example of apply research

25 Applied Research Examples

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Applied research is research intended to solve specific and practical problems faced by the researcher and their shareholders.

Grimsgaard (2023) defines it below:

“Applied research tends to drill down more toward solving specific problems that affect people in the here and now.”

It is contrasted to basic research , which is research for its own sake. Bentley, Gulbrandsen and Kyvik (2015) define basic research as “research undertaken with a primary purpose of the advancement of knowledge for its own sake.”

The key benefit of applied research is that it helps solve problems in the real world – it is the embodiment of the concept of ‘invention is the mother of invention. But if we only did applied research, we wouldn’t achieve any of the blue skies breakthroughs that are achieved through basis research.

In fact, applied research often follows up from basic research, finding ways to apply that basic research to real-life needs in society.

Applied Research Examples

• Medicine: Development of a new vaccine to combat a specific viral strain.
• Computer Science: Creating an algorithm to enhance image recognition in smartphones.
• Agriculture: Introducing a genetically modified crop variety to improve yield and pest resistance.
• Psychology: Implementing cognitive-behavioral therapy techniques to treat anxiety disorders.
• Environmental Science: Designing a method to purify water using solar energy in remote areas.
• Engineering: Developing a more efficient and lightweight battery for electric cars.
• Education: Evaluating the effectiveness of online teaching methods on student performance.
• Economics: Assessing the impact of a new taxation policy on consumer spending.
• Sociology: Creating community programs based on studies of urban youth engagement.
• Architecture: Designing earthquake-resistant buildings based on geological research.
• Nutrition: Formulating a diet plan to mitigate the effects of type 2 diabetes.
• Linguistics: Developing language learning apps based on cognitive linguistics research.
• Sports Science: Designing a training regimen to enhance the performance of long-distance runners.
• Marketing: Analyzing consumer behavior to optimize product placement in retail stores.
• Geology: Creating risk assessment tools for communities near active volcanoes.
• Transportation: Designing an urban transportation system based on traffic flow research.
• Marine Biology: Establishing sustainable fishing guidelines based on studies of fish populations.
• Chemistry: Developing a new drug formulation for faster pain relief.
• Physics: Creating more efficient solar panels based on the study of photovoltaic materials.
• Communication Studies: Implementing crisis communication strategies for corporations based on media research.
• Aerospace Engineering: Designing a new airplane wing for reduced fuel consumption.
• Biotechnology: Producing biofuels from algae after studying their growth and energy properties.
• Musicology: Enhancing acoustics in concert halls based on sound wave research.
• Pharmacology: Testing a new drug to treat a rare form of cancer.
• Urban Planning: Designing green spaces in cities based on studies of residents’ mental well-being.

Case Studies

1. the invention of the internet.

One of the most celebrated examples of applied research leading to a groundbreaking invention is the development of the World Wide Web by Sir Tim Berners-Lee.

In the late 1980s and early 1990s, Tim Berners-Lee, a British engineer and computer scientist, was working at CERN, the European Organization for Nuclear Research. His task was to find a way to allow scientists to share data and research results efficiently across the world. The challenge was significant because, at that time, there were no universally accepted and easy-to-use methods for data sharing and retrieval across different computer networks and platforms.

In solving this problem, Berners-Lee developed the three fundamental technologies that remain the foundation of today’s Web (and which you may recognize!):

• HTML : HyperText Markup Language
• URI : Uniform Resource Identifier
• HTTP : Hypertext Transfer Protocol

These technologies enabled the creation and retrieval of linked documents and multimedia across a network of computers. Berners-Lee also created the first web browser and web server to demonstrate and utilize these technologies.

The invention of the World Wide Web has had a profound and transformative impact on society, affecting almost every aspect of our daily lives, including communication, education, business, and entertainment. Berners-Lee’s applied research, initially aimed at solving a specific problem related to scientific data sharing, ended up unleashing a revolutionary tool that reshaped the world.

2. The Discovery of Penicillin

The discovery and development of penicillin, an antibiotic, by Alexander Fleming and its subsequent mass production shows how applied research can lead to revolutionary inventions.

In 1928, Alexander Fleming, a Scottish bacteriologist, observed that a mold called Penicillium notatum was able to kill bacteria in a petri dish. This discovery was quite accidental and came while Fleming was researching staphylococci, a type of bacteria. At this point, it was just basic research .

But in 1939, Howard Florey and Ernst Boris Chain took Fleming’s discovery from a useful laboratory finding to a life-saving drug through extensive research and development. They conducted systematic, applied research to figure out how to mass-produce and purify penicillin.

By 1941, the team had successfully treated its first patient with penicillin, marking a major milestone in medicinal history.

But it was in the years of World War II that penicillin really became a life safer – literally. During World War II, the production of penicillin was scaled up massively to treat wounded soldiers, saving countless lives that might have been lost to bacterial infections.

Fleming’s initial discovery and the subsequent applied research by Florey, Chain, and their team transformed penicillin into a practical, widely available antibiotic.

The development and mass production of penicillin marked the beginning of the antibiotic era, fundamentally altering medicine by providing an effective treatment for bacterial infections.

Applied vs Basic Research

Unlike applied research, basic research seeks to expand knowledge and understanding of fundamental principles and theories without immediate application in mind (Abeysekera, 2019; Bentley, Gulbrandsen & Kyvik, 2015).

Basic research is exploratory and often driven by curiosity or the academic interests of the researcher. The results may not have immediate practical implications but can form the foundation for future applied research (Grimsgaard, 2023).

Applied research , on the other hand, is aimed at addressing specific problems or questions, with the intent of applying the findings to practical solutions or actions (Abeysekera, 2019; Baimyrzaeva, 2018).

It is more structured, systematic, and focused on practical problem-solving or enhancing existing methods. The results are typically intended for immediate application, with direct, observable implications.

Benefits and Limitations of Applied Research

Applied research is specifically designed to address immediate problems, which is one of its greatest advantages.

It helps businesses, industries and policy makers improve operations, products, services or policies, thereby providing practical and immediate solutions (Baimyrzaeva, 2018).

Moreover, its impact can be quantified, making it easier to secure funding. However, the main disadvantage is that it is narrowly focused and its findings may not be universally applicable.

However, the desire for quick, practical results can constrain the methodology, perhaps limiting creativity or ignoring broader implications (Baimyrzaeva, 2018; Marotti de Mello & Wood 2019).

The pressure for immediate usability can also drive researchers towards safe, predictable projects rather than innovative or risky ones.

Abeysekera, A. (2019). Basic research and applied research.  Journal of the National Science Foundation of Sri Lanka ,  47 (3).

Baimyrzaeva, M. (2018). Beginners’ guide for applied research process: What is it, and why and how to do it.  University of Central Asia ,  4 (8).

Bentley, P. J., Gulbrandsen, M., & Kyvik, S. (2015). The relationship between basic and applied research in universities.  Higher Education ,  70 , 689-709. ( Source )

Dunn, D. S. (2012). Research Methods for Social Psychology (2nd ed.). Wiley Global Education.

Grimsgaard, W. (2023). Design and strategy: a step by step guide . New York: Taylor & Francis.

Marotti de Mello, A., & Wood Jr, T. (2019). What is applied research anyway?.  Revista de Gestão ,  26 (4), 338-339. ( Source )

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Home » Applied Research – Types, Methods and Examples

Applied Research – Types, Methods and Examples

Table of Contents

Applied Research

Definition:

Applied research is a type of scientific inquiry that focuses on developing practical solutions to real-world problems. It involves the use of existing knowledge, theories, and techniques to address specific problems or challenges in a particular field or industry.

Applied research is often conducted in collaboration with industry or government partners, who provide funding and expertise to support the research. The results of applied research are typically intended to be directly applicable to the real world, and may involve the development of new products, technologies, or processes.

Types of Applied Research

Types of Applied Research are as follows:

Action Research

This type of research is designed to solve specific problems within an organization or community. The research involves collaboration between researchers and stakeholders to develop solutions to issues that affect the organization or community.

Evaluation Research

This type of research is used to assess the effectiveness of a particular program, policy, or intervention. Evaluation research is often used in government, healthcare, and social service settings to determine whether programs are meeting their intended goals.

Developmental Research

This type of research is used to develop new products, technologies, or processes. The research may involve the testing of prototypes or the development of new methods for production or delivery.

Diagnostic Research

This type of research is used to identify the causes of problems or issues. Diagnostic research is often used in healthcare, where researchers may investigate the causes of a particular disease or condition.

Policy Research

This type of research is used to inform policy decisions. Policy research may involve analyzing the impact of existing policies or evaluating the potential outcomes of proposed policies.

Predictive Research

This type of research is used to forecast future trends or events. Predictive research is often used in marketing, where researchers may use data analysis to predict consumer behavior or market trends.

Data Collection Methods

In applied research, data collection methods can be broadly classified into two categories: Quantitative and Qualitative methods:

Quantitative Data Collection

Quantitative research methods involve collecting numerical data that can be analyzed statistically. The most commonly used quantitative data collection methods in applied research include:

• Surveys : Surveys are questionnaires designed to collect data from a large sample of people. Surveys can be conducted face-to-face, over the phone, or online.
• Experiments : Experiments involve manipulating variables to test cause-and-effect relationships. Experiments can be conducted in the lab or in the field.
• Observations : Observations involve watching and recording behaviors or events in a systematic way. Observations can be conducted in the lab or in natural settings.
• Secondary data analysis: Secondary data analysis involves analyzing data that has already been collected by someone else. This can include data from government agencies, research institutes, or other sources.

Qualitative Data Collection

Qualitative research methods involve collecting non-numerical data that can be analyzed for themes and patterns. The most commonly used qualitative data collection methods in applied research include:

• Interviews : Interviews involve asking open-ended questions to individuals or groups. Interviews can be conducted in-person, over the phone, or online.
• Focus groups : Focus groups involve a group of people discussing a topic with a moderator. Focus groups can be conducted in-person or online.
• Case studies : Case studies involve in-depth analysis of a single individual, group, or organization.
• Document analysis : Document analysis involves analyzing written or recorded documents to extract data. This can include analyzing written records, audio recordings, or video recordings.

Data Analysis Methods

In applied research, data analysis methods can be broadly classified into two categories: Quantitative and Qualitative methods:

Quantitative Data Analysis

Quantitative data analysis methods involve analyzing numerical data to identify patterns and trends. The most commonly used quantitative data analysis methods in applied research include:

• Descriptive statistics: Descriptive statistics involve summarizing and presenting data using measures such as mean, median, mode, and standard deviation.
• Inferential statistics : Inferential statistics involve testing hypotheses and making predictions about a population based on a sample of data. This includes methods such as t-tests, ANOVA, regression analysis, and correlation analysis.
• Data mining: Data mining involves analyzing large datasets to identify patterns and relationships using machine learning algorithms.

Qualitative Data Analysis

Qualitative data analysis methods involve analyzing non-numerical data to identify themes and patterns. The most commonly used qualitative data analysis methods in applied research include:

• Content analysis: Content analysis involves analyzing written or recorded data to identify themes and patterns. This includes methods such as thematic analysis, discourse analysis, and narrative analysis.
• Grounded theory: Grounded theory involves developing theories and hypotheses based on the analysis of data.
• Interpretative phenomenological analysis: Interpretative phenomenological analysis involves analyzing data to identify the subjective experiences of individuals.
• Case study analysis: Case study analysis involves analyzing a single individual, group, or organization in-depth to identify patterns and themes.

Applied Research Methodology

Applied research methodology refers to the set of procedures, tools, and techniques used to design, conduct, and analyze research studies aimed at solving practical problems in real-world settings. The general steps involved in applied research methodology include:

• Identifying the research problem: The first step in applied research is to identify the problem to be studied. This involves conducting a literature review to identify existing knowledge and gaps in the literature, and to determine the research question.
• Developing a research design : Once the research question has been identified, the next step is to develop a research design. This involves determining the appropriate research method (quantitative, qualitative, or mixed methods), selecting the data collection methods, and designing the sampling strategy.
• Collecting data: The third step in applied research is to collect data using the selected data collection methods. This can include surveys, interviews, experiments, observations, or a combination of methods.
• Analyzing data : Once the data has been collected, it needs to be analyzed using appropriate data analysis methods. This can include descriptive statistics, inferential statistics, content analysis, or other methods, depending on the type of data collected.
• Interpreting and reporting findings : The final step in applied research is to interpret the findings and report the results. This involves drawing conclusions from the data analysis and presenting the findings in a clear and concise manner.

Applications of Applied Research

Some applications of applied research are as follows:

• Product development: Applied research can help companies develop new products or improve existing ones. For example, a company might conduct research to develop a new type of battery that lasts longer or a new type of software that is more efficient.
• Medical research : Applied research can be used to develop new treatments or drugs for diseases. For example, a pharmaceutical company might conduct research to develop a new cancer treatment.
• Environmental research : Applied research can be used to study and address environmental problems such as pollution and climate change. For example, research might be conducted to develop new technologies for reducing greenhouse gas emissions.
• Agriculture : Applied research can be used to improve crop yields, develop new varieties of plants, and study the impact of pests and diseases on crops.
• Education : Applied research can be used to study the effectiveness of teaching methods or to develop new teaching strategies.
• Transportation : Applied research can be used to develop new technologies for transportation, such as electric cars or high-speed trains.
• Communication : Applied research can be used to improve communication technologies, such as developing new methods for wireless communication or improving the quality of video calls.

Examples of Applied Research

Here are some real-time examples of applied research:

• COVID-19 Vaccine Development: The development of COVID-19 vaccines is a prime example of applied research. Researchers applied their knowledge of virology and immunology to develop vaccines that could prevent or reduce the severity of COVID-19.
• Autonomous Vehicles : The development of autonomous vehicles involves applied research in areas such as artificial intelligence, computer vision, and robotics. Companies like Tesla, Waymo, and Uber are conducting extensive research to improve their autonomous vehicle technology.
• Renewable Energy : Research is being conducted on renewable energy sources like solar, wind, and hydro power to improve efficiency and reduce costs. This is an example of applied research that aims to solve environmental problems.
• Precision Agriculture : Applied research is being conducted in the field of precision agriculture, which involves using technology to optimize crop yields and reduce waste. This includes research on crop sensors, drones, and data analysis.
• Telemedicine : Telemedicine involves using technology to deliver healthcare remotely. Applied research is being conducted to improve the quality of telemedicine services, such as developing new technologies for remote diagnosis and treatment.
• Cybersecurity : Applied research is being conducted to improve cybersecurity measures and protect against cyber threats. This includes research on encryption, network security, and data protection.

Purpose of Applied Research

The purpose of applied research is to solve practical problems or improve existing products, technologies, or processes. Applied research is focused on specific goals and objectives and is designed to have direct practical applications in the real world. It seeks to address problems and challenges faced by individuals, organizations, or communities and aims to provide solutions that can be implemented in a practical manner.

The primary purpose of applied research is to generate new knowledge that can be used to solve real-world problems or improve the efficiency and effectiveness of existing products, technologies, or processes. Applied research is often conducted in collaboration with industry, government, or non-profit organizations to address practical problems and create innovative solutions.

Applied research is also used to inform policy decisions by providing evidence-based insights into the effectiveness of specific interventions or programs. By conducting research on the impact of policies and programs, decision-makers can make informed decisions about how to allocate resources and prioritize interventions.

Overall, the purpose of applied research is to improve people’s lives by developing practical solutions to real-world problems. It aims to bridge the gap between theory and practice, and to ensure that research findings are put into action to achieve tangible benefits.

When to use Applied Research

Here are some specific situations when applied research may be appropriate:

• When there is a need to develop a new product : Applied research can be used to develop new products that meet the needs of consumers. For example, a company may conduct research to develop a new type of smartphone with improved features.
• When there is a need to improve an existing product : Applied research can also be used to improve existing products. For example, a company may conduct research to improve the battery life of an existing product.
• When there is a need to solve a practical problem: Applied research can be used to solve practical problems faced by individuals, organizations, or communities. For example, research may be conducted to find solutions to problems related to healthcare, transportation, or environmental issues.
• When there is a need to inform policy decisions: Applied research can be used to inform policy decisions by providing evidence-based insights into the effectiveness of specific interventions or programs.
• When there is a need to improve efficiency and effectiveness: Applied research can be used to improve the efficiency and effectiveness of processes or systems. For example, research may be conducted to identify ways to streamline manufacturing processes or to improve the delivery of healthcare services.

Characteristics of Applied Research

The following are some of the characteristics of applied research:

• Focus on solving real-world problems : Applied research focuses on addressing specific problems or needs in a practical setting, with the aim of developing solutions that can be implemented in the real world.
• Goal-oriented: A pplied research is goal-oriented, with a specific aim of solving a particular problem or meeting a specific need. The research is usually designed to achieve a specific outcome, such as developing a new product, improving an existing process, or solving a particular issue.
• Practical and relevant: Applied research is practical and relevant to the needs of the industry or field in which it is conducted. It aims to provide practical solutions that can be implemented to improve processes or solve problems.
• Collaborative : Applied research often involves collaboration between researchers and practitioners, such as engineers, scientists, and business professionals. Collaboration allows for the exchange of knowledge and expertise, which can lead to more effective solutions.
• Data-driven: Applied research is data-driven, relying on empirical evidence to support its findings and recommendations. Data collection and analysis are important components of applied research, as they help to identify patterns and trends that can inform decision-making.
• Results-oriented: Applied research is results-oriented, with an emphasis on achieving measurable outcomes. Research findings are often used to inform decisions about product development, process improvement, or policy changes.
• Time-bound : Applied research is often conducted within a specific timeframe, with deadlines for achieving specific outcomes. This helps to ensure that the research stays focused on its goals and that the results are timely and relevant to the needs of the industry or field.

Advantages of Applied Research

Some of the advantages of applied research are as follows:

• Practical solutions: Applied research is focused on developing practical solutions to real-world problems, making it highly relevant to the needs of the industry or field in which it is conducted. The solutions developed through applied research are often highly effective and can be implemented quickly to address specific issues.
• Improved processes: Applied research can help organizations to improve their processes, leading to increased efficiency and productivity. The research can identify areas for improvement, such as bottlenecks or inefficiencies, and provide recommendations for optimizing processes.
• Innovation: Applied research can lead to the development of new products, services, and technologies that can transform industries and create new opportunities for growth and innovation. The research can help organizations to identify unmet needs and develop new solutions to meet them.
• Collaboration : Applied research often involves collaboration between researchers and practitioners, leading to the exchange of knowledge and expertise. Collaboration can result in more effective solutions and can help to build partnerships between academia and industry.
• Increased competitiveness : Applied research can help organizations to stay competitive by enabling them to adapt to changing market conditions and customer needs. The research can provide insights into emerging trends and technologies, helping organizations to stay ahead of the curve.
• Economic growth: Applied research can contribute to economic growth by creating new industries and jobs. The research can lead to the development of new technologies and products that can drive economic growth and create new opportunities for entrepreneurship and innovation.

Limitations of Applied Research

Some of the limitations of applied research are as follows:

• Limited generalizability: Applied research often focuses on specific contexts and may not be generalizable to other settings. This means that the findings of applied research may not be applicable to other industries, regions, or populations.
• Time and resource constraints: Applied research is often conducted within a specific timeframe and with limited resources. This can limit the scope and depth of the research and may prevent researchers from exploring all possible avenues.
• Potential for bias: Applied research may be influenced by the interests and perspectives of the organization or industry funding the research. This can lead to a bias in the research and potentially compromise the objectivity and validity of the findings.
• Ethical considerations: Applied research may raise ethical concerns, particularly if it involves human subjects or sensitive issues. Researchers must adhere to ethical standards and ensure that the research is conducted in a responsible and respectful manner.
• Limited theoretical development: Applied research tends to focus on practical solutions and may not contribute significantly to theoretical development in a particular field. This can limit the broader impact of the research and may hinder the development of new theories and frameworks.
• Limited focus on long-term impact: Applied research often focuses on short-term outcomes, such as developing a new product or improving a process. This may limit the focus on long-term impacts, such as the sustainability of the solution or its broader implications for the industry or society.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Applied Research: Definition, Types & Examples

Every research project begins with a clear definition of the investigation’s purpose, which helps to identify the research procedure or approach used. In this sense, a researcher can conduct either basic or applied research.

This research focuses on answering specific questions to solve a specific problem. It tries to identify a solution to a cultural or organizational problem and is often a follow-up research plan for basic or pure research.

In this blog, we will explain the types of applied research and give some examples. But before that, we will go through what it is.

What is applied research?

Applied research is a non-systematic way of finding solutions to specific research problems or issues. These problems or issues can be on an individual, group, or societal level. It is called “non-systematic” because it goes straight to finding solutions.

It is often called a “scientific process” because it uses the available scientific tools and puts them to use to find answers.

Like in regular research, the researcher identifies the problem, makes a hypothesis, and then experiments to test it. It goes deeper into the findings of true or basic research.

LEARN ABOUT:   Research Process Steps

Types of applied research

This research has three types: 

• Evaluation research, 
• Research and Development, and 
• Action research. 

The short versions of each type are explained below:

• Evaluation research

Evaluation research is one type of applied research. It looks at the information on a research subject. This kind of research leads to objective research or helps people make better decisions sooner. Most of the time, evaluation research is used in business settings. 

The organization uses this research to figure out how the overhead costs can be cut down or cut down a lot.

• Research and development

Research and Development is the second type of applied research. Its main goal is to create or design new products, goods, or services that meet the needs of certain markets in society. It finds out what the needs of the market are. It focuses on finding new ways to improve products that already meet an organization’s needs.

• Action research

Action research is the third type of applied research. Action research is a way to learn about things that happen in everyday life and nature. Its goal is to find real-world solutions to business problems by pointing the business in the right direction.

LEARN ABOUT: Action Research

Examples of applied research

Applied study is used in many areas of study and research, from the sciences to the social sciences. We also talk about how it’s used in those fields and give some examples:

• Applied study in business

Applied study in business sectors is fully dependent on their products and services. It helps organizations understand market needs and trends, and then shape their products to fit customers.

Businesses benefit from This research because it allows them to detect gaps in their findings and obtain primary information on target market preferences.

• It can improve hiring.
• It improves work and policy.
• It identifies workplace skill gaps.
• Applied study in education

The applied study is used in the education field to test different ways of teaching and to find better ways of teaching and learning. Before implementing new education policies, they are tested to see how well they work, how they affect teaching, and how the classroom works.

Applied education research uses quantitative and qualitative methods to collect data from first-hand sources. This information is then looked at and interpreted differently to generate valuable results or conclusions.

LEARN ABOUT: Qualitative Interview

Most applied research in this field is done to develop and test different ways of doing things by trying them out in different situations. It is based on accurate observations and descriptions of the real world.

• Applied study to understand the reach of online learning initiatives.
• Applied study to promote teacher-student classroom engagement.
• Applied study on the new math program.
• Applied study in science

As already said, applied study is often called a scientific process because it uses the available scientific tools to find answers. It can be used in physics, microbiology, thermodynamics, and other fields.

• The applied study is put into practice to cure a disease.
• The applied study is put into practice to improve agricultural practices.
• The applied study is applied to testing new laboratory equipment.
• Applied study in psychology

Researchers use this research in psychology to figure out how people act at work, how HR works, and how the organization is growing and changing so they can come up with solutions.

It is used a lot in areas where researchers try to figure out how people think and then come up with solutions that fit their behavior best.

• Applied study to figure out new ways to deal with depression.
• Applied study to improve students’ grades by emphasizing practical Education.
• Applied study to create a plan to keep employees coming to work regularly.
• Applied study in health

This research is used to examine new drugs in the medical industry. It combines scientific knowledge and procedures with health experiences to produce evidence-based results.

• Applied study in heart surgery.
• Applied study to determine a drug’s efficacy.
• Applied study on a medicine’s adverse effects.

LEARN ABOUT: Theoretical Research

Applied research is an important way to research because it helps organizations find real-world solutions to specific problems while also increasing their output and productivity. In contrast to basic research, which focuses on making theories that explain things, applied research focuses on describing evidence to find solutions.

In the applied study, the researcher uses qualitative and quantitative methods to collect data, such as questionnaires, interviews, and observation methods. Conducting interviews is one of the examples of qualitative data in education . It helps the researcher collect real-world evidence, which is then tested depending on the type of applied research and the main focus.

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• What is Applied Research? + [Types, Examples & Method]

Every research begins with a clear delineation of the purpose of the investigation as this goes a long way to determine the research process or methodology adopted. In this sense, a researcher may choose to carry out basic research or applied research. 

Applied research is set on providing answers to specific questions in a bid to provide a solution to a defined problem. In this article, we will outline the features of this method of systematic investigation as well as how it differs from other approaches to research. 

What is Applied Research?

Applied research is a type of research design that seeks to solve a specific problem or provide innovative solutions to issues affecting an individual, group or society. It is often referred to as a scientific method of inquiry or contractual research because it involves the practical application of scientific methods to everyday problems. 

When conducting applied research, the researcher takes extra care to identify a problem, develop a research hypothesis and goes ahead to test these hypotheses via an experiment. In many cases, this research approach employs empirical methods in order to solve practical problems. 

Applied research is sometimes considered to be a non-systematic inquiry because  of its direct approach in  seeking a solution to a problem. It is typically a follow-up research design that further investigates the findings of pure or basic research in order to validate these findings and apply them to create innovative solutions.     

Types of Applied Research

There are 3 types of applied research. These are evaluation research, research and development, and action research.

• Evaluation Research

Evaluation research is a type of applied research that analyses existing information about a research subject to arrive at objective research outcomes or reach informed decisions. This type of applied research is mostly applied in business contexts, for example, an organisation may adopt evaluation research to determine how to cut down  overhead costs.

• Research and Development

Research and development is a type of applied research that is focused on developing new products and services based on the needs of target markets. It focuses on gathering information about marketing needs and finding ways to improve on an existing product or create new products that satisfy the identified needs.

• Action Research

Action research is a type of applied research that is set on providing practical solutions to specific business problems by pointing the business in the right directions. Typically, action research is a process of reflective inquiry that is limited to specific contexts and situational in nature.

Examples of Applied Research 

Applied research is relevant in different fields of study; especially science and social science-related fields. Examples of applied research can be seen in medicine, education, business, engineering, psychology and health, and these would be further explicated below. 

Applied Research Example in Business

Applied research is used in business to build knowledge and develop product solutions. It enables organisations to identify the peculiar needs of target markets and this would help them to create different business strategies that would allow them to satisfy these needs. 

In addition, conducting contractual research would help business owners to get insightful feedback on product gaps that may have, otherwise, been ignored. This is a great way to get first-hand information on target market reactions which can inform brand decisions. 

Applied research also helps employers of labour to identify and address the productivity needs of their workforce. For instance, an organization may carry out applied research in order to measure the effectiveness of its recruitment practices or of its organisational structure. 

• Applied research to improve an organization’s hiring process.
• Applied research to improve workplace efficiency and organizational policies.
• Applied research to bridge skill gaps in the workplace.

Applied Research Examples in Education  

In education, applied research is used to test pedagogic processes in order to discover the best teaching and learning methods. It is also used to test educational policies before implementation and to address different issues associated with teaching paradigms and classroom dynamics for a better learning experience. 

Educational applied research attempts solving a problem by gathering data from primary sources using a combination of qualitative and quantitative data collection methods. This data serves as empirical evidence which is then subjected to rigorous analysis and description in order to arrive at valid conclusions.

The goal of this research methodology is to determine the applicability of educational theory and principles by way of subjecting hypotheses to experimentation within specific settings. Applied research in education is also more utilitarian as it gathers practical evidence that can inform pragmatic solutions to problems. 

Characteristics of Applied Research in Education 

• It clearly highlights generalizations and hypotheses that inform the research findings.
• It relies on empirical evidence.
• It is set at providing solutions to a defined problem.
• It requires accurate observation and description.
• A study into the way to improve teacher-learner classroom engagements.
• A study into the way to improve a school’s readiness for its students.
• A study to build students’ interests in Mathematics.

Applied Research Example in Science

In itself, applied research is a scientific method of investigation because it applies existing scientific knowledge to practical situations. It is useful in different fields including thermodynamics, physics, material sciences and microbiology. 

Examples of applied research in science include the following: 

• Applied research to improve agricultural crop production
• Applied research to treat or cure a specific disease.

Applied Research Examples in Psychology  

There are different reasons psychologists would make use of applied research in the course of their work. In many cases, industrial-psychologists concerned with workplace behavior, human resources and organisational development combine psychological principles with applied research to proffer solutions. 

Examples of applied research in psychology include:

• Applied research to improve workplace commitment by arriving at practical worker-motivation strategies.
• Investigating treatment and management options for anxiety and panic attacks.
• Investigating factors that improve worker’s productivity.

Applied Research Example in Health   

In health and medical sciences, applied research serves as the background to evidence-based and solution-oriented medicine. It effectively merges scientific knowledge and methods with health experiences in order to arrive at accurate and verifiable results; using empirical research data or evidence. 

The adaptation of applied research to medicine is referred to as applied clinical research . Many health and medical practitioners use applied research to measure the extent to which the findings of basic or pure research can be adopted or modified into a solution-oriented approach.

Examples of applied research in health include:

• An investigation to identify the healing properties of a specific herb.
• An investigation to identify the side effects of using a particular drug.

APPLIED RESEARCH METHODS

Qualitative and quantitative data collection methods are used in applied research to gather empirical evidence that is further subjected to experimentation in order to arrive at valid research outcomes. The following are data collection methods in applied research:

An interview is a qualitative method of data collection that involves having a one-on-one interaction or discussion with the research subjects in order to gather relevant information that can serve as empirical data. It can be conducted with the use of an audio recorder, digital camera or camcorder.

Even though it is time-consuming and expensive, interviews allow the researcher to gather the most relevant data which gives him or her in-depth knowledge about the research subjects. An interview may be structured, semi-structured or unstructured; depending on the research purpose. 

• Surveys/Questionnaires

A questionnaire is an instrument that is typically used for quantitative data gathering . It outlines a series of questions relating to the research context and requires the research subjects to choose or provide responses that reflect their knowledge and experiences.

There are different types of questions that can be contained in a questionnaire including rating scale question s, close and open-ended questions and fixed alternatives. You can create and administer your applied research survey using data-collection platforms like Formplus . 

You can also start choosing from our over 200 online survey/questionnaire templates.

Here is a step-by-step guide on  how to create and administer questionnaires for applied research using Formplus

Sign in to Formplus

In the Formplus builder, you can easily create different questionnaires for applied research by dragging and dropping preferred fields into your form. To access the Formplus builder, you will need to create an account on Formplus. 

Once you do this, sign in to your account and click on “Create Form ” to begin.

Edit Form Title

Click on the field provided to input your form title, for example, “Applied Research Questionnaire”.

Click on the edit button to edit the form.

i. Add Fields: Drag and drop preferred form fields into your form in the Formplus builder inputs column. There are several field input options for questionnaires in the Formplus builder. 

ii. Edit fields

iii. Click on “Save”

iv. Preview form. 

Form Customization

With the form customization options in the form builder, you can easily improve on the appearance of your questionnaire and make it more unique and personalized. Formplus allows you to change your form theme, add background images and even change the font according to your needs. 

Multiple Sharing Options

Formplus also provides multiple form sharing options which enables you to easily share your questionnaire with respondents. With the direct social media sharing buttons, you can swiftly share your applied research questionnaire link to your organization’s social media pages. 

You can send out your questionnaire as email invitations to your research subjects too. Formplus also allows you to share your form’s QR code or embed it in your organization’s website for easy access. 

• Data Reporting

The process of gathering useful information about a research subject which can be used for further research. This can be done through not-for-profit reports, newspapers, website articles and hospital records.

It helps you  gather relevant data that results in more insightful decisions.  However, it is susceptible to bias because the information can easily be exaggerated by the individual or group collecting the data. 

• Observation

A type of data gathering method in applied research that requires the researcher to pay close attention to a subject (s) in order to gather useful information about it. Although bias may arise with this method, observation is widely considered as a universally accepted research practice.

Observation helps the researcher to gather empirical data and thus, it is the starting point for the formulation of a hypothesis. There are different techniques for observation including complete observer, complete participant, participant as observer and observer as participant. 

• Focus Groups

A focus group is a type of qualitative data collection process that allows the researcher to gather information about the disposition, feelings and opinions of the research subjects about a specific issue.

Here, the researcher engages a group comprising 6-10 individuals with a range of open-ended questions with the aim of gathering feedback about their emotional disposition to the issue at hand. This method is cost-effective compared to one-on-one interviews, and the information obtained is insightful and detailed. 

How is Applied Research Different from Basic Research?

Applied research and basic research are common methods of inquiry, based on purpose or utility. However, there are key differences between these 2 research approaches and these would be clearly outlined below: 

Applied research is a type of research that is aimed at the practical application of science in order to solve practical problems. On the other hand, basic research is a type of research that is aimed at expanding knowledge rather than solving problems. 

Basic research is theoretical in nature while applied research is practical and descriptive in nature. Basic research explores and generates theories that may be abstract while applied research tests these theories in order to solve a problem. 

Basic research is universal while applied research is limited. Basic research can focus on diverse or multiple contexts while applied research focuses on specific contexts with the aim of providing a solution to an identified problem. 

Applied research is focused on providing answers or solutions to a specific research question while basic research focuses on multiple concepts at the same time in its quest to expand knowledge. 

• Applied research pays attention to external validity while basic research is more focused on internal validity .

Characteristics of Applied Research 

• Applied research is solution-specific and it addresses practical problems. Unlike basic research that is aimed at theorizing and expanding knowledge, applied research focuses on addressing a particular problem using a range of science-based approaches.
• Applied research is descriptive in nature as it arrives at solutions by experimenting on empirical evidence and describing research outcomes.
• Usually, applied research tests theories arrived at by pure research in order to determine the usefulness of these theories in solving practical problems.
• It describes the relationship between research variables by measuring the characteristics of dependent and independent variables.
• Applied research relies on empirical evidence in order to arrive at valid research outcomes.
• It is not theoretical and it is not directly concerned with the expansion of knowledge.
• Applied research is synthetic in nature.
• It is aimed at the cost-effective reduction of social problems.
• Applied research is action-oriented.

Advantages of Applied Research

• Validity: Applied research is unbiased in nature because it tests empirical evidence in order to arrive at valid research outcomes. It employs carefully mapped-out procedures, and this makes it a more valid research approach.
• It is useful in solving specific problems. It helps individuals and organizations to find solutions to specific problems.

Disadvantages of Applied Research 

• It is not flexible in nature as it is restricted to a stipulated deadline.
• Applied research is limited in nature and it cannot be generalized. In other words, the findings from applied research cannot be generalized.

Conclusion 

Applied research is an important research approach because it helps organisations to arrive at practical solutions to specific problems while improving their productivity and output. Unlike basic research that focuses on generating theories that explain phenomena, applied research pays attention to describing empirical evidence with the aim of providing solutions. 

In carrying out applied research, the researcher combines a number of qualitative and quantitative data-gathering methods including questionnaires, observation methods, and interviews. This helps the researcher to gather empirical evidence that is then subjected to experimentation depending on the type of applied research and the overall focus. 

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What is Applied Research? Definition, Types, Examples

Appinio Research · 10.01.2024 · 35min read

Ever wondered how groundbreaking solutions to real-world challenges are developed, or how innovations come to life? Applied research holds the key. In this guide, we will delve deep into the world of applied research, uncovering its principles, methodologies, and real-world impact.  From harnessing cutting-edge technology to address healthcare crises to revolutionizing industries through data-driven insights, we'll explore the diverse domains where applied research thrives.

What is Applied Research?

Applied research is a systematic and organized inquiry aimed at solving specific real-world problems or improving existing practices, products, or services. Unlike basic research, which focuses on expanding general knowledge, applied research is all about using existing knowledge to address practical issues.

The primary purpose of applied research is to generate actionable insights and solutions that have a direct impact on practical situations. It seeks to bridge the gap between theory and practice by taking existing knowledge and applying it in real-world contexts. Applied research is driven by the need to address specific challenges, make informed decisions, and drive innovation in various domains.

Importance of Applied Research

Applied research holds immense significance across various fields and industries. Here's a list of reasons why applied research is crucial:

• Problem Solving:  Applied research provides effective solutions to real-world problems, improving processes, products, and services.
• Innovation:  It drives innovation by identifying opportunities for enhancement and developing practical solutions.
• Evidence-Based Decision-Making:  Policymakers and decision-makers rely on applied research findings to make informed choices and shape effective policies.
• Competitive Advantage:  In business, applied research can lead to improved products, increased efficiency, and a competitive edge in the market.
• Social Impact:  Applied research contributes to solving societal issues, from healthcare improvements to environmental sustainability.
• Technological Advancement:  In technology and engineering, it fuels advancements by applying scientific knowledge to practical applications.

Applied Research vs. Basic Research

Applied research differs from basic research in several key ways:

• Objectives:  Applied research aims to address specific practical problems or improve existing processes, while basic research seeks to expand general knowledge.
• Focus:  Applied research focuses on solving real-world challenges, whereas basic research explores fundamental principles and concepts.
• Applicability:  Applied research findings are directly applicable to practical situations, while basic research often lacks immediate practical applications.
• Immediate Impact:  Applied research has a more immediate impact on solving problems and improving practices, whereas basic research may have longer-term or indirect effects on knowledge and innovation.
• Research Questions:  Applied research formulates research questions related to practical issues, while basic research poses questions to explore theoretical or fundamental concepts.

Understanding these distinctions is essential for researchers, policymakers, and stakeholders in various fields, as it guides the choice of research approach and the expected outcomes of a research endeavor.

Types of Applied Research

Applied research encompasses various types, each tailored to specific objectives and domains. Understanding these types is essential for choosing the right approach to address real-world problems effectively. Here are some common types of applied research, each with its distinct focus and methodologies.

Evaluation Research

Purpose:  Evaluation research assesses the effectiveness, efficiency, and impact of programs, interventions, or policies. It aims to determine whether these initiatives meet their intended goals and objectives.

Methodology:  Researchers employ a range of quantitative and qualitative methods , including surveys, interviews, observations, and data analysis, to evaluate the outcomes and outcomes of programs or interventions.

Example:  Evaluating the impact of a public health campaign aimed at reducing smoking rates by analyzing pre- and post-campaign survey data on smoking habits and attitudes.

Action Research

Purpose:  Action research focuses on solving practical problems within a specific organizational or community context. It involves collaboration between researchers and practitioners to implement and assess solutions.

Methodology:  Action research is iterative and participatory, with researchers and stakeholders working together to identify problems, develop interventions, and assess their effectiveness. It often involves cycles of planning, action, reflection, and adjustment.

Example:  Teachers collaborating with researchers to improve classroom teaching methods and student outcomes by implementing and refining innovative teaching strategies.

Case Study Research

Purpose:   Case study research investigates a particular individual, organization, or situation in-depth to gain a comprehensive understanding of a specific phenomenon or issue.

Methodology:  Researchers collect and analyze a wealth of data, which may include interviews, documents, observations, and archival records. The goal is to provide a detailed and context-rich description of the case.

Example:  A detailed examination of a successful startup company's growth strategies and challenges, offering insights into factors contributing to its success.

Applied Experimental Research

Purpose:  Applied experimental research seeks to establish causal relationships between variables by manipulating one or more factors and observing their impact on outcomes. It helps determine cause-and-effect relationships in real-world settings.

Methodology:  Researchers conduct controlled experiments, similar to those in basic research, but within practical contexts. They manipulate variables and use statistical analysis to assess their effects on specific outcomes.

Example:  Testing the impact of different website designs on user engagement and conversion rates by randomly assigning visitors to various design versions and measuring their interactions.

Survey Research

Purpose:   Survey research involves collecting data from a sample of individuals or organizations to understand their opinions, attitudes, behaviors, or characteristics. It is commonly used to gather quantitative data on specific topics.

Methodology:  Researchers design surveys with carefully crafted questions and administer them to a representative sample of the target population . Statistical analysis is used to draw conclusions based on survey responses.

Example:  Conducting a national survey to assess public sentiment and preferences on environmental conservation initiatives and policies.

These types of applied research provide a framework for approaching real-world challenges systematically. Researchers can choose the most appropriate type based on their research goals, objectives, and the nature of the problem or phenomenon they seek to address. By selecting the right approach, applied researchers can generate valuable insights and practical solutions in various fields and disciplines.

How to Prepare for Applied Research?

In the preparatory phase of your applied research journey, you'll lay the groundwork for a successful study. This phase involves a series of crucial steps that will shape the direction and ethics of your research project.

Identifying Research Questions

One of the key starting points for any applied research endeavor is identifying the right research questions. Your research questions should be clear, specific, and directly related to the problem or issue you aim to address.

• Engage with Stakeholders:  Reach out to individuals or groups who are affected by or have an interest in the issue you're researching. Their perspectives can help you formulate relevant questions.
• Consider Feasibility:  Ensure that your research questions are feasible within your available resources, including time, budget, and access to data or participants.
• Prioritize Impact:  Focus on questions that have the potential to create meaningful change or provide valuable insights in your chosen field.

Formulating Hypotheses

Hypotheses serve as the guiding stars of your research, providing a clear direction for your investigation. Formulating hypotheses is a critical step that sets the stage for testing and validating your ideas.

• Testable Predictions:  Your hypotheses should be testable and capable of being proven or disproven through empirical research.
• Informed by Literature:  Base your hypotheses on existing knowledge and insights gained from the literature review. They should build upon what is already known and aim to expand that knowledge.
• Clarity and Precision:  Write your hypotheses in a clear and precise manner, specifying the expected relationship or outcome you intend to explore.

Literature Review

Conducting a thorough literature review is like embarking on a treasure hunt through existing knowledge in your field. It's a comprehensive exploration of what other researchers have already discovered and what gaps in knowledge still exist.

• Search Strategies:  Utilize academic databases, journals, books, and credible online sources to search for relevant literature.
• Analyze Existing Research:  Examine the findings, methodologies, and conclusions of previous studies related to your research topic.
• Identify Research Gaps:  Look for areas where current knowledge is insufficient or contradictory. These gaps will be the foundation for your own research.

Data Collection Methods

Selecting the proper data collection methods is crucial to gather the information needed to address your research questions. The choice of methods will depend on the nature of your research and the type of data you require.

• Quantitative vs. Qualitative:  Decide whether you will collect numerical data (quantitative) or focus on descriptive insights and narratives (qualitative).
• Survey Design :  If surveys are part of your data collection plan, carefully design questions that are clear, unbiased, and aligned with your research goals.
• Sampling Strategies:  Determine how you will select participants or data points to ensure representativeness and reliability.

Ethical Considerations

Ethical considerations are at the heart of responsible research. Ensuring that your study is conducted ethically and with integrity is paramount.

• Informed Consent:  Obtain informed consent from participants, ensuring they understand the purpose of the research, potential risks, and their right to withdraw at any time.
• Confidentiality:  Safeguard participants' personal information and ensure their anonymity when reporting findings.
• Minimizing Harm:  Take measures to mitigate any physical or emotional harm that participants may experience during the research process.
• Ethical Reporting:  Accurately represent your research findings, avoiding manipulation or selective reporting that may mislead readers or stakeholders.

By diligently addressing these aspects of research preparation, you are building a solid foundation for your applied research project, setting the stage for effective data collection and meaningful analysis in the subsequent phases of your study.

How to Design Your Research Study?

When it comes to applied research, the design of your study is paramount. It shapes the entire research process, from data collection to analysis and interpretation. In this section, we will explore the various elements that make up the foundation of your research design.

Research Design Types

Your choice of research design is like selecting the blueprint for your research project. Different research design types offer unique advantages and are suited for different research questions. Here are some common research design types:

• Experimental Design :  In this design, researchers manipulate one or more variables to observe their impact on outcomes. It allows for causal inference but may not always be feasible in applied research due to ethical or practical constraints.
• Descriptive Design:  This design aims to describe a phenomenon or population without manipulating variables. It is often used when researchers want to provide a snapshot of a situation or gain insights into a specific context.
• Correlational Design :  In this design, researchers examine relationships between variables without manipulating them. It helps identify associations but does not establish causation.
• Longitudinal Design :   Longitudinal studies involve collecting data from the same subjects over an extended period. They are valuable for tracking changes or developments over time.
• Cross-Sectional Design :  This design involves data collection from a diverse group of subjects at a single point in time. It's helpful in studying differences or variations among groups.

Sampling Methods

Sampling methods determine who or what will be included in your study. The choice of sampling method has a significant impact on the generalizability of your findings. Here are some standard sampling methods:

• Random Sampling:  This method involves selecting participants or data points entirely at random from the population. It ensures every element has an equal chance of being included, which enhances representativeness .
• Stratified Sampling:  In stratified sampling, the population is divided into subgroups or strata, and then random samples are drawn from each stratum. This method ensures that each subgroup is adequately represented.
• Convenience Sampling:  Researchers choose subjects or data points that are readily available and accessible. While convenient, this method may lead to sampling bias as it may not accurately represent the entire population.
• Purposive Sampling:  In purposive sampling, researchers deliberately select specific individuals or groups based on their expertise, experience, or relevance to the research topic. It is often used when seeking specialized knowledge.

Data Collection Tools

Selecting the right data collection tools is essential to gather accurate and relevant information. Your choice of tools will depend on the research design and objectives. Standard data collection tools include:

• Questionnaires and Surveys:  These structured instruments use standardized questions to gather data from participants. They are suitable for collecting large amounts of quantitative data.
• Interviews:   Interviews can be structured, semi-structured, or unstructured. They provide an opportunity to gather in-depth, qualitative insights from participants.
• Observation:  Direct observation involves systematically watching and recording behaviors or events. It's valuable for studying behaviors or phenomena in their natural context.
• Secondary Data :  Researchers can also utilize existing data sources, such as government reports, databases, or historical records, for their research.

Variables and Measurement

Defining variables and choosing appropriate measurement methods is crucial for ensuring the reliability and validity of your research. Variables are characteristics, phenomena, or factors that can change or vary in your study. They can be categorized into:

• Independent Variables:  These are the variables you manipulate or control in your study to observe their effects on other variables.
• Dependent Variables:  These are the variables you measure to assess the impact of the independent variables.

Choosing the right measurement techniques, scales, or instruments is essential to accurately quantify variables and collect valid data. It's crucial to establish clear operational definitions for each variable to ensure consistency in measurement.

Data Analysis Techniques

Once you have collected your data, the next step is to analyze it effectively. Data analysis involves:

• Data Cleaning:  Removing any errors, inconsistencies, or outliers from your dataset to ensure data quality.
• Statistical Analysis :  Depending on your research design and data type, you may use various statistical techniques such as regression analysis , t-tests, ANOVA, or chi-square tests.
• Qualitative Analysis:  For qualitative data, techniques like thematic analysis, content analysis, or discourse analysis help uncover patterns and themes.
• Data Visualization:  Using graphs, charts, and visual representations to present your data effectively.

Chi-Square Calculator :

t-Test Calculator :

One-way ANOVA Calculator :

Selecting the appropriate analysis techniques depends on your research questions, data type, and objectives. Proper data analysis is crucial for drawing meaningful conclusions and insights from your research.

With a solid understanding of research design, sampling methods, data collection tools, variables, and measurement, you are well-equipped to embark on your applied research journey. These elements lay the groundwork for collecting valuable data and conducting meaningful analyses in the subsequent phases of your study.

How to Conduct Applied Research?

Now that you've prepared and designed your research study, it's time to delve into the practical aspects of conducting applied research. This phase involves the execution of your research plan, from collecting data to drawing meaningful conclusions. Let's explore the critical components in this stage.

Data Collection Phase

The data collection phase is where your research plan comes to life. It's a crucial step that requires precision and attention to detail to ensure the quality and reliability of your data.

• Implement Data Collection Methods:   Execute the data collection methods you've chosen, whether they involve surveys, interviews, observations, or the analysis of existing datasets.
• Maintain Consistency:  Ensure that data collection is carried out consistently according to your research design and protocols. Minimize any variations or deviations that may introduce bias .
• Document the Process:  Keep thorough records of the data collection process. Note any challenges, unexpected occurrences, or deviations from your original plan. Documentation is essential for transparency and replication.
• Quality Assurance:  Continuously monitor the quality of the data you collect. Check for errors, missing information, or outliers. Implement data validation and cleaning procedures to address any issues promptly.
• Participant Engagement:  If your research involves human participants, maintain open and respectful communication with them. Address any questions or concerns and ensure participants' comfort and willingness to participate.

Data Analysis Phase

Once you've collected your data, it's time to make sense of the information you've gathered. The data analysis phase involves transforming raw data into meaningful insights and patterns.

• Data Preparation:  Start by organizing and cleaning your data. This includes dealing with missing values, outliers, and ensuring data consistency.
• Selecting Analysis Methods:  Depending on your research design and data type, choose the appropriate statistical or qualitative analysis methods. Common techniques include regression analysis , content analysis, or thematic coding .
• Conducting Analysis:  Perform the chosen analysis systematically and according to established protocols. Ensure that your analysis is reproducible by documenting every step.
• Interpreting Results:  Interpretation involves making sense of your findings in the context of your research questions and hypotheses. Consider the statistical significance of the results and any practical implications they may have.
• Visualization:  Create visual representations of your data, such as graphs, charts, or tables, to convey your findings effectively. Visualizations make complex data more accessible to a broader audience.

Interpretation of Results

Interpreting research results is a critical step that bridges the gap between data analysis and drawing conclusions. This process involves making sense of the patterns and insights that emerge from your analysis.

• Relate to Hypotheses:  Determine whether your results support or refute your hypotheses. Be prepared to explain any unexpected findings.
• Contextualize Findings:  Consider the broader context in which your research takes place. How do your results fit into the larger body of knowledge in your field?
• Identify Patterns :  Highlight significant trends, correlations, or relationships you've uncovered. Discuss their practical implications and relevance.
• Acknowledge Limitations:  Be transparent about any limitations in your study that may affect the interpretation of results. This includes sample size, data quality, and potential biases.

Drawing Conclusions

Drawing conclusions is the ultimate goal of your research. It involves synthesizing your findings and answering the research questions you initially posed.

• Answer Research Questions:  Explicitly address the research questions you formulated at the beginning of your study. State whether your findings confirm or challenge your initial hypotheses.
• Highlight Insights:  Emphasize the key insights and contributions of your research. Discuss the practical implications of your findings and their relevance to the field.
• Recommend Actions:  Based on your conclusions, suggest practical steps, recommendations, or future research directions. How can your research contribute to addressing the problem or challenge you investigated?
• Consider Implications:  Reflect on the broader implications of your research for stakeholders, policymakers, or practitioners in your field.

Common Pitfalls to Avoid

During the data collection, analysis, interpretation, and conclusion-drawing phases, it's essential to be aware of common pitfalls that can affect the quality and integrity of your research.

• Sampling Bias :  Ensure that your sample is representative of the population you intend to study. Address any bias that may have been introduced during data collection.
• Data Manipulation:  Avoid manipulating or selectively reporting data to fit preconceived notions. Maintain transparency in your analysis and reporting.
• Overinterpretation:  Be cautious of drawing overly broad conclusions based on limited data. Acknowledge the limitations of your study.
• Ignoring Ethical Considerations:  Continuously uphold ethical standards in your research, from data collection to reporting. Protect participants' rights and privacy.
• Lack of Validation:  Ensure that the methods and tools you use for data collection and analysis are valid and reliable. Validation helps establish the credibility of your findings.

By navigating the data collection, analysis, interpretation, and conclusion-drawing phases with care and attention to detail, you'll be well-prepared to confidently share your research findings and contribute to advancing knowledge in your field.

How to Report Applied Research Results?

Now that you've conducted your applied research and drawn meaningful conclusions, it's time to share your insights with the world. Effective reporting and communication are crucial to ensure that your research has a real impact and contributes to the broader knowledge base.

Writing Research Reports

Writing a comprehensive research report is the cornerstone of communicating your findings. It provides a detailed account of your research process, results, and conclusions. Here's what you need to consider:

Structure of a Research Report

• Title:  Create a concise, informative title that reflects the essence of your research.
• Abstract:  Summarize your research in a clear and concise manner, highlighting key objectives, methods, results, and conclusions.
• Introduction:  Provide an overview of your research topic, objectives, significance, and research questions.
• Literature Review:  Summarize relevant literature and identify gaps in existing knowledge that your research addresses.
• Methodology:  Describe your research design, sampling methods, data collection tools, and data analysis techniques.
• Results:  Present your findings using tables, charts, and narratives. Be transparent and objective in reporting your results.
• Discussion:  Interpret your results, discuss their implications, and relate them to your research questions and hypotheses.
• Conclusion:  Summarize your main findings, their significance, and the implications for future research or practical applications.
• References:  Cite all sources and studies you referenced in your report using a consistent citation style (e.g., APA, MLA).

Writing Tips

• Use clear and concise language, avoiding jargon or overly technical terms.
• Organize your report logically, with headings and subheadings for easy navigation.
• Provide evidence and data to support your claims and conclusions.
• Consider your target audience and tailor the report to their level of expertise and interest.

Creating Visualizations

Visualizations are powerful tools for conveying complex data and making your research findings more accessible. Here are some types of visualizations commonly used in research reports:

Charts and Graphs

• Bar Charts:  Ideal for comparing categories or groups.
• Line Charts:  Effective for showing trends or changes over time.
• Pie Charts:  Useful for displaying proportions or percentages.
• Data Tables:  Present numerical data in an organized format.
• Cross-tabulations:  Show relationships between variables.

Diagrams and Maps

• Flowcharts:  Visualize processes or workflows.
• Concept Maps:  Illustrate connections between concepts.
• Geographic Maps:  Display spatial data and patterns.

When creating visualizations:

• Choose the correct type of visualization for your data and research questions.
• Ensure that visualizations are labeled, clear, and easy to understand.
• Provide context and explanations to help readers interpret the visuals.

Presenting Your Research

Presenting your research to an audience is an opportunity to engage, educate, and inspire. Whether it's through a conference presentation, seminar, or webinar, effective communication is vital.

• Know Your Audience:  Tailor your presentation to the interests and expertise of your audience.
• Practice:  Rehearse your presentation to ensure a smooth delivery and confident demeanor.
• Use Visual Aids:  Enhance your presentation with visual aids such as slides, images, or videos.
• Engage with Questions:  Encourage questions and discussions to foster interaction and clarify points.
• Stay within Time Limits:  Respect time constraints and stay on schedule.

Peer Review Process

Before your research is published, it typically undergoes a peer review process. This involves experts in your field evaluating the quality, validity, and significance of your work. The peer review process aims to ensure the integrity and credibility of your research.

• Submission:  Submit your research manuscript to a journal or conference for review.
• Editorial Review:  The editorial team assesses your submission's fit with the journal's scope and may conduct an initial review for quality and compliance.
• Peer Review:  Your manuscript is sent to peer reviewers who evaluate it for methodology, validity, significance, and adherence to ethical standards.
• Feedback and Revision:  Based on reviewers' feedback, you may be asked to revise and improve your research.
• Acceptance or Rejection:  After revisions, the manuscript is reevaluated, and a decision is made regarding publication.

Publishing Your Research

Publishing your research is the final step in sharing your findings with the broader scientific community. It allows others to access and build upon your work. Consider the following when choosing where to publish:

• Journal Selection:  Choose a reputable journal that aligns with your research field and target audience.
• Review Process:  Understand the journal's peer review process and requirements for submission.
• Open Access:  Consider whether you want your research to be open access, freely accessible to all.

Once published, actively promote your research through academic networks, conferences, and social media to maximize its reach and impact.

By effectively reporting and communicating your research findings, you contribute to the advancement of knowledge, inspire others, and ensure that your hard work has a meaningful impact on your field and beyond.

Applied Research Examples

To provide a deeper understanding of applied research's impact and relevance, let's delve into specific real-world examples that demonstrate how this type of research has addressed pressing challenges and improved our lives in tangible ways.

Applied Medical Research: mRNA Vaccines

Example:  mRNA (messenger RNA) vaccine technology, exemplified by the COVID-19 vaccines developed by Pfizer-BioNTech and Moderna, is a remarkable achievement in the field of applied medical research.

Applied researchers in this domain utilized mRNA technology to create vaccines that provide immunity against the SARS-CoV-2 virus. Unlike traditional vaccines, which use weakened or inactivated viruses, mRNA vaccines instruct cells to produce a harmless spike protein found on the virus's surface. The immune system then recognizes this protein and mounts a defense, preparing the body to combat the actual virus.

Impact:  The rapid development and deployment of mRNA vaccines during the COVID-19 pandemic have been groundbreaking. They've played a crucial role in controlling the spread of the virus and saving countless lives worldwide. This example underscores how applied research can revolutionize healthcare and respond swiftly to global health crises.

Environmental Science and Applied Research: Ocean Cleanup

Example:  The Ocean Cleanup Project, founded by Boyan Slat, is an ambitious endeavor rooted in applied research to combat plastic pollution in the world's oceans.

This project employs innovative technology, such as large-scale floating barriers and autonomous systems, to collect and remove plastic debris from the ocean. Applied researchers have played a pivotal role in designing, testing, and optimizing these systems to make them efficient and environmentally friendly.

Impact:  The Ocean Cleanup Project is a testament to the power of applied research in addressing pressing environmental challenges. By removing plastic waste from the oceans, it mitigates harm to marine ecosystems and raises awareness about the urgent need for sustainable waste management.

Business and Applied Research: E-commerce Personalization

Example:   E-commerce giants like Amazon and Netflix use applied research to develop sophisticated personalization algorithms that tailor product recommendations and content to individual users.

Applied researchers in data science and machine learning analyze user behavior, preferences, and historical data to create recommendation systems. These algorithms utilize predictive analytics to suggest products, movies, or shows that align with a user's interests.

Impact:  The application of research-driven personalization has transformed the e-commerce and streaming industries. It enhances user experiences, increases customer engagement, and drives sales by presenting customers with products or content they are more likely to enjoy.

Agriculture and Applied Research: Precision Agriculture

Example:  Precision agriculture employs data-driven technology and applied research to optimize farming practices.

Farmers utilize satellite imagery, sensors, and data analytics to monitor crop conditions, soil health, and weather patterns. Applied research guides the development of precision farming techniques, enabling more efficient resource allocation and reducing environmental impact.

Impact:  Precision agriculture increases crop yields, conserves resources (such as water and fertilizer), and minimizes the ecological footprint of farming. This approach contributes to sustainable and economically viable agriculture.

These real-world examples underscore the versatility and impact of applied research across diverse domains. From healthcare and environmental conservation to business, education, and agriculture, applied research continually drives innovation, addresses critical challenges, and enhances the quality of life for individuals and communities worldwide.

Conclusion for Applied Research

Applied research is a powerful force for solving real-world problems and driving progress. By applying existing knowledge and innovative thinking, we can address healthcare challenges, protect our environment, improve businesses, enhance education, and revolutionize agriculture. Through this guide, you've gained valuable insights into the what, why, and how of applied research, unlocking the potential to make a positive impact in your field. So, go forth, conduct meaningful research, and be part of the solution to the world's most pressing issues. Remember, applied research is not just a concept; it's a practical approach that empowers individuals and teams to create solutions that matter. As you embark on your own applied research endeavors, keep the spirit of inquiry alive, remain open to new ideas, and never underestimate the transformative power of knowledge put into action.

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How Applied Research Is Used in Psychology

Verywell / JR Bee

Basic vs. Applied Research

How it works, potential challenges.

• Real-World Applications

Applied research refers to scientific study and research that seeks to solve practical problems. This type of research plays an important role in solving everyday problems that can have an impact on life, work, health, and overall well-being. For example, it can be used to find solutions to everyday problems, cure illness, and develop innovative technologies.

There are many different types of psychologists who perform applied research. Human factors or industrial/organizational psychologists often do this type of research.

A few examples of applied research in psychology include:

• Analyzing what type of prompts will inspire people to volunteer their time to charities
• Investigating if background music in a work environment can contribute to greater productivity
• Investigating which treatment approach is the most effective for reducing anxiety
• Researching which strategies work best to motivate workers
• Studying different keyboard designs to determine which is the most efficient and ergonomic

As you may notice, all of these examples explore topics that will address real-world issues. This immediate and practical application of the findings is what distinguishes applied research from basic research , which instead focuses on theoretical concerns.  

Basic research tends to focus on "big picture" topics, such as increasing the scientific knowledge base around a particular topic. Applied research tends to work toward solving specific problems that affect people in the here and now.

For example a social psychologist may perform basic research on how different factors may contribute to violence in general. But if a social psychologist were conducting applied research, they may be tackling the question of what specific programs can be implemented to reduce violence in school settings.

However, basic research and applied research are actually closely intertwined. The information learned from basic research often builds the basis on which applied research is formed.

Basic research often informs applied research, and applied research often helps basic researchers refine their theories.

Applied research usually starts by identifying a problem that exists in the real world. Then psychologists begin to conduct research in order to identify a solution.

The type of research used depends on a variety of factors. This includes unique characteristics of the situation and the kind of problem psychologists are looking to solve.

Researchers might opt to use naturalistic observation to see the problem as it occurs in a real-world setting. They may then conduct experiments to determine why the problem occurs and to explore different solutions that may solve it.

As with any type of research, challenges can arise when conducting applied research in psychology. Some potential problems that researchers may face include:

Ethical Challenges

When conducting applied research in a naturalistic setting, researchers have to avoid ethical issues, which can make research more difficult. For example, they may come across concerns about privacy and informed consent.

In some cases, such as in workplace studies conducted by industrial-organizational psychologists, participants may feel pressured or even coerced into participating as a condition of their employment. Such factors sometimes impact the result of research studies.

Problems With Validity

Since applied research often takes place in the field, it can be difficult for researchers to maintain complete control over all of the variables . Extraneous variables can also exert a subtle influence that experimenters may not even consider could have an effect on the results.

In many cases, researchers are forced to strike a balance between a study's ecological validity (which is usually quite high in applied research) and the study's internal validity .  

Since applied research focuses on taking the results of scientific research and applying it to real-world situations, those who work in this line of research tend to be more concerned with the external validity of their work.

External validity refers to the extent that scientific findings can be generalized to other populations.

Researchers don't just want to know if the results of their experiments apply to the participants in their studies, rather they want these results to also apply to larger populations outside of the lab.

External validity is often of particular importance in applied research. Researchers want to know that their findings can be applied to real people in real settings.

How It's Used in the Real-World

Here are some examples of how applied research is used to solve real-world problems:

• A hospital may conduct applied research to figure out how to best prepare patients for certain types of surgical procedures.
• A business may hire an applied psychologist to assess how to design a workplace console to maximize efficiency and productivity while minimizing worker fatigue and error.
• An organization may hire an applied researcher to determine how to select employees that are best suited for certain positions within the company.

Applied research is an important tool in the process of understanding the human mind and behavior. Thanks to much of this research, psychologists are able to investigate problems that affect people's daily lives. This kind of research specifically targets real-world issues, however it also contributes to knowledge about how people think and behave.

National Science Foundation. Definitions of research and development: An annotated compilation of official sources .

CDC. Evaluation briefs .

Helmchen H. Ethical issues in naturalistic versus controlled trials .  Dialogues Clin Neurosci . 2011;13(2):173‐182.

Truijens FL, Cornelis S, Desmet M, De Smet MM, Meganck R. Validity beyond measurement: Why psychometric validity is insufficient for valid psychotherapy research .  Front Psychol . 2019;10:532. doi:10.3389/fpsyg.2019.00532

 McBride D.  The Process Of Research In Psychology . SAGE Publications; 2018.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Applied Research Examples: Empowering Real-World Solutions

Understand the meaning of ‘ibid’ and its usage in academic and legal writing. Explore citation practices and raise your referencing skills.

Applied research plays a crucial role in various fields, providing practical solutions to real-world problems and driving advancements in technology, healthcare, business, and more. It bridges the gap between theory and practice by translating scientific knowledge into tangible outcomes that positively impact individuals, organizations, communities, or industries. Applied research enables us to develop innovative solutions, refine existing practices, and make informed decisions based on evidence. By focusing on practical applications, applied research contributes to advancements in various fields, ultimately leading to societal progress and improvement. It serves as a driving force for innovation, economic growth, and the overall betterment of individuals and communities. 

What is Applied Research and its purposes?

Applied research is a systematic and practical approach to investigating real-world problems and finding practical solutions. It makes application of scientific methods and techniques to gather and analyze data, conduct experiments, and make evidence-based recommendations. The primary purpose of applied research is to address specific issues or challenges in various fields. It aims to make better-existing practices, processes, or products, validate the effectiveness of interventions or programs, inform policy decisions, and contribute to the development and implementation of evidence-based strategies. 

When using Applied Research

Some common applications of applied research include:

Business and Marketing

Through the utilization of applied research, organizations can gain valuable insights into consumer behavior, evaluate marketing strategies, assess market trends, and identify opportunities for product development and innovation.

Healthcare and Medicine

Applied research is conducted to evaluate the effectiveness of medical treatments, interventions, and healthcare policies. It helps in the advancement of patient outcomes, optimizes healthcare delivery systems, and informs evidence-based medical practices.

In education, applied research informs curriculum development, evaluates program effectiveness, guides evidence-based instruction, informs policy decisions, supports professional development, and optimizes student assessment and evaluation.

Public Policy and Governance

Policymakers can leverage data-driven insights to inform decision-making, evaluate program effectiveness, and enhance governance practices. This approach fosters evidence-based policymaking, promotes transparency, and facilitates effective and equitable governance.

Environmental Studies

Applied research is used to address environmental challenges, such as climate change, pollution, and natural resource management. It helps develop sustainable practices, assess the environmental impact of policies and projects, and guide conservation efforts.

Technology and Engineering

Optimizing efficiency and advancing technology, applied research in technological and engineering fields develops and upgrades products, systems, and processes, addressing practical problems with innovative solutions.

Agriculture and Food Science

Applied research is conducted to raise agricultural practices and crop yields, ensure food safety, and develop sustainable farming methods. It addresses challenges related to food production, distribution, and environmental impact.

Types of Applied Research

Applied research includes various types tailored to address specific practical issues and inform decision-making. Some common types of applied research are:

Evaluation Research

This type of research focuses on assessing the effectiveness, efficiency, and impact of programs, interventions, policies, or initiatives. It measures outcomes, identifies strengths and weaknesses, and provides recommendations for improvement.

Action Research

Action research involves collaborative inquiry and problem-solving in real-world settings. It emphasizes the active participation of stakeholders to identify and address practical challenges, often leading to immediate changes or interventions.

Research and Development

R&D is a type of applied research with the objective to create innovative products and services to meet market needs. It requires gathering market information, improving existing products, and developing new ones to fulfill customer demands and enhance organizational effectiveness.

Policy Research

Generating evidence-based recommendations for policymakers, policy research plays a crucial role in informing policy development, implementation, and evaluation. By analyzing existing policies and identifying areas for improvement, it aims to shape effective and informed decision-making processes.

Data Collection Methods

Data collection methods refer to the techniques and approaches used to gather information or data for research purposes. These methods vary depending on the nature of the research question, the type of data needed, and the resources available. Here are some common data collection methods:

This collects data through structured questionnaires or interviews. They can be administered in person, over the phone, through mail, or online. Surveys are useful for gathering information from a large number of participants and obtaining self-reported data on attitudes, opinions, behaviors, or demographics.

Interviews conduct one-on-one or group conversations with participants to gather detailed information. Interviews can be structured (with predetermined questions), semi-structured (with a set of guiding questions), or unstructured (allowing for open-ended discussion). Interviews are useful for exploring complex topics, capturing in-depth insights, and understanding participants’ perspectives.

Observations

The method of observation watches and records behaviors, actions, or events in their natural settings. Researchers can be participant observers (actively participating in the observed context) or non-participant observers (observing from a distance). Observations are valuable for studying social interactions, behaviors, and patterns in real-life contexts.

Experiments

Experiments manipulate variables under controlled conditions to determine cause-and-effect relationships. Participants are assigned to different experimental conditions, and data is collected to assess the impact of the manipulated variables. Experiments allow researchers to study causal relationships and test hypotheses.

Data Analysis Methods

Data analysis methods are the techniques and procedures used to analyze and interpret data collected during a research study. These methods help researchers make sense of the data, identify patterns, draw conclusions, and answer research questions. Data analysis plays a crucial role in research as it transforms raw data into meaningful insights and supports evidence-based decision-making. Some common data analysis methods are:

Descriptive Statistics

Descriptive statistics summarize and describe the main characteristics of the data. They include measures such as mean, median, mode, standard deviation, and frequency distributions. Descriptive statistics provide a snapshot of the data’s central tendency, dispersion, and distribution.

Inferential Statistics

Inferential statistics make inferences or draw conclusions about a population based on a sample. These methods help researchers test hypotheses, determine statistical significance, and make generalizations. Examples of inferential statistics include t-tests, analysis of variance (ANOVA), regression analysis, and chi-square tests.

Data Mining

Data mining uses computational algorithms to discover patterns, trends, and relationships within large datasets. It helps identify hidden insights and generate predictive models. Data mining techniques include association rule mining, classification, clustering, and anomaly detection.

Applied Research Methodology

Applied research methodology refers to the systematic approach used to conduct applied research studies. It is a series of steps and procedures designed to gather relevant data, analyze it, and draw meaningful conclusions to address real-world problems or provide practical solutions. The methodology for applied research typically includes the following key components:

Problem Identification

Clearly defining and understanding the specific problem or issue to be addressed is the first step in applied research methodology. Conducting a thorough literature review, consulting with experts, and engaging stakeholders are essential steps to gain insights into the problem’s context, scope, and potential impact.

Research Design

Developing a research design involves determining the appropriate research approach, such as quantitative, qualitative, or mixed methods, based on the research objectives and the nature of the problem. It also includes selecting the appropriate data collection methods, sampling techniques, and data analysis procedures.

Data Collection

Data collection methods are chosen based on the research design and the type of data required. Common data collection methods include surveys, interviews, observations, experiments, case studies, and document analysis. Rigorous data collection techniques ensure the collection of accurate and reliable data relevant to the research problem.

Data Analysis

Data analysis is about processing, organizing, and interpreting the collected data to derive meaningful insights. Depending on the nature of the data, quantitative analysis techniques such as statistical analysis, regression analysis, or data mining may be used. Qualitative analysis techniques, such as thematic analysis or content analysis, can be employed for textual or qualitative data.

Results and Conclusion

The analyzed data is used to draw conclusions, identify patterns, and make inferences related to the research problem. The results are presented in a clear and concise manner, often through tables, charts, or visualizations. Conclusions should be supported by evidence from the data analysis and aligned with the research objectives.

Examples of Applied Research

The applied research examples illustrate how this addresses real-world issues and aims to provide practical solutions that can be implemented and make a meaningful impact in various domains. Here are some applied research examples across different fields:

In healthcare, the focus is finding practical solutions to improve patient care and outcomes. For example, a study investigating the effectiveness of a new medical treatment or therapy for a specific condition would be considered applied research.

The primary objective is to increase teaching methods, curriculum development, and student learning outcomes. This involves evaluating the effectiveness of various instructional approaches and designing interventions to foster improved student engagement and achievement.

In the domain of business and marketing, the emphasis is on tackling practical issues encountered by organizations. This can include analyzing consumer behavior to devise impactful marketing strategies or conducting market research to evaluate the viability of introducing a new product. Such endeavors align with the principles of applied research.

Environmental Science

The objective is to devise practical solutions for addressing environmental challenges. This can encompass studying the effects of pollution on ecosystems, formulating sustainable practices, or assessing the efficacy of conservation initiatives. These pursuits align with the principles of applied research.

Engineering and Technology

The emphasis of applied research on engineering and technology is to create inventive solutions for real-world problems. This could entail research endeavors aimed at enhancing energy efficiency, refining manufacturing techniques, or pioneering novel materials.

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Basic vs. applied research

• Coding qualitative data for valuable insights

What is the difference between applied research and basic research?

Examples of basic research vs. applied research, basic vs. applied research: a comparative analysis, the interplay between basic and applied research, introduction.

Basic and applied research look at existing knowledge and create new knowledge in different ways. They share the same basic principles of contributing to knowledge through research findings, but their aims and objectives are distinctly different.

In the vast realm of scientific inquiry, research stands as the cornerstone for advancement, driving our understanding of the world and fostering innovation. At its core, research can be bifurcated into two primary types: applied and basic research . While both serve pivotal roles in contributing to our collective knowledge, they operate with distinct objectives and outcomes.

Any approach that is called basic research delves into the foundational principles and theories of science. It is driven by a researcher's curiosity and the aspiration to expand the frontiers of understanding. The primary goal isn't to solve an immediate problem but to garner knowledge for the sake of understanding.

On the other hand, applied research focuses on analysis intended to solve practical problems. Conducting applied research means seeking solutions to specific, tangible challenges that society or industries face. Using the principles derived from basic research, applied research aims to bring about real-world impact and deliver pragmatic solutions.

Basic research

Basic research, often called "pure" or "fundamental" research , is characterized by its intrinsic quest to unravel the mysteries of nature and society. It is an investigation into the very core of phenomena, aiming to discover new principles, theories, or facts without an immediate application in mind. This kind of research is often propelled by the researcher's curiosity, a thirst to understand the "why" and "how" of things, rather than the "what can we do with it."

Basic research has a relatively broad scope and aims to enhance the existing body of knowledge in a particular field. It's not about creating a new product, improving a process, or solving a current societal problem. Instead, it's about laying the groundwork for future investigations, paving the way for applied research to build upon. Basic research poses questions like, "What are the fundamental principles of this phenomenon?" or "How does this process work at different levels?"

Such goals provide the essential framework upon which much of our modern understanding and technological advancement rests. Without the exploratory and explanatory nature of basic research, the foundational knowledge needed to drive innovation would be missing.

Applied research

While basic research focuses on curiosity and the pursuit of knowledge for its own sake, applied research takes a different approach by examining how real-world phenomena or outcomes can be altered. At its core, applied research is oriented towards identifying practical solutions to specific problems. Its primary objective is not just to add to the existing knowledge base but to leverage that knowledge to develop solutions, innovations, or interventions that can be directly applied in the real world.

Applied research is deeply rooted in real-world issues. Whether it's finding a cure for a specific disease, developing a new technological solution for environmental challenges, or creating strategies to improve education in underprivileged communities, the primary goal is to generate practical outcomes that can be directly implemented. Its relevance is often immediately apparent, as it's tailored to answer particular challenges faced by society, industries, or organizations.

The line between basic and applied research can sometimes blur, especially when foundational discoveries from basic research lead directly to tangible applications. However, the main distinction lies in the intent: while basic research seeks to understand the fundamental nature of phenomena, applied research aims to harness that understanding for tangible benefits.

Applied research is invaluable as it accelerates the transition of theoretical knowledge into practical, impactful solutions. Through applied research, the abstract findings of basic research are transformed into actionable insights, tools, and technologies that shape our daily lives and address pressing challenges.

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Research in the social sciences encompasses a broad spectrum of topics, ranging from understanding human behavior and societal structures to exploring the dynamics of interpersonal relationships. Basic and applied research methods in the social sciences offer unique insights into these areas. Let's delve into some examples to understand their distinct approaches.

Basic research examples

The social construction of reality

A classic area of investigation in sociology is understanding how societies construct reality. This kind of research delves deep into the ways cultures, languages, and institutions shape our understanding of the world. It doesn't immediately aim to solve societal problems but provides essential insights into how perceptions and beliefs are formed. Research methods often used for this type of study include in-depth interviews , participant observations , and ethnographic studies .

Attachment theory in psychology

Attachment theory seeks to understand the deep emotional and physical attachment between a child and at least one primary caregiver. It delves into the nature of attachment and its implications for personal development. The research often involves longitudinal studies that observe behaviors over extended periods.

Applied research examples

Interventions for at-risk youth

Applied researchers might design programs or interventions to help at-risk youth, building on the foundational knowledge of psychology, sociology, and education. The research might involve evaluating the effectiveness of a particular program, using methods like surveys , focus groups , and pre-and-post assessments.

Communication strategies for public health

Understanding human behavior is crucial for successful public health campaigns. Researchers might study the best ways to communicate vital health information to various populations, especially in times of crisis like pandemics. Methods often include A/B testing of messages, surveys to assess message efficacy, and observational studies to gauge real-world behavior following communication campaigns.

The distinction between basic and applied research is not just a matter of intent or outcome; it also encompasses differences in methodologies , scopes, and approaches. Let's undertake a comparative analysis to illuminate these distinctions further, particularly in the context of the social sciences.

Purpose and motivation

Basic research is motivated by the quest for knowledge. It seeks to answer fundamental questions about human behavior, societal structures, and the interplay between various social factors. The driving force here is curiosity. In contrast, applied research is driven by the need to address specific societal or practical problems. Its purpose is to take the theoretical knowledge derived from basic research and convert it into actionable solutions.

Methodological approaches

It's important to acknowledge that there is no one universal research method that can address all potential research inquiries. Moreover, the same research methods, such as conducting interviews or engaging in inductive and deductive reasoning , can be utilized in basic and applied research, but they will differ in their scope and objectives. While applied research is more experimental or confirmatory, a basic research approach is often exploratory or explanatory in nature. Basic research methods include ethnography , in-depth interviews , or longitudinal studies to gain a deep understanding of a topic. The focus is on generating theories and understanding patterns.

Applied research, on the other hand, often employs more structured and targeted methodologies. Surveys , experiments, and evaluations are commonly used to verify propositions, assess the efficacy of interventions, or gauge public opinion. The approach is more pragmatic, seeking results that can inform decisions and guide actions.

Outcomes and results

Basic research outcomes are usually theoretical contributions: new concepts, theories, or insights into existing phenomena. The results expand the academic literature and provide a foundation for future studies.

Applied research results in tangible solutions or recommendations. The outcomes might include a new social program, policy recommendations, interventions, or communication strategies. The results are geared towards immediate implementation and often have direct implications for organizations, governments, or communities.

The discourse on basic and applied research often sets them apart, emphasizing their distinct objectives and methodologies. However, it's crucial to recognize that these research types aren't isolated from each other. They coexist in a symbiotic relationship, where the findings from basic research often provide the foundational knowledge for applied research, and the results of applied research can inspire further basic investigations.

The transition of knowledge

One of the most notable instances of the interplay is how basic research's findings become the bedrock for applied research projects. For example, a basic research study on cognitive development in children might reveal specific patterns or stages. An applied researcher, recognizing the implications of these findings, could then design educational interventions tailored to these developmental stages.

How one complements the other

Basic research pushes the boundaries of our understanding, expanding the horizon of what we know. Applied research, on the other hand, can reframe this expansive knowledge and make it relevant and actionable for society's immediate needs.

But the relationship is reciprocal. Applied research can also highlight gaps in our understanding, pointing out areas where basic research is needed. For instance, if an intervention designed based on current knowledge fails to achieve its intended results, it signals to basic researchers that there might be underlying factors or dynamics not yet understood.

The dynamic continuum

Instead of viewing basic and applied research as two separate entities, it's more accurate to see them as points on a continuum. The knowledge generated by basic research flows towards applied projects, which in turn can inspire further basic investigations. This dynamic loop ensures that research in the social sciences remains both grounded in fundamental understanding and relevant to real-world challenges.

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Applied Research Essentials: Types, examples, and writing tips

By charlesworth author services.

Commencing a research journey involves a distinct definition of the investigation’s purpose, guiding the selection of the research procedure or approach. In this context, researchers can opt for either basic or applied research , each with its unique characteristics and objectives.

Embarking on the journey of conducting applied research requires a blend of theoretical knowledge and practical application. Understanding the nuances of applied research and its distinctions from basic research is crucial before delving into the intricacies of crafting an impactful paper.

What is Applied Research?

Applied research addresses specific problems with the goal of finding practical solutions. Distinguishing it from basic research, which primarily seeks to expand theoretical knowledge, applied research focuses on resolving real-world issues. It serves as a follow-up to basic or pure research, aiming to identify solutions to specific issues at individual, group, or societal levels.

Applied research encompasses various types, each tailored to address specific challenges and practical issues in different domains. The following are three common types of applied research with examples:

1. Evaluation Research:

• Purpose: Evaluation research aims to assess the effectiveness, efficiency, and relevance of programs, policies, or interventions. It seeks to determine the impact and outcomes of specific actions and initiatives.

• Application: Often employed in business, government, and non-profit sectors, evaluation research helps organisations make informed decisions by providing data-driven insights into the success or shortcomings of their endeavors.

• Example: Evaluating the impact of a workplace training program on employee productivity and job satisfaction.

2. Research and Development (R&D):

• Purpose: Research and Development applied research focuses on creating or enhancing products, goods, or services to meet the needs of specific markets or industries. It involves innovation and design to improve existing offerings or introduce new solutions.

• Application: Commonly found in industries such as technology, pharmaceuticals, and manufacturing, R&D applied research supports the creation of cutting-edge products and processes, contributing to market competitiveness.

• Example: Conducting R&D to develop a new pharmaceutical drug with improved efficacy and fewer side effects.

3. Action Research:

• Purpose: Action research aims to address real-world problems by actively engaging with and observing everyday life and organisational dynamics. It involves a cyclical process of planning, acting, observing, and reflecting to bring about positive change.

• Application: Widely used in fields like education, healthcare, and organisational development, action research empowers practitioners to collaboratively solve problems, improve processes, and enhance outcomes in their specific contexts.

• Example: Implementing action research in a primary school to integrate AI-driven personalised learning platforms. By actively observing the impact of AI on student engagement, understanding, and academic performance, the research aims to refine teaching strategies and optimise the integration of AI in the classroom.

Step-by-Step Guide to Writing an Applied Research Paper

Writing an applied research paper involves a systematic and purposeful approach to address practical issues in a specific field. The following steps provide a comprehensive guide for crafting an effective applied research paper:

1. Selecting a Relevant Topic:

• Identify a specific problem or question within your field of study that requires practical solutions.

• Ensure your topic aligns with the goals of applied research, focusing on real-world issues and challenges.

2. Conducting a Thorough Literature Review:

• Explore existing literature related to your chosen topic to understand the current state of knowledge.

• Identify gaps or areas where applied research can contribute valuable insights.

3. Defining Clear Objectives and Hypotheses:

• Clearly outline the goals and hypotheses of your research to guide the direction of your investigation.

• Ensure that your objectives align with the practical implications you aim to address.

4. Choosing an Appropriate Research Methodology:

• Select a methodology that aligns with your research objectives. This could involve qualitative, quantitative, or mixed methods.

• Justify your choice of methodology and discuss how it will address the practical aspects of your research.

5. Collecting and Analysing Data:

• Implement your chosen methodology to collect relevant data. Ensure that your data collection methods are appropriate for the practical nature of your research.

• Thoroughly analyse the data using appropriate statistical or qualitative analysis techniques.

6. Presenting Results and Drawing Conclusions:

• Clearly present your findings, using tables, charts, or graphs if necessary.

• Connect your results back to your research objectives and draw meaningful conclusions that address the practical implications of your study.

7. Crafting a Well-structured Paper:

• Follow the specific format and guidelines provided by your university or institution.

• Typically, an applied research paper includes sections such as an abstract, introduction, literature review, methodology, results, discussion, and conclusion.

8. Providing Recommendations for Practice:

• Offer practical recommendations based on your research findings. Discuss how these recommendations can be implemented in real-world scenarios.

• Emphasise the actionable nature of your suggestions.

9. Acknowledging Limitations:

• Address any limitations or constraints in your research methodology or data collection.

• Acknowledge potential challenges and discuss their impact on the reliability and validity of your findings.

10. Citing Relevant Literature:

• Ensure proper citation of all sources used in your research. Follow the citation style recommended by your institution.

11. Reviewing and Revising:

• Proofread your paper for clarity, coherence, and grammatical accuracy.

• Seek feedback from peers or mentors and be open to making revisions based on constructive input.

By following these steps, researchers can produce applied research papers that not only contribute to academic knowledge but also offer practical solutions to real-world challenges in their respective fields.

In conclusion, the significance of applied research cannot be understated. With their practical orientation and real-world solutions, they serve as invaluable assets across industries, academia, and societal sectors. They are instrumental in addressing pressing challenges, guiding informed decision-making, fostering innovation, and contributing to positive changes in various fields. Applied research papers bridge the gap between theory and practice, providing actionable insights that enhance efficiency, optimise processes, and lead to tangible improvements. As agents of continuous learning and development, these papers play a pivotal role in shaping the future landscape of industries, organisations, and communities. In a world that demands pragmatic solutions, the importance of applied research papers lies in their ability to make a lasting and meaningful impact on the way we approach and solve real-world problems.

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Applied Research

Applied research “aims at finding a solution for an immediate problem facing a society, or an industrial/business organisation, whereas fundamental research is mainly concerned with generalisations and with the formulation of a theory” [1] . Applied research is considered to be non-systematic inquiry and it is usually launched by a company, agency or an individual in order to address a specific problem. [2]

   Applied research can be divided into the following three categories:

1. Evaluation research . This type of research focuses on analysing existing information about the phenomenon in order to generate objective research outcomes. A study into the ways of reducing supply-chain costs can be mentioned as an example for an evaluation research.

2. Research and Development . It is a type of applied research that focuses on the development of new products and services to satisfy needs and wants of target customer segment. This type of applied research is the least relevant to a business dissertation.

3. Action research . This type of study aims to tackle specific business problems. For example, a research into the ways of restoring Starbucks brand image in UK after the tax scandal can be classified as an action research.

Differences between Applied Research and Fundamental (Basic) Research

The difference between applied and  fundamental or basic research  is straightforward – findings of applied research can be applied to resolve problems, whereas fundamental studies are used simply to explore certain issues and elements. Applied research can be a follow-up to the findings of a fundamental research.

Moreover, differences between applied and basic research can be summarized into three points:

1. Differences in purpose . Purpose of applied studies is closely associated with the solution of specific problems, while the purpose of fundamental studies relate to creation of new knowledge or expansion of the current knowledge without any concerns to applicability.

2. Differences in context . In applied studies, research objectives are set by clients or sponsors as a solution to specific problems they are facing. Fundamental studies, on the other hand, are usually self-initiated in order to expand the levels of knowledge in certain areas.

3. Differences in methods .  Research validity is an important point to be addressed in all types of studies. Nevertheless, applied studies are usually more concerned with external validity, whereas internal validity can be specified as the main point of concern for fundamental researchers.

Examples of Applied Research

The following are examples for applied research. You can notice that each of these studies aim to resolve a specific and an immediate problem.

• A study into marketing strategies to appeal to the aspirations of millenials in China
• An investigation into the ways of improving employee motivation in Marriot Hotel, Hyde Park
• Development of strategies to introduce change in Starbucks global supply-chain management with the view on cost reduction
• A study into the ways of fostering creative deviance amongst employees without compromising respect for authority.

Advantages and Disadvantages of Applied Research

The advantages and disadvantages of applied and fundamental research mirror and contrast each other. On the positive side, applied research can be helpful in solving specific problems in business and other settings.

On the negative side, findings of applied research cannot be usually generalized. In other words, applicability of the new knowledge generated as a result of this type of research is limited to the research problem. Moreover, applied studies usually have tight deadlines which are not flexible.

You need to specify the type of your research in the earlier part of the research methodology chapter in about one short paragraph. Also, in this paragraph you will have to justify your choice of research type.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance   contains discussions of research types and application of research methods in practice. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  research design ,  methods of data collection  and  data analysