind of positive surprises, these are also the participants in which the vmPFC encoded more strongly the difference between expensive and cheap wines, which makes a nice parallel to what we know from placebo hyperalgesia where it has also been shown that the sensitivity of the reward system in the brain can moderate placebo analgesia with participants with higher reward sensitivity in the ventral striatum, for example, another region showing stronger placebo analgesia.

So this is to basically hope to let you appreciate that these effects parallel nicely what we know from placebo effects in the pain and also in disease. So we went further beyond actually, so beyond just taste liking which is basically experiencing rewards such as wine. But what could be -- could placebos also affect motivational processes per se? So when we, for example, want something more.

And one way to study is to study basic motivation such as, for example, hunger. It is long thought, for instance, eating behavior that is conceptualized to be driven by homeostatic markers, hormone markers such as Ghrelin and Leptin that signal satiety and energy stores. And as a function of these different hormonal markers in our blood, we're going to go and look for food and eat food. But we also know from the placebo effects on taste pleasantness that there is a possibility that our higher order beliefs about our internal states not our hormones can influence whether we want to eat food, whether we engage in these types of very basic motivations. And that we tested that, and other people also, that's a replication.

In the study where we gave healthy participants who came into the lab in a fasted state a glass of water. And we told them well, water sometimes can stimulate hunger by stimulating the receptors in your mouth. And sometimes you can also drink a glass of water to kill your hunger. And a third group, a control group was given a glass of water and told it's just water; it does nothing to hunger. And then we asked them to rate how hungry they feel over the course of the experiment. And it's a three-hour experiment. Everybody has fasted. And they have to do this food choice task in an fMRI scanner so they get -- everybody gets hungry over this three hours.

But what was interesting and what you see here on this rain cloud plot is that participants who believed or drank the water suggested to be a hunger killer increased in their hunger rating less than participants who believed the water will enhance their hunger. So this is a nice replication what we already know from the field; other people have shown this, too.

And the interesting thing is that it also affected this food wanting, this motivational process how much you want to eat food. So when people laid there in the fMRI scanner, they saw different food items, and they were asked whether they want to eat it or not for real at the end of the experiment. So it's incentive compatible. And what you see here is basically what we call stimulus value. So how much do you want to eat this food.

And the hunger sensation ratings that I just showed you before parallel what we find here. The people in the decreased hunger suggestion group wanted to eat the food less than in the increased hunger suggestion group, showing that it is not only an effect on subjective self-reports or how you feel your body signals about hunger. It's also about what you would actually prefer, what your subjective preference of food that is influenced by the placebo manipulation. And it's also influencing how your brain valuation system again encodes the value for your food preference. And that's what you see on this slide.

Slide two, you see the ventral medial prefrontal cortex. The yellow boxes that the more yellow they are, the stronger they correlate to your food wanting. And you see on the right side with the temporal time courses of the vmPFC that that food wanting encoding is stronger when people were on the increased hunger suggestion group than in the decreased hunger suggestion group.

So basically what I've showed you here is three placebo effects. Placebo effects on subjective hunger ratings, placebo effects on food choices, and placebo effects on how the brain encodes food preference and food choices. And you could wonder -- these are readouts. So these are behavioral readouts, neural readouts. But you could wonder what is the mechanism behind? Basically what is in between the placebo intervention here and basically the behavior feed and neural readout of this effect.

And one snippet of the answer to this question is when you look at the expectation ratings. For example, expectations have long been shown to be one of the mediators, the cognitive mediators of placebo effects across domains. And that's what we see here, too. Especially in the hunger killer suggestion group. The participants who believed that the hunger -- that the drug will kill their hunger more strongly were also those whose hunger increased less over the course of the experiment experience.

And this moderated activity in the region that you see here, which is called the medial prefrontal cortex, that basically activated when people saw food on the screen and thought about whether they want to eat it or not. And this region activated by that activity was positively moderated by the strength of the expectancy about the glass of water to decrease their hunger. So the more you expect that the water will decrease your hunger, the more the mPFC activates when you see food on the screen.

It's an interesting brain region because it's right between the ventral medial prefrontal cortex that encodes the value, the food preference, and the dorsal lateral prefrontal cortex. And it has been shown by past research to connect to the vmPFC when participants self-control, especially during food decision-making paradigms.

But another mechanism or another way to answer the question about the mechanism of how the placebo intervention can affect this behavior in neural effects is to use computational modelings to better understand the preference formation -- the preference formation basically. And one way is that -- is drift diffusion modeling. So these drift diffusion models come from perceptual research for understanding perception. And they are recently also used to better understand preference formation. And they assume that your preference for a yes or no food choice, for example, is a noisy accumulation of evidence.

And there are two types of evidence you accumulate in these two -- in these decision-making paradigms is basically how tasty and how healthy food is. How much you like the taste, how much you consider the health. And this could influence this loop of your evidence accumulation how rapidly basically you reach a threshold towards yes or no.

It could also be that the placebo and the placebo manipulation could influence this loop. But the model loops test several other hypotheses. It could be that the placebo intervention basically affected also just the threshold like that reflects how carefully you made the decision towards a yes or no choice. It could be your initial bias; that is, basically initially you were biased towards a yes or a no response. Or it could be the nondecision time which reflects more sensory motor integration.

And the answer to this question is basically that three parameters were influenced by the placebo manipulation. Basically how much you integrated healthiness and tastiness in your initial starting bias. So you paid more attention to the healthiness when you believed that you were on a hunger killer. And more the tastiness when you believed that you were on a hunger enhancer. And similarly, you were initially biased towards accepting food more when participants believed they were on a hunger enhancer than on a hunger killer.

Interestingly, so this basically shows that this decision-making process is biased by the placebo intervention and basically also how much you filter information that is most relevant. When you are hungry, basically taste is very relevant for your choices. When you are believing you are less hungry, then you have more actually space or you pay less attention to taste, but you can also pay attention more to healthiness of food.

And so the example that shows that this might be a filtering of expectation-relevant information is to use psychophysiologic interaction analyzers that look basically at the brain activity in the vmPFC, that's our seed region. Where in the brain does it connect when people, when participants see food on a computer screen and have to think about whether they want to eat this food or not?

And what we observed there that's connected to the dlPFC, the dorsal lateral prefrontal cortex region. And it's a region of interest that we localized first to be sure it is actually a region that is inter -- activating through an interference resolution basically when we filter -- have to filter information that is most relevant to a task in a separate Stroop localizer task.

So the vmPFC connects stronger to this dlPFC interference resolution region and this is moderated especially in the decreased hunger suggestion group by how much participants considered the healthiness against the tastiness of food.

To wrap this part up, it's basically that we replicated findings from previous studies about appetitive placebo effects by showing that expectancies about efficiency of a drink can affect hunger sensations. How participants make -- form their food preferences, make food choices. And value encoding in the ventral medial prefrontal cortex.

But we also provided evidence for underlying neurocognitive mechanisms that involve the medial prefrontal cortex that is moderated by the strengths of the hunger expectation. That the food choice formation is biased in the form of attention-filtering mechanism toward expectancy congruent information that is taste for an increased hunger suggestion group, and healthiness for a decreased hunger suggestion group. And this is implemented by regions that are linked to interference resolution but also to valuation preference encoding.

And so why should we care? In the real world, it is not very relevant to provide people with deceptive information about hunger-influencing ingredients of drinks. But studies like this one provide insights into cognitive mechanisms of beliefs about internal states and how these beliefs can affect the interoceptive sensations and also associated motivations such as economic choices, for example.

And this can actually also give us insights into the synergy between drug experiences and outcome expectations. And that could be harnessed via motivational processes. So translated basically via motivational processes. And then through it maybe lead us to better understand active treatment susceptibility.

And I'm going to elaborate on this in the next part of the talk by -- I'm going a little bit far, coming a little bit far, I'm not talking about or showing evidence about placebo effects. But yes -- before that, yes, so basically it is.

Links to these motivational processes have long been suggested actually to be also part of placebo effects or mechanisms of placebo effect. And that is called the placebo-reward hypothesis. And that's based on findings in Parkinson's disease that has shown that when you give Parkinson's patients a placebo but tell them it's a dopaminergic drug, then you can measure dopamine in the brain. And the dopamine -- especially the marker for dopamine, its binding potential decreases. That is what you see here on this PET screen -- PET scan results.

And that suggests that the brain must have released endogenous dopamine. And dopamine is very important for expectations and learning. Basically learning from reward. And clinical benefit is the kind of reward that patients expect. So it might -- it is possible that basically when a patient expects reward clinical benefit, its brain -- their brain releases dopamine in remodulating that region such as the vmPFC or the ventral striatum.

And we have shown this in the past that the behavioral consequence of such a nucleus dopamine release under placebo could be linked to reward learning, indeed. And what we know is that, for example, that Parkinson patients have a deficit in learning from reward when they are off dopaminergic medication. But this normalizes when they are under active dopaminergic medication.

So we wondered if based on these PET studies under placebo, the brain releases dopamine, does this also have behavior consequences on their reward learning ability. And that is what you see here on the screen on the right side on the screen is that the Parkinson patients basically tested on placebo shows similar reward learning abilities as under active drug.

And this again was also underpinned by increased correlation of the ventral medial prefrontal cortex. Again, this hub of the brain valuation system to the learned reward value. That was stronger in the placebo and active drug condition compared to baseline of drug condition.

And I want to make now this -- a link to another type of disease where also the motivation is deficitary, and which is depression. And depression is known to be maintained or is sympathized to be maintained by this triad of very negative beliefs about the world, the future and one's self. Which is very insensitive to belief disconfirming information, especially if the belief disconfirming information is positive, so favorable. And this has been shown by cognitive neuroscience studies to be reflected by a thought of like of good news/bad news bias or optimism biases and belief updating in depression. And this good news/bad news bias is basically a bias healthy people have to consider favorable information that contradicts initial negative beliefs more than negative information.

And this is healthy because it avoids reversing of beliefs. And it also includes a form of motivational process because good news have motivational salience. So it should be more motivating to update beliefs about the future, especially if these beliefs are negative, then when we learn that our beliefs are way too negative and get information about that disconfirms this initial belief. But depressed patients, they like this good news/bad news bias. So we wonder what happens when patients respond to antidepressant treatments that give immediate sensory evidence about being on an antidepressant.

And these new fast-acting antidepressants such as Ketamine, these types of antidepressants that patients know right away whether they got the treatment through dissociative experiences. And so could it be that this effect or is it a cognitive model of depression. So this was the main question of the study. And then we wondered again what is the computational mechanism. And is it linked again also, as shown in the previous studies, to reward learning mechanisms, so biased updating of beliefs. And is it linked to clinical antidepressant effects and also potentially outcome expectations makes the link to placebo effects.

So patients were given the -- were performing a belief updating task three times before receiving Ketamine infusions. And then after first infusion and then one week after the third infusion, each time, testing time we measured the depression with the Montgomery-Asberg Depression Rating Scale. And patients performed this belief updating task where they were presented with different negative life events like, for example, getting a disease, losing a wallet here, for example.

And they were asked to estimate their probability of experiencing this life event in the near future. And they were presented with evidence about the contingencies of this event in the general population, what we call the base rate. And then they had the possibility to update their belief knowing now the base rate.

And this is, for example, a good news trial where participants initially overestimated the chance for losing a wallet and then learn it's much less frequent than they initially thought. Updates, for example, 15%. And in a bad news trial, it's you initially underestimated your probability of experiencing this adverse life event. And if you have a good news/bad news bias, well, you're going to consider this information to a lesser degree than in a good news trial.

And that's what -- exactly what happens in the healthy controls that you see on the left most part of the screen. I don't know whether you can see the models, but basically we have the belief updating Y axis. And this is healthy age-matched controls to patients. And you can see updating of the good news. Updating of the bad news. We tested the participants more than two times within a week. You can see the bias. There is a bias that decreases a little bit with more sequential testing in the healthy controls. But importantly, in the patients the bias is there although before Ketamine treatment.

But it becomes much more stronger after Ketamine treatment. It emerged basically. So patients become more optimistically biased after Ketamine treatment. And this correlates to the MADRS scores. Patients who improve more with treatment are also those who show a stronger good news/bad news bias after one week of treatment.

And we wondered again about the computational mechanisms. So one way to get at this using a Rescoria-Wagner model reward reinforcement learning model that basically assumes that updating is proportional to your surprise which is called the estimation error.

The difference between the initial estimate and the base rate. And this is weighted by learning rate. And the important thing here is the learning rate has got two components, a scaling parameter and an asymmetry parameter. And the asymmetry parameter basically weighs in how much the learning rate varies after good news, after positive estimation error, than after negative estimation errors.

And what we can see that in healthy controls, there is a stronger learning rate for positive estimation errors and less stronger for negative estimation errors translating this good news/bad news bias. It's basically an asymmetrical learning mechanism. And in the patients, the asymmetrical learning is non-asymmetrical before Ketamine treatment. And it becomes asymmetrical as reflected in the learning rates after Ketamine treatment.

So what we take from that is basically that Ketamine induced an optimism bias. But an interesting question is whether -- basically what comes first. Is it basically the improvement in the depression that we measured with the Montgomery-Asberg Depression Rating Scale, or is it the optimism bias that emerged and that triggered basically. Since it's a correlation, we don't know what comes first.

And an interesting side effect or aside we put in the supplement was that in 16 patients, it's a very low sample size, the expectancy about getting better also correlated to the clinical improvement after Ketamine treatment. We have two expectancy ratings here about the efficiency about Ketamine and also what patients expect their intensity of depression will be after Ketamine treatment.

And so that suggested the clinical benefit is kind of in part or synergistically seems to interact with the drug experience that emerges that generates an optimism bias. And to test this more, we continued data collection just on the expectancy ratings. And basically wondered how the clinical improvement after first infusion links to the clinical improvement after third infusion.

And we know from here that patients improve after first infusion are also those that improved after a third infusion. But is it mediated by their expectancy about the Ketamine treatment? And that's what we indeed found is that basically the more patients expected to get better, the more they got better after one week of treatment. But it mediated this link between the first drug experience and the later drug experiences and suggested there might not be an additive effect as other panelist members today already put forward today, it might be synergetic link.

And one way to get at these synergies is basically again use computational models. And this idea has been around although yesterday that basically there could be self-fulfilling prophesies that could contribute to the treatment responsiveness and treatment adherence. And these self-fulfilling prophesies are biased symmetrically learning mechanisms that are more biased when you have positive treatment experiences, initial positive treatment experiences, and then might contribute how you adhere to the treatment in the long term and also how much you benefit from it in the long term. So it's both drug experience and an expectancy.

And so this is nonpublished work where we played with this idea basically using a reinforcement learning model. This is also very inspired by we know from placebo analgesia. Tor and Luana Kuven, they have a paper on showing that self-fulfilling prophecies can be harnessed with biased patient and reinforcement learning models. And the idea of these models is that there are two learning rates, alpha plus and alpha minus. And these learning rates rate differently into the updating of your expectation after drug experience.

LIANE SCHMIDT: Okay, yeah, I'm almost done.

So rate differently on these drug experiences and expectations as a function of whether the initial experience was congruent to your expectations. So a positive experience, then a negative one. And here are some simulations of this model. I'm showing this basically that your expectation is getting more updated the more bias, positively biased you are. Then when you are negatively biased. And these are some predictions of the model concerning depression improvement.

To wrap this up, the conclusion about this is that there seems to be asymmetrical learning that can capture self-fulfilling prophesies and could be a mechanism that translates expectations and drug experiences potentially across domains from placebo hypoalgesia to antidepressant treatment responsiveness. And the open question is obviously to challenge these predictions of these models more with empirical data in pain but also in mood disorders as Marta does and as we do also currently at Cypitria where we test the mechanisms of belief updating biases in depression with fMRI and these mathematical models.

And this has a direct link implication because it could help us to better understand how these fast-acting antidepressants work and what makes patients adhere to them and get responses to them. Thank you for your attention. We are the control-interoception-attention team. And thank you to all the funders.

CRISTINA CUSIN: Fantastic presentation. Thank you so much. Without further ado, let's move on to the next speaker. Dr. Greg Corder.

GREG CORDER: Did that work? Is it showing?

GREG CORDER: Awesome, all right. One second. Let me just move this other screen. Perfect. All right.

Hi, everyone. My name is Greg Corder. I'm an Assistant Professor at the University of Pennsylvania. I guess I get to be the final scientific speaker in this session over what has been an amazing two-day event. So thank you to the organizers for also having me get the honor of representing the entire field of preclinical placebo research as well.

And so I'm going to give a bit of an overview, some of my friends and colleagues over the last few years and then tell you a bit about how we're leveraging a lot of current neuroscience technologies to really identify the cell types and circuits building from, you know, the human fMRI literature that's really honed in on these key circuits for expectations, belief systems as well as endogenous antinociceptive symptoms, in particular opioid cell types.

So the work I'm going to show from my lab has really been driven by these two amazing scientists. Dr. Blake Kimmey, an amazing post-doc in the lab. As well as Lindsay Ejoh, who recently last week just received her D-SPAN F99/K00 on placebo circuitry. And we think this might be one of the first NIH-funded animal projects on placebo. So congratulations, Lindsay, if you are listening.

Okay. So why use animals, right? We've heard an amazing set of stories really nailing down the specific circuits in humans leveraging MRI, fMRI, EEG and PET imaging that give us this really nice roadmap and idea of how beliefs in analgesia might be encoded within different brain circuits and how those might change over times with different types of patient modeling or updating of different experiences.

And we love this literature. We -- in the lab we read it in depth as best as we can. And we use this as a roadmap in our animal studies because we can take advantage of animal models that really allow us to dive deep into the very specific circuits using techniques like that on the screen here from RNA sequencing, electrophysiology really showing that those functional measurements in fMRI are truly existent with the axons projecting from one region to another.

And then we can manipulate those connections and projections using things like optogenetics and chemogenetics that allow us really tight temporal coupling to turn cells on and off. And we can see the effects of that intervention in real time on animal behavior. And that's really the tricky part is we don't get to ask the animals do you feel pain? Do you feel less pain? It's hard to give verbal suggestions to animals.

And so we have to rely on a lot of different tricks and really get into the heads of what it's like to be a small prey animal existing in a world with a lot of large monster human beings around them. So we really have to be very careful about how we design our experiments. And it's hard. Placebo in animals is not an easy subject to get into. And this is reflected in the fact that as far as we can tell, there is only 24 published studies to date on placebo analgesia in animal models.

However, I think this is an excellent opportunity now to really take advantage of what has been the golden age of neuroscience technologies exploding in the last 10-15 years to revisit a lot of these open questions about when are opioids released, are they released? Can animals have expectations? Can they have something like a belief structure and violations of those expectations that lead to different types of predictions errors that can be encoded in different neural circuits. So we have a chance to really do that.

But I think the most critical first thing is how do we begin to behaviorally model placebo in these preclinical models. So I want to touch on a couple of things from some of my colleagues. So on the left here, this is a graph that has been shown on several different presentations over the past two days from Benedetti using these tourniquet pain models where you can provide pharmacological conditioning with an analgesic drug like morphine to increase this pain tolerance.

And then if it is covertly switched out for saline, you can see that there is an elevation in that pain tolerance reflective of something like a placebo analgesic response overall. And this is sensitive to Naloxone, the new opioid receptor antagonist, suggesting endogenous opioids are indeed involved in this type of a placebo-like response.

And my colleague, Dr. Matt Banghart, at UCSD has basically done a fantastic job of recapitulating this exact model in mice where you can basically use morphine and other analgesics to condition them. And so if I just kind of dive in a little bit into Matt's model here.

You can have a mouse that will sit on a noxious hot plate. You know, it's an environment that's unpleasant. You can have contextual cues like different types of patterns on the wall. And you can test the pain behavior responses like how much does the animal flick and flinch and lick and bite and protect itself to the noxious hot plate.

And then you can switch the contextual cues, provide an analgesic drug like morphine, see reductions in those pain behaviors. And then do the same thing in the Benedetti studies, you switch out the morphine for saline, but you keep the contextual cues. So the animal has effectively created a belief that when I am in this environment, when I'm in this doctor's office, I'm going to receive something that is going to reduce my perceptions of pain.

And, indeed, Matt sees a quite robust effect here where this sort of placebo response is -- shows this elevated paw withdrawal latency indicating that there is endogenous nociception occurring with this protocol. And it happens, again, pretty robustly. I mean most of the animals going through this conditioning protocol demonstrate this type of antinociceptive behavioral response. This is a perfect example of how we can leverage what we learn from human studies into rodent studies for acute pain.

And this is also really great to probe the effects of placebo in chronic neuropathic pain models. And so here this is Dr. Damien Boorman who was with Professor Kevin Key in Australia, now with Lauren Martin in Toronto.

And here Damien really amped up the contextual cues here. So this is an animal who has had an injury to the sciatic nerve with this chronic constriction injury. So now this animal is experiencing something like a tonic chronic neuropathic pain state. And then once you let the pain develop, you can have the animals enter into this sort of placebo pharmacological conditioning paradigm where animals will go onto these thermal plates, either hot or cool, in these rooms that have a large amount of visual tactile as well as odorant cues. And they are paired with either morphine or a controlled saline.

Again, the morphine is switched for saline on that last day. And what Damien has observed is that in a subset of the animals, about 30%, you can have these responder populations that show decreased pain behavior which we interpret as something like analgesia overall. So overall you can use these types of pharmacological conditionings for both acute and chronic pain.

So now what we're going to do in our lab is a bit different. And I'm really curious to hear the field's thoughts because all -- everything I'm about to show is completely unpublished. Here we're going to use an experimenter-free, drug-free paradigm of instrumental conditioning to instill something like a placebo effect.

And so this is what Blake and Lindsay have been working on since about 2020. And this is our setup in one of our behavior rooms here. Our apparatus is this tiny little device down here. And everything else are all the computers and optogenetics and calcium imaging techniques that we use to record the activity of what's going on inside the mouse's brain.

But simply, this is just two hot plates that we can control the temperature of. And we allow a mouse to freely explore this apparatus. And we can with a series of cameras and tracking devices plot the place preference of an animal within the apparatus. And we can also record with high speed videography these highly conserved sort of protective recuperative pain-like behaviors that we think are indicative of the negative affect of pain.

So let me walk you through our little model here real quick. Okay. So we call this the placebo analgesia conditioning assay or PAC assay. So here is our two-plate apparatus here. So plate number one, plate number two. And the animal can always explore whichever plate it wants. It's never restricted to one side. And so we have a habituation day, let the animal familiarize itself. Like oh, this is a nice office, I don't know what's about to happen.

And then we have a pretest. And in this pretest, importantly, we make both of these plates, both environments a noxious 45-degree centigrade. So this will allow the animal to form an initial expectation that the entire environment is noxious and it's going to hurt. So both sides are noxious. Then for our conditioning, this is where we actually make one side of the chamber non-noxious. So it's just room temperature. But we keep one side noxious. So now there is a new expectation for the animal that it learns that it can instrumentally move its body from one side to the other side to avoid and escape feeling pain.

And so we'll do this over three days, twice per day. And then on our post tester placebo day we make both environments hot again. So now we'll start the animal off over here and the animals will get to freely choose do they want to go to the side that they expect should be non-noxious? Or what happens? So what happens?

Actually, if you just look at the place preference for this, over the course of conditioning we can see that the animals will, unsurprisingly, choose the environment that is non-noxious. And they spend 100% of their time there basically. But when we flip the plates or flip the conditions such that everything is noxious on the post test day, the animals will still spend a significant amount of time on the expected analgesia side. So I'm going to show you some videos here now and you are all going to become mouse pain behavior experts by the end of this.

So what I'm going to show you are both side by side examples of conditioned and unconditioned animals. And try to follow along with me as you can see what the effect looks like. So on this post test day. Oh, gosh, let's see if this is going to -- here we go. All right. So on the top we have the control animal running back and forth. The bottom is our conditioned animal.

And you'll notice we start the animal over here and it's going to go to the side that it expects it to not hurt. Notice the posture of the animals. This animal is sitting very calm. It's putting its entire body down on the hot plate. This animal, posture up, tail up. It's running around a little bit frantically. You'll notice it start to lick and bite and shake its paws. This animal down here might have a couple of flinches so it's letting you know that some nociception is getting into the nervous system overall.

But over the course of this three-minute test, the animals will rightly choose to spend more time over here. And if we start to quantify these types of behaviors that the animals are doing in both conditions, what we find is that there is actually a pretty significant reduction in these nociceptive behaviors. But it's not across the entire duration of this placebo day or post test day.

So this trial is three minutes long. And what we see is that this antinociceptive and preference choice only exists for about the first 90 seconds of this assay. So this is when the video I just showed, the animal goes to the placebo side, it spends a lot of its time there, does not seem to be displaying pain-like behaviors.

And then around 90 seconds, the animal -- it's like -- it's almost like the belief or the expectation breaks. And at some point, the animal realizes oh, no, this is actually quite hot. It starts to then run around and starts to show some of the more typical nociceptive-like behaviors. And we really like this design because this is really, really amenable to doing different types of calcium imaging, electrophysiology, optogenetics because now we have a really tight timeline that we can observe the changing of neural dynamics at speeds that we can correlate with some type of behavior.

Okay. So what are those circuits that we're interested in overall that could be related to this form of placebo? Again, we like to use the human findings as a wonderful roadmap. And Tor has demonstrated, and many other people have demonstrated this interconnected distributed network involving prefrontal cortex, nucleus accumbens, insula, thalamus, as well as the periaqueductal gray.

And so today I'm going to talk about just the periaqueductal gray. Because there is evidence that there is also release of endogenous opioids within this system here. And so we tend to think that the placebo process and the encoding, whatever that is, the placebo itself is likely not encoded in the PAG. The PAG is kind of the end of the road. It's the thing that gets turned on during placebo and we think is driving the antinociceptive or analgesic effects of the placebo itself.

So the PAG, for anyone who's not as familiar, we like it because it's conserved across species. We look at in a mouse. There's one in a human. So potentially it's really good for translational studies as well. It has a very storied past where it's been demonstrated that the PAG subarchitecture has these beautiful anterior to posterior columns that if you electrically stimulate different parts of PAG, you can produce active versus passive coping mechanisms as well as analgesia that's dependent on opioids as well as endocannabinoids.

And then the PAG is highly connected. Both from ascending nociception from the spinal cord as well as descending control systems from prefrontal cortex as well as the amygdala. So with regard to opioid analgesia. If you micro infuse morphine into the posterior part of the PAG, you can produce an analgesic effect in rodents that is across the entire body. So it's super robust analgesia from this very specific part of the PAG.

If you look at the PAG back there and you do some of these techniques to look for histological indications that the mu opioid receptor is there, it is indeed there. There is a large amount of mu opioid receptors, it's OPRM1. And it's largely on glutamatergic neurons. So the excitatory cells, not the inhibitory cells. They are on some of them.

And as far as E-phys data goes as well, we can see that the mu opioid receptor is there. So DAMGOs and opioid agonist. We can see activation of inhibitory GIRK currents in those cells. So the system is wired up for placebo analgesia to happen in that location. Okay. So how are we actually going to start to tease this out? By finding these cells where they go throughout the brain and then understanding their dynamics during placebo analgesia.

So last year we teamed up with Karl Deisseroth's lab at Stanford to develop a new toolkit that leverages the genetics of the opioid system, in particular the promoter for the mu opioid receptor. And we were able to take the genetic sequence for this promoter and package it into adeno associated viruses along with a range of different tools that allow us to turn on or turn off cells or record their activity. And so we can use this mu opioid receptor promoter to gain genetic access throughout the brain or the nervous system for where the mu opioid receptors are. And we can do so with high fidelity.

This is just an example of our mu opioid virus in the central amygdala which is a highly mu opioid specific area. But so Blake used this tool using the promoter to drive a range of different trans genes within the periaqueductal gray. And right here, this is the G camp. So this is a calcium indicator that allows us to in real time assess the calcium activity of PAG mu opioid cells.

And so what Blake did was he took a mouse, and he recorded the nociceptive responses within that cell type and found that the mu opioid cell types are actually nociceptive. They respond to pain, and they do so with increasing activity to stronger and stronger and more salient and intense noxious stimuli. So these cells are actually nociceptive.

And if we look at a ramping hot plate, we can see that those same mu opioid cell types in the PAG increase the activity as this temperature on this hot plate increases. Those cells can decrease that activity if we infuse morphine.

Unsurprisingly, they express the mu opioid receptor and they're indeed sensitive to morphine. If we give naltrexone to block the mu opioid receptors, we can see greater activity to the noxious stimuli, suggesting that there could be an opioid tone or some type of an endogenous opioid system that's keeping this system in check, that it's repressing its activity. So when we block it, we actually enhance that activity. So it's going to be really important here. The activity of these mu opioid PAG cells correlates with affective measures of pain.

When animals are licking, shaking, biting, when it wants to escape away from noxious stimuli, that's when we see activity within those cells. So this is just correlating different types of behavior when we see peak amplitudes within those cell types. So let me skip that real quick.

Okay. So we have this ability to look and peek into the activity of mu opioid cell types. Let's go back to that placebo assay, our PAC assay I mentioned before. If we record from the PAG on that post test day in an animal that has not undergone conditioning, when the plates are super hot, we see a lot of nocioceptive activity in these cells here. They're bouncing up and down.

But if we look at the activity of the nociception in an animal undergoing placebo, what we see is there's a suppression of neural activity within that first 90 seconds. And this actually does seem to extinguish within the lighter 90 seconds. So kind of tracks along with the behavior of those animals. When they're showing anti nocioceptive behavior, that's when those cells are quiet.

When the pain behavior comes back, that's when those cell types are ramping up. But what about the opioids too? Mu opioid receptor cell type's decreasing activity. What about the opioids themselves here? The way to do this in animals has been to use microdialysis, fantastic technique but it's got some limitations to it. This is a way of sampling peptides in real time and then using liquid chromatography to tell if the protein was present. However, the sampling rate is about 10 minutes.

And in terms of the brain processing, 10 minutes might as well be an eternity. If we're talking about milliseconds here. But we want to know what these cells here and these red dots are doing. These are the enkephaliner cells in the PAG. We needed revolution in technologies. One of those came several years ago from Dr. Lin Tian, who developed some of the first sensors for dopamine. Some of you may have heard of it. It's called D-Light.

This is a version of D-Light. But it's actually an enkephalin opioid sensor. What Lin did to genetically engineer this is to take the delta opioid receptor, highly select it for enkephalin, and then link it with this GFP molecule here such that when enkephalin binds to the sensor it will fluoresce.

We can capture that florescence with microscopes that we implant over the PAG and we can see when enkephalin is being released with subsecond resolution. And so what we did for that is we want to see if enkephalin is indeed being released onto those mu opioid receptor expressing pain encoding neurons in the PAG. What I showed you before is that those PAG neurons, they ramp up their activity as the nociception increases, a mouse standing on a hot plate. We see nociception ramp up. What do you all think happened with the opoids?

It wasn't what we expected. It actually drops. So what we can tell is that there's a basal opioid tone within the PAG, but that as nociception increases, acute nociception, we see a decrease suppression of opioid peptide release.

We think this has to do with stuff that Tor has published on previously that the PAG is more likely involved in updating prediction errors. And this acute pain phenomenon we think is reflective of the need to experience pain to update your priors about feeling pain and to bias the selection of the appropriate behaviors, like affect related things to avoid pain. However, what happens in our placebo assay?

We actually see the opposite. So if we condition animals to expect pain relief within that PAC assay, we actually see an increase from the deltoid sensor suggesting that there is an increase in enkephalin release post conditioning. So there can be differential control of the opioid system within this brain region. So this next part is the fun thing you can do with animals. What if we just bypassed the need to do the placebo assay?

If we know that we just need to cause release of enkephalin within the PAG to produce pain relief, we could just directly do that with optigenetics. So we tried to us this animal that allows us to put a red light sensitive opsin protein into the enkephalinergic interneurons into the PAG.

When we shine red light on top of these cells, they turn on and they start to release their neurotransmitters. These are GABAergic and enkephalinergic. So they're dumping out GABA and now dumping out enkephalin into the ERG. We can visualize that using the Delta Light sensor from Lin Tien.

So here is an example of optogenetically released enkephalin within the PAG over 10 minutes. The weird thing that we still don't fully understand is that this signal continues after the optogenetic stimulation. So can we harness the placebo effect in mice? At least it seems we can. So if we turn on these cells strongly, cause them to release enkephalin and put animals back on these ramping hot plate tests we don't see any changes in the latency to detect pain, but we see specific ablation or reductions in these affective motivational pain like behaviors overall. Moderator: You have one minute remaining.

GREGORY CORDER: Cool. In this last minutes, people are skeptical. Can we actually test these higher order cognitive processes in animals? And for anyone who is not a behavioral preclinical neural scientist, you might not be aware there's an absolute revolution happening in behavior with the use of deep learning modules that can precisely and accurately quantify animal behavior. So this is an example of a deep learning tracking system.

We've built the Light Automated Pain Evaluator that can capture a range of different pain related behaviors fully automated without human intervention whatsoever that can be paired with brain reporting techniques like calcium imaging, that allow us to fit a lot of different computational models to understand what the activity of single neurons might be doing, let's say, in the cingulate cortex that might be driving that placebo response.

We can really start to tie now in at single cell resolution the activity of prefrontal cortex to drive these placebo effects and see if that alters anti nocioceptive behavior endogenously. I'll stop there and thank all the amazing people, Blake, Greg, and Lindsay, who did this work, as well as all of our funders and the numerous collaborators who have helped us do this. So thank you.

CRISTINA CUSIN: Terrific talk. Thank you so much. We're blown away. I'll leave the discussion to our two moderators. They're going to gather some of the questions from the chat and some of their own questions for all the presenters from today and from yesterday as well.

TED KAPTCHUK: Matt, you start gathering questions. I got permission to say a few moments of comments. I wanted to say this is fantastic. I actually learned an amazing amount of things. The amount of light that was brought forward about what we know about placebos and how we can possibly control placebo effects, how we can possibly harness placebo effects.

There was so much light and new information. What I want to do in my four minutes of comments is look to the future. What I mean by that is -- I want to give my comments and you can take them or leave them but I've got a few minutes.

What I want to say is we got the light, but we didn't put them together. There's no way we could have. We needed to be more in the same room. How does this fit in with your model? It's hard to do. What I mean by putting things together is I'll give you an example. In terms of how do we control placebo effects in clinical trials. I not infrequently get asked by the pharmaceutical industry, when you look at our placebo data -- we just blew it. Placebo was good as or always as good as the drug.

And the first thing I say is I want to talk to experts in that disease. I want to know the natural history. I want to know how you made your entry criteria so I can understand regression to the mean.

I want to know what's the relationship of the objective markers and subjective markers so I can begin to think about how much is the placebo response. I always tell them I don't know. If I knew how to reduce -- increase the difference between drug and placebo I'd be a rich man, I wouldn't be an academic. What I usually wind up saying is, get a new drug. And they pay me pretty well for that. And the reason is that they don't know anything about natural history. We're trying to harness something, and I just want to say -- I've done a lot of natural history controls, and that's more interesting than the rest of the experiments because they're unbelievable, the amount of improvement people show entering the trial without any treatment.

I just want to say we need to look at other things besides the placebo effect. We want to control the placebo response in a randomized control trial. I want to say that going forward. But I also want to say that we need a little bit of darkness. We need to be able to say, you know, I disagree with you. I think this other data, and one of the things I've learned doing placebo reach there's a paper that contradicts your paper real quickly and there's lots of contradictory information. It's very easy to say you're wrong, and we don't say it enough.

I want to take one example -- please forgive me -- I know that my research could be said that, Ted, you're wrong. But I just want to say something. Consistently in the two days of talk everyone talks about the increase of the placebo response over time. No one refers to the article published in 2022 in BMJ, first author was Mark Stone and senior author was Irving Kirsch. And they analyzed all FDA Mark Stone is in the Division of Psychiatry at CDER at the FDA. They analyzed all data of placebo controlled trials in major depressive disorder. They had over 230 trials, way more than 70,000 patients, and they analyzed the trend over time, in 1979 to the present, the publication. There was no increase in the placebo effect.

Are they right or are other people right? Nothing is one hundred percent clear right now and we need to be able to contradict each other when we get together personally and say, I don't think that's right, maybe that's right. I think that would help us. And the last thing I want to say is that some things were missing from the conference that we need to include in the future. We need to have ethics. Placebo is about ethics. If you're a placebo researcher in placebo controlled trials, that's an important question:

What are we talking about in terms of compromising ethics? There's no discussion that we didn't have time but in the future, let's do that.

And the last thing I would say is, we need to ask patients what their experience is. I've got to say I've been around for a long time. But the first time I started asking patients what their experiences were, they were in double blind placebo or open label placebo, I did it way after they finished the trial, the trial was over, and I actually took notes and went back and talked to people. They told me things I didn't even know about. And we need to have that in conferences. What I want to say, along those lines, is I feel so much healthier because I'm an older person, and I feel with this younger crowd here is significantly younger than me.

Maybe Matt and I are the same age, I don't know, but I think this is really one of the best conferences I ever went to. It was real clear data. We need to do lots of other things in the future. So with that, Matt, feed me some questions.

MATTHEW RUDORFER: Okay. Thanks. I didn't realize you were also 35. But okay. [LAUGHTER].

MATTHEW RUDORFER: I'll start off with a question of mine. The recent emergence of intravenous ketamine for resistant depression has introduced an interesting methodologic approach that we have not seen in a long time and that is the active placebo. So where the early trials just used saline, more recently we have seen benzodiazapine midazolam, while not mimicking really the full dissociative effect that many people get from ketamine, but the idea is for people to feel something, some kind of buzz so that they might believe that they're on some active compound and not just saline. And I wonder if the panel has any thoughts about the merits of using an active placebo and is that something that the field should be looking into more?

TED KAPTCHUK: I'm going to say something. Irving Kirsch published a meta analysis of H studies that used atropine as a control in depression studies. He felt that it made it difficult to detect a placebo drug difference. But in other meta analysis said that was not true. That was common in the '80s. People started thinking about that. But I have no idea how to answer your question.

MICHAEL DETKE: I think that's a great question. And I think in the presentations yesterday about devices, Dr. Lisanby was talking about the ideal sham. And I think it's very similar, the ideal active placebo would have none of the axia of the drug, of the drug in question, but would have, you know, exactly the same side effects and all other features, and of course that's attractive, but of course we probably would never have a drug that's exactly like that. I think midazolam was a great thing to try with ketamine. It's still not exactly the same. But I'd also add that it's not black and white. It's not like we need to do this with ketamine and ignore it for all of our other drugs. All of our drugs have side effects.

Arguably, if you do really big chunks, like classes of relatively modern antidepressants, antipsychotics and the psychostimulants, those are in order of bigger effect sizes in clinical trials, psychostimulants versus anti psychotics, versus -- and they're also in the order of roughly, I would argue, of unblinding, of functional unblinding. And in terms of more magnitude, Zyprexa will make you hungry. And also speed of onset of some of the adverse effects, stimulants and some of the Type II -- the second generation and beyond -- anti psychotics, they have pretty noticeable side effects for many subjects and relatively rapidly. So I think those are all important features to consider.

CRISTINA CUSIN: Dr. Schmidt?

LIANE SCHMIDT: I think using midazolam could give, like, some sensory sensations so the patients actually can say there's some effect on the body like immediately. But this raises actually a question whether these dissociations we observe in some patients of ketamine infusions we know have, will play a role for the antidepressant response. It's still an open question. So I don't have the answer to that question. And I think with midazolam doesn't really induce dissociations. I don't know, maybe you can isolate the dissociations you get on ketamine. But maybe even patients might be educated, expecting scientific reaction experiences and basically when they don't have -- so they make the midazolam experience something negative. So yeah, just self fulfilling prophesies might come into play.

CRISTINA CUSIN: I want to add for five seconds. Because I ran a large ketamine clinic. We know very little about cyto placebo maintaining an antidepressant response while the dissociation often wears off over time. It's completely separate from the anti depressant effect. We don't have long term placebo studies. The studies are extremely short lived and we study the acute effect. But we don't know how to sustain or how to maintain, what's the role of placebo effect in long term treatments. So that's another field that really is open to investigations. Dr. Rief.

WINFRIED RIEF: Following up on the issue of active placebos. I just want to mention that we did a study comparing active placebos to passive placebos and showing that active placebos are really more powerful. And I think the really disappointing part of this news is that it questions the blinding of our typical RCTs comparing antidepressants versus placebos because many patients who are in the active group or the tracked group, they perceive these onset effects and this will further boost the placebo mechanisms in the track group that are not existing in the passive placebo group. This is a challenge that further questions the validity of our typical RCTs.

CRISTINA CUSIN: Marta.

MARTA PECINA : Just a quick follow up to what Cristina was saying, too, that we need to clarify whether we want to find an active control for the dissociative effects or for the antidepressive effects. I think the approach will be very different. And this applies to ketamine but also psychodelics because we're having this discussion as well. So when thinking about how to control for or how to blind or how we just -- these treatments are very complicated. They have multiple effects. We just need to have the discussion of what are we trying to blind because the mechanism of action of the blinding drug will be very different.

TED KAPTCHUK: Can I say something about blinding? Robertson, who is the author of the 1970 -- no -- 1993 New England Journal paper saying that there's no that the placebo effect is a myth.

In 2022, published in BMJ, the largest -- he called it a mega meta analysis on blinding. And he took 144 randomized control trials that included nonblinded evidence on the drug versus blinded evidence of the drug. I'm not going to tell you the conclusion because it's unbelievable. But you should read it because it really influences -- it would influence what we think about blinding. That study was just recently replicated on a different set of patients with procedures in JAMA Surgery three months ago. And blinding like placebo is more complicated than we think. That's what I wanted to say.

MATTHEW RUDORFER: Another clinical factor that's come up during our discussion has been the relationship of the patient to the provider that we saw data showing that a warm relationship seemed to enhance therapeutic response, I believe, to most interventions. And I wonder what the panel thinks about the rise on the one hand of shortened clinical visits now that, for example, antidepressants are mostly given by busy primary care physicians and not specialists and the so called med check is a really, kind of, quickie visit, and especially since the pandemic, the rise of telehealth where a person might not ever even meet their provider in person, and is it possible we're on our way to where a clinical trial could involve, say, mailing medication every week to a patient, having them do their weekly ratings online and eliminating a provider altogether and just looking at the pharmacologic effect?

I mean, that probably isn't how we want to actually treat people clinically, but in terms of research, say, early phase efficacy, is there merit to that kind of approach?

LUANA COLLOCA: I'll comment on this, Dr. Rudorfer. We're very interested to see how the telemedicine or virtual reality can affect placebo effects, and we're modeling in the lab placebo effects induced via, you know, in person interaction.

There's an Avatar and virtual reality. And actually we found placebo effects with both the settings. Or whether, when we look at empathy, the Avatar doesn't elicit any empathy in the relationship. We truly need the in person connection to have empathy. So that suggests that our outcome that are affected by having in person versus telemedicine/para remote interactions, but yet the placebo effects persist in both the settings. The empathy is differently modulated and the empathy mediated, interestingly in our data, placebo effects only in the in person interactions. There is still a value in telemedicine. Effects that bypass empathy completely in competence.

MATTHEW RUDORFER: Dr. Hall.

KATHRYN HALL: Several of the large studies, like the Women's Health Study, Physicians' Health Study and, more recently, Vital, they did exactly that, where they mail these pill packs. And I mean, the population, obviously, is clinicians. So they are very well trained and well behaved. And they follow them for years but there's very little contact with the providers, and you still have these giant -- I don't know if you can call them placebo effects -- but certainly many of these trials have not proven to be more effective, the drugs they're studying, than placebo.

MATTHEW RUDORFER: Dr. Atlas.

LAUREN ATLAS: I wanted to chime in briefly on this important question. I think that the data that was presented yesterday in terms of first impressions of providers is relevant for this because it suggests that even when we use things like soft dot (phonetic) to select physicians and we have head shots (phonetic), that really we're making these decisions about who to see based on these kinds of just first impressions and facial features and having the actual interactions by providers is critical for sort of getting beyond that kind of factor that may drive selection. So I think if we have situations where there's reduced chances to interact, first of all, people are bringing expectations to the table based on what they know about the provider and then you don't really have the chance to build on that without the actual kind of therapeutic alliance. That's why I think, even though our study was done in an artificial setting, it really does show how we make these choices when there are bios for physicians and things available for patients to select from. I think there's a really important expectation being brought to the table before the treatment even occurs.

MATTHEW RUDORFER: Thanks. Dr. Lisanby.

SARAH “HOLLY” LISANBY: Thanks for raising this great question, Matt. I have a little bit of a different take on it. Equity in access to mental health care is a challenge. And the more that we can leverage technology to provide and extend the reach of mental health care the better. And so telemedicine and telepsychiatry, we've been thrust into this era by the pandemic but it existed before the pandemic as well. And it's not just about telepsychotherapy or teleprescription from monitoring pharmacotherapy, but digital remote neuromodulation is also a thing now. There are neuromodulation interventions that can be done at home that are being studied, and so there have been trials on transcranial direct current stimulation at home with remote monitoring. There are challenges in those studies differentiating between active and sham. But I think you're right in that we may have to rethink how do we control remote studies when the intensity of the clinician contact is very different, but I do think that we should explore these technologies so that we can extend the reach and extend access to research and to care for people who are not able to come into the research lab setting.

TED KAPTCHUK: May I add something on this? It's also criticizing myself. In 2008, I did this very nice study showing you could increase the doctor/patient relationship. And as you increase it, the placebo effect got bigger and bigger, like a dose response. A team in Korea that I worked with replicated that. I just published that replication.

The replication came out with the exact opposite results. The less doctor/patient relationship, the less intrusive, the less empathy got better effects. We're dealing with very complicated culturally constructed issues, and I just want to put it out there, the sand is soft. I'm really glad that somebody contradicted a major study that I did.

LUANA COLLOCA: Exactly. The central conference is so critical, what we observed in one context in one country, but even within the same in group or out group can be completely different in Japan, China or somewhere else. So the Americas, South Africa. So we need larger studies and more across country collaborations.

MATTHEW RUDORFER: Dr. Schmidt.

LIANE SCHMIDT: I just wanted to raise a point not really like -- it's more like a comment, like there's also very interesting research going on in the interactions between humans and robots, and usually humans treat robots very badly. And so I wonder what could be like -- here we focus on very human traits, like empathy, competence, what we look at. But when it comes to artificial intelligence, for example, and when we have to interact with algorithms, basically, like all these social interactions might completely turn out completely different, actually, and all have different effects on placebo effects. Just a thought.

MATTHEW RUDORFER: Dr. Rief.

WINFRIED RIEF: Yesterday, I expressed a belief for showing more warmth and competence, but I'll modify it a little bit today because I think the real truth became quite visible today, and that is that there is an interaction between these non specific effect placebo effects and the track effect. In many cases, at least. We don't know whether there are exceptions from this rule, but in many cases we have an interaction. And to learn about the interaction, we instead need study designs that modulate track intake versus placebo intake, but they also modulate the placebo mechanisms, the expectation mechanisms, the context of the treatment. And only if we have these 2 by 2 designs, modulating track intake and modulating context and psychological factors, then we learn about the interaction. You cannot learn about the interaction if you modulate only one factor.

And, therefore, I think what Luana and others have said that interact can be quite powerful and effective in one context but maybe even misleading in another context. I think this is proven. We have to learn more about that. And all the studies that have been shown from basic science to application that there could be an interaction, they're all indicating this line and to this necessity that we use more complex designs to learn about the interaction.

MATTHEW RUDORFER: Yes. And the rodent studies we've seen, I think, have a powerful message for us just in terms of being able to control a lot of variables that are just totally beyond our control in our usual human studies. It always seemed to me, for example, if you're doing just an antidepressant versus placebo trial in patients, well, for some people going into the clinic once a week to get ratings, that might be the only day of the week that they get up and take a shower, get dressed, have somebody ask them how they're doing, have some human interaction. And so showing up for your Hamilton rating could be a therapeutic intervention that, of course, we usually don't account for in the pharmacotherapy trial. And the number of variables really can escalate in a hurry when we look at our trials closely.

TED KAPTCHUK: Tor wants to say something.

TOR WAGER: Thanks, Ted.

I wanted to add on to the interaction issue, which came up yesterday, which Winfried and others just commented on, because it seems like it's really a crux issue. If the psychosocial or expectation effects and other things like that are entangled with specific effects so that one can influence the other and they might interact, then, yeah, we need more studies that independently manipulate specific drug or device effects and other kinds of psychological effects independently. And I wanted to bring this back up again because this is an idea that's been out here for a long time. I think the first review on this was in the '70s, like '76 or something, and it hasn't really been picked up for a couple of reasons. One, it's hard to do the studies. But second, when I talk to people who are in industry and pharma, they are very concerned about changing the study designs at all for FDA approval.

And since we had some, you know, FDA and regulatory perspectives here yesterday, I wanted to bring that up and see what people think, because I think that's been a big obstacle. And if it is, then that may be something that would be great for NIH to fund instead of pharma companies because then there's a whole space of drugs, psychological or neurostimulation psychological interactions, that can be explored.

MATTHEW RUDORFER: We also had a question. Yesterday there was discussion in a naloxone trial in sex differences in placebo response. And wonder if there's any further thoughts on studies of sex differences or diversity in general in placebo trials. Yes.

LUANA COLLOCA: We definitely see sex differences in placebo effect, and I show also, for example, women responded to arginine vasopressin in a way that we don't observe in men.

But also you asked about diversity. Currently actually in our paper just accepted today where we look at where people are living, the Maryland states, and even the location where they are based make a difference in placebo effects. So people who live in the most distressed, either the greatest Baltimore area, tended to have lower placebo effects as compared to a not distressful location. And we define that the radius of the criteria and immediately it's a race but we take into account the education, the income and so on. So it is interesting because across studies consistently we see an impact of diversity. And in that sense, I echo, listen to the comment that we need to find a way to reach out to these people and truly improve access and the opportunity for diversity. Thank you for asking.

MATTHEW RUDORFER: Thank you. Another issue that came up yesterday had to do with the pharmacogenomics. And there was a question or a question/comment about using candidate approaches and are they problematic.

KATHRYN HALL: What approaches.

MATTHEW RUDORFER: Candidate genes.

KATHRYN HALL: I think we have to start where we are. I think that the psychiatric field has had a really tough time with genetics. They've invested a lot and, sadly, don't have as much to show for it as they would like to. And I think that that has really tainted this quest for genetic markers of placebo and related studies, these interaction factors. But it's really important to not, I think, to use that to stop us from looking forward and identifying what's there. Because when you start to scratch the surface, there are interactions. You can see them. They're replete in the literature. And what's really fascinating is everybody who finds them, they don't see them when they report their study. And even some of these vasopressin studies, not obviously, Tor, yours, but I was reading one the other day where they had seen tremendous differences by genetics in response to arginine vasopressin. And they totally ignored what they were seeing in placebo and talked about who responds to drug. And so I think that not only do we need to start looking for what's happening, we need to start being more open minded and paying attention to what we're seeing in the placebo arm and accounting for that, taking that into account to understand what we're seeing across a trial in total.

CRISTINA CUSIN: I'll take a second to comment on sufficient selection and trying to figure out, depending on the site who are the patients who went there, treatment and depression clinical trial. If we eliminate from the discussion professional patient and we think about the patients who are more desperate, patients who don't have access to care, patients who are more likely to have psychosocial stressors or the other extreme, there are patients who are highly educated. The trials above and they search out, but they're certainly not representative of the general populations we see in the clinical setting.

They are somewhat different. And then if you think about the psychedelics trial, they go from 5,000 patients applying for a study and the study ends up recruiting 20, 30. So absolutely not representative of the general population we see in terms of diversity, in terms of comorbidities, in terms of psychosocial situations. So that's another factor that adds to the complexity of differentiating what happens in the clinical setting versus artificial setting like a research study. Tor.

MATTHEW RUDORFER: The question of who enters trials and I think the larger issue of diagnosis in general has, I think, really been a challenge to the field for many years. Ted and I go back a ways, and just looking at depression, of course, has dominated a lot of our discussion these last couple of days, with good reason. Now I realize the good database, my understanding is that the good database of placebo controlled trials go back to the late '90s, is what we heard yesterday. And if you go back further, the tricyclic era not only dealt with different medications, which we don't want to go back to, but if you think about practice patterns then, on the one hand, the tricyclics, most nonspecialists steered clear of, they required a lot of hands on. They required titration slowly up. They had some concerning toxicities, and so it was typical that psychiatrists would prescribe them but family docs would not. And that also had the effect of a naturalistic screening, that is, people would have to reach a certain level of severity before they were referred to a psychiatrist to get a prescription for medication.

More mildly ill people either wound up, probably inappropriately, on tranquilizers or no treatment at all and moderately to severely ill people wound up on tricyclics, and of course inpatient stays were common in those days, which again was another kind of screening. So it was the sort of thing, I mean, in the old days I heard of people talk about, well, you could, if you go to the inpatient board, you could easily collect people to be in clinical trial and you kind of knew that they were vetted already. That they had severe depression, the general sense was that the placebo response would be low. Though there's no real evidence for that. But the thing is, once we had the SSRIs on the one hand, the market vastly expanded because they're considered more broad spectrum. People with milder illness and anxiety disorders now are appropriate candidates and they're easier to dispense. The concern about overdose is much less, and so they're mostly prescribed by nonspecialists. So it's the sort of thing where we've seen a lot of large clinical trials where it doesn't take much to reach the threshold for entry, being if I go way back and this is just one of my personal concerns over many years the finer criteria, which I think were the first good set of diagnostic criteria based on data, based on literature, those were published in 1972 to have a diagnosis of major depression, called for four weeks of symptoms. Actually, literally, I think it said one month.

DSM III came out in 1980 and it called for two weeks of symptoms. I don't know -- I've not been able to find any documentation of how the one month went to two weeks, except that the DSM, of course, is the manual that's used in clinical practice. And you can understand, well, you might not want to have too high a bar to treat people who are seeking help. But I think one of the challenges of DSM, it was not meant as a research manual. Though that's often how it's used. So ever since that time, those two weeks have gotten reified, and so my point is it doesn't take much to reach diagnostic criteria for DSM, now, 5TR, major depression. So if someone is doing a clinical trial of an antidepressant, it is tempting to enroll people who meet, honestly meet those criteria but the criteria are not very strict. So I wonder whether that contributes to the larger placebo effect that we see today.

End of soapbox. The question -- I'd like to revisit an excellent point that Dr. Lisanby raised yesterday which has to do with the research domain criteria, the RDOC criteria. I don't know if anyone on the panel has had experience in using that in any trials and whether you see any merit there. Could RDOC criteria essentially enrich the usual DSM type clinical criteria in terms of trying to more finely differentiate subtypes of depression, might respond differently to different treatments.

MODERATOR: I think Tor has been patient on the hand off. Maybe next question, Tor, I'm not sure if you had comments on previous discussion.

TOR WAGER: Sure, thanks. I wanted to make a comment on the candidate gene issue. And I think it links to what you were just saying as well, doctor, in a sense. I think it relates to the issue of predicting individual differences in placebo effects and using that to enhance clinical trials, which has been really sort of a difficult issue. And in genetics, I think what's happened, as many of us know, is that there were many findings on particular candidate genes, especially comped and other particular set of genes in Science and Nature, and none of those really replicated when larger GWA studies started being done. And the field of genetics really focused in on reproducibility and replicability in one of our sample sizes. So I think my genetics colleagues tell me something like 5,000 is a minimum for even making it into their database of genetic associations. And so that makes it really difficult to study placebo effects in sample sizes like that. And at the same time, there's been this trend in psychology and in science, really, in general, towards reproducibility and replicability that probably in part are sort of evoked by John Ioannidis's provocative claims that most findings are false, but there's something really there.

There's been many teams of people who have tried to pull together, like Brian Nosek's work with Open Science Foundation, and many lab studies to replicate effects in psychology with much higher power. So there's this sort of increasing effort to pull together consortia to really test these things vigorously. And I wonder if -- we might not have a GWA study of placebo effects in 100,000 people or something, which is what would convince a geneticist that there's some kind of association. I'm wondering what the ways forward are, and I think one way is to increasingly come together to pull studies or do larger studies that are pre registered and even registered reports which are reviewed before they're published so that we can test some of these associations that have emerged in these what we call early studies of placebo effects.

And I think if we preregister and found something in sufficiently large and diverse samples, that might make a dent in convincing the wider world that essentially there is something that we can use going forward in clinical trials. And pharma might be interested in, for example, as well. That's my take on that. And wondering what people think.

KATHRYN HALL: My two cents. I completely agree with you. I think the way forward is to pull our resources to look at this and not simply stop -- I think when things don't replicate, I think we need to understand why they don't replicate. I think there's a taboo on looking beyond, if you prespecified it and you don't see it, then it should be over. I think in at least this early stage, when we're trying to understand what's happening, I think we need to allow ourselves deeper dives not for action but for understanding.

So I agree with you. Let's pull our resources and start looking at this. The other thing I would like to point out that's interesting is when we've looked at some of these clinical trials at the placebo arm, we actually learn a lot about natural history. We just did one in Alzheimer's disease and in the placebo arm the genome wide significant hit was CETP, which is now a clinical target in Alzheimer's disease. You can learn a lot by looking at the placebo arms of these studies not just about whether or not the drug is working or how the drug is working, but what's happening in the natural history of these patients that might change the effect of the drug.

TED KAPTCHUK: Marta, did you have something to say; you had your hand up.

MARTA PECINA: Just a follow up to what everybody is saying. I do think the issue of individualability is important. I think that one thing that maybe kind of explains some of the things that was also saying at the beginning that there's a little bit of lack of consistency or a way to put all of these findings together. The fact that we think about it as a one single placebo effect and we do know that there's not one single placebo effect, but even within differing clinical conditions is the newer value placebo effect the same in depression as it is in pain?

Or are there aspects that are the same, for example, expectancy processing, but there's some other things that are very specific to the clinical condition, whether it's pain processing, mood or some others. So I think we face the reality of use from a neurobiology perspective that a lot of the research has been done in pain and still there's very little being done at least in psychiatry across many other clinical conditions that we just don't know. And we don't really even know if the placebo how does the placebo effect look when you have both pain and depression, for example?

And so those are still very open questions that kind of reflect our state, right, that we're making progress but there's a lot to do.

TED KAPTCHUK: Winfried, did you want to say something? You have your hand up.

WINFRIED RIEF: I wanted to come back to the question of whether we really understand this increase of placebo effects. I don't know whether you have (indiscernible) for that. But I'm more like a scientist I can't believe that people are nowadays more reacting to placebos than they did 20 years ago. So there might be other explanations for this effect, like we changed the trial designs. We have more control visits maybe nowadays compared to 30 years ago, but there could be also other factors like publication bias which was maybe more frequent, more often 30 years ago than it is nowadays with the need for greater registration. So there are a lot of methodological issues that could explain this increase of placebo effects or of responses in the placebo groups. I would be interested whether you think that this increase is well explained or what your explanations are for this increase.

TED KAPTCHUK: Winfried, I want to give my opinion. I did think about this issue. I remember the first time it was reported in scientists in Cleveland, 40, 50 patients, and I said, oh, my God, okay, and the newspapers had it all over: The placebo effect is increasing. There's this boogie man around, and everyone started believing it. I've been consistently finding as many papers saying there's no -- I've been collecting them. There's no change over time there are changes over time. When I read the original article, I said, of course there's differences. The patients that got recruited in 1980 were different than the patients in 1990 or 2010. They were either more chronic, less chronic.

They were recruited in different ways, and that's really an easy explanation of why things change. Natural history changes. People's health problems are different, and I actually think that the Stone's meta analysis with 70,033 patients says it very clearly. It's a flat line from 1979. And the more data you have, the more you have to believe it. That's all. That's my personal opinion. And I think we actually are very deeply influenced by the media. I mean, I can't believe this:

The mystery of the placebo. We know more about placebo effects at least compared to many drugs on the market. Thanks my opinion. Thanks, Winfried, for letting me say it.

MATTHEW RUDORFER: Thanks, Ted.

We have a question for Greg. The question is, I wonder what the magic of 90 seconds is? Is there a physiologic basis to the turning point when the mouse changes behavior?

GREGORY CORDER: I think I addressed it in a written post somewhere. We don't know. We see a lot of variability in those animals. So like in this putative placebo phase, some mice will remain on that condition side for 40 seconds, 45 seconds, 60 seconds. Or they'll stay there the entire three minutes of the test. We're not exactly sure what's driving the difference in those different animals. These are both male and females. We see the effect in both male and female C57 black six mice, a genetically inbred animal. We always try to restrict the time of day of testing. We do reverse light testing. This is the animal wake cycle.

And there are things like dominance hierarchies within the cages, alpha versus betas. They may have different levels of pain thresholds. But the breaking of whatever the anti nocioceptive effect is they're standing on a hot plate for quite a long time. At some point those nociceptors in the periphery are going to become sensitized and signal. And to some point it's to the animal's advantage to pay attention to pain. You don't want to necessarily go around not paying attention to something that's potentially very dangerous or harmful to you. We would have to scale up the number of animals substantially I think, to really start parse out what the difference is that would account for that. But that's an excellent point, though.

MATTHEW RUDORFER: Carolyn.

CAROLYN RODRIGUEZ: I want to thank all today's speakers and wonderful presentations today. I just wanted to just go back for a second to Dr. Pecina's point about thinking about a placebo effect is not a monolith and also thinking about individual disorders.

And so I'm a clinical trialist and do research in obsessive compulsive disorder, and a lot of the things that are written in the literature meta analysis is that OCD has one of the lowest placebo rates. And so, you know, from what we gathered today, I guess to turn the question on its head is, is why is that, is that the case, why is that the case, and does that say something about OCD pathology, and what about it? Right? How can we really get more refined in terms of different domains and really thinking about the placebo effect.

So just want to say thank you again and to really having a lot of food for thought.

MATTHEW RUDORFER: Thanks. As we're winding down, one of the looming questions on the table remains what are research gaps and where do you think the next set of studies should go. And I think if anyone wants to put some ideas on the table, they'd be welcome.

MICHAEL DETKE: One of the areas that I mentioned in my talk that is hard for industry to study, or there's a big incentive, which is I talked about having third party reviewers review source documents and videos or audios of the HAM D, MADRS, whatever, and that there's not much controlled evidence.

And, you know, it's a fairly simple design, you know, within our largest controlled trial, do this with half the sites and don't do it with the other half.

Blinding isn't perfect. I haven't thought about this, and it can probably be improved upon a lot, but imagine you're the sponsor who's paying the $20 million in three years to run this clinical trial. You want to test your drug as fast as you possibly can. You don't want to really be paying for this methodology.

So that might be -- earlier on Tor or someone mentioned there might be some specific areas where this might be something for NIH to consider picking up. Because that methodology is being used in hundreds of trials, I think, today, the third party remote reviewer. So there's an area to think about.

MATTHEW RUDORFER: Thanks. Holly.

SARAH “HOLLY” LISANBY: Yeah. Carolyn just mentioned one of the gap areas, really trying to understand why some disorders are more amenable to the placebo response than others and what can that teach us. That sounds like a research gap area to me.

Also, throughout these two days we've heard a number of research gap areas having to do with methodology, how to do placebos or shams, how to assess outcome, how to protect the blind, how do you select what your outcome measures should be.

And then also today my mind was going very much towards what can preclinical models teach us and the genetics, the biology of a placebo response, the biogender line, individual differences in placebo response.

There may be clues there. Carolyn, to your point to placebo response being lower in OCD, and yet there are some OCD patients who respond, what's different about them that makes them responders?

And so studies that just look at within a placebo response versus nonresponse or gradation response or durability response and the mechanisms behind that.

These are questions that I think may ultimately facilitate getting drugs and devices to market, but certainly are questions that might be helpful to answer at the research stage, particularly at the translational research stage, in order to inform the design of pivotal trials that you would ultimately do to get things to market.

So it seems like there are many stages before getting to the ideal pivotal trial. So I really appreciate everyone's input. Let me stop talking because I really want to hear what Dr. Hall has to say.

KATHRYN HALL: I wanted to just come back for one of my favorite gaps to this question increasing the placebo effect. I think it's an important one because so many trials are failing these days. And I think it's not all trials are the same.

And what's really fascinating to me is that you see in Phase II clinical trials really great results, and then what's the first thing you do as a pharma company when you got a good result? You get to put out a press release.

And what's the first thing you're going to go do when you enroll in a clinical trial? You're going to read a press release. You're going to read as much as you can about the drug or the trial you're enrolling in. And how placebo boosting is it going to be to see that this trial had amazing effects on this condition you're struggling with.

If lo and behold we go to Phase III, and you can -- we're actually writing a paper on this, how many times we see the words "unexpected results," and I think we saw them here today, today or yesterday. Like, this should not be unexpected. When your Phase III trial fails, you should not be surprised because this is what's happening time and time again.

And I think some of the -- yeah, I agree, Ted, it's like this is a modern time, but there's so much information out there, so much information to sway us towards placebo responses that I think that's a piece of the problem. And finding out what the problem is I think is a really critical gap.

MATTHEW RUDORFER: Winfried.

WINFRIED RIEF: Yeah. May I follow up in that I think it fits quite nicely to what has been said before, and I want to direct I want to answer directly to Michael Detke.

On first glance, it seems less expensive to do the trials the way we do it with one placebo group and one drug arm, and we try to keep the context constant. But this is the problem. We have a constant context without any variation, so we don't learn under which context conditions is this drug really effective and what are the context conditions the drug might not be effective at all.

And therefore I think the current strategy is more like a lottery. It's really by chance it can happen that you are in this little window where the drug can show the most positive effectivity, but it can also be that you are in this little window or the big window where the drug is not able to show its effectivity.

And therefore I think, on second glance, it's a very expensive strategy only to use one single context to evaluate a drug.

MATTHEW RUDORFER: If I have time for--

TED KAPTCHUK: Marta speak, and then Liane should speak.

MARTA PECINA: I just wanted to add kind of a minor comment here, which is this idea that we're going to have to move on from the idea that giving someone a placebo is enough to induce positive expectancies and the fact that expectancies evolve over time.

So at least in some of the data that we've shown, and it's a small sample, but still we see that 50% of those subjects who are given a placebo don't have drug assignment beliefs. And so that is a very large amount of variability there that we are getting confused with everything else.

And so I do think that it is really important, whether in clinical trials, in research, to really come up with very and really develop new ways of measuring expectancies and allow expectancies to be measured over time. Because they do change. We have some prior expectancies, and then we have some expectancies that are learned based on experience. And I do think that this is an area of improvement that the field could improve relatively easily, you know, assess expectancies better, measure expectancies better.

TED KAPTCHUK: Liane, why don't you say something, and Luana, and then Cristina.

LIANE SCHMIDT: So I wanted to -- maybe one -- another open gap is like about the cognition, like what studying placebo, how can it help us to better understand human reasoning, like, and vice versa, actually, all the biases we have, these cognitive processes like motivation, for example, or memory, and yet all the good news about optimism biases, how do they contribute to placebo effects on the patient side but also on the clinician side when the clinicians have to make diagnosis or judge, actually, treatment efficiency based on some clinical scale.

So basically using like tools from cognition, like psychology or cognitive neuroscience, to better understand the processes, the cognitive processes that intervene when we have an expectation and behavior reach out, a symptom or neural activation, what comes in between, like how is it translated, basically, from cognitive predictability.

LUANA COLLOCA: I think we tended to consider expectation as static measurement when in reality we know that what we expect at the beginning of this workshop is slightly different by the end of what we are hearing and, you know, learning.

So expectation is a dynamic phenomenon, and the assumption that we can predict placebo effects with our measurement of expectation can be very limiting in terms of, you know, applications. Rather, it is important to measure expectation over time and also realize that there are so many nuance, like Liane just mentioned, of expectations, you know.

There are people who say I don't expect anything, I try everything, or people who say, oh, I truly want, I will be I truly want to feel better. And these also problematic patients because having an unrealistic expectation can often destroy, as I show, with a violation of expectancies of placebo effects.

TED KAPTCHUK: Are we getting close? Do you want to summarize? Or who's supposed to do that? I don't know.

CRISTINA CUSIN: I think I have a couple of minutes for remarks. There's so much going on, and more questions than answers, of course.

That has been a fantastic symposium, and I was trying to pitch some idea about possibly organizing a summit with all the panelists, all the presenters, and everyone else who wants to join us, because I think that with a coffee or a tea in our hands and talking not through a Zoom video, we could actually come up with some great idea and some collaboration projects.

Anyone who wants to email us, we'll be happy to answer. And we're always open to collaborating and starting a new study, bouncing off each other new ideas. This is what we do for a living. So we're very enthusiastic about people asking difficult questions.

And some of the questions that are ongoing and I think would be future areas is what we were talking a few minutes ago, we don't know if a placebo responder in a migraine study, for example, would be a placebo responder of depression study or IBS study. We don't know if this person is going to be universal placebo responder or is the context include the type of disease they're suffering from so it's going to be fairly different, and why some disorders have lower placebo response rate overall compared to others. Is that a chronicity, a relaxing, remitting disorder, has higher chance of placebo because the system can be modulated, versus a disorder that is considered more chronic and stable? A lot of this information is not known in the natural history.

Also comes to mind the exact trial it is because we almost never have a threshold for number of prior episodes of depression to enter a trial or how chronic has it been or years of depression or other factors that can clearly change our probability of responding to a treatment.

We heard about methodology for clinical trial design and how patients could be responsive to placebo responses or sham, responsive to drug. How about patients who could respond to both? We have no idea how many of those patients are undergoing a trial, universal responders, unless we do a crossover. And we know that crossover is not a popular design for drug trials.

So we need to figure out also aspects of methodology, how to assess outcome, what's the best way to assess the outcome that we want, is it clinically relevant, how to protect the blind aspect, assess expectations and how expectations change over time.

We didn't hear much during the discussion about the role of mindfulness in pain management, and I would like to hear much more about how we're doing in identifying the areas and can we actually intervene on those areas with devices to help with pain management. That's one of the biggest problems we have in terms of clinical care.

In the eating disorder aspect, creating computational models to influence food choices. And, again, with devices or treatments specifically changing the balance about making healthier food choices, I can see an entire field developing. Because most of the medications we prescribe for psychiatric disorders affect food choices and there's weight gain, potentially leading to obesity and cardiovascular complications. So there's an entire field of research we have not touched on.

And the role of animal models in translational results, I don't know if animal researchers, like Greg, talk much with clinical trialists. I think that would be a cross fertilization that is much needed, and we can definitely learn from each other.

And just fantastic. I thank all the panelists for their willingness to work with us and their time, dedication, and just so many meetings to discuss to agree on the program and to divide and conquer different topics. Has been a phenomenal experience, and I'm very, very grateful.

And the NIMH staff has been also amazing, having to collaborate with all of them, and they were so organized. And just a fantastic panel. Thank you, everybody.

MATTHEW RUDORFER: Thank you.

TOR WAGER: Thank you.

NIMH TEAM: Thanks from the NIMH team to all of our participants here.

(Meeting adjourned)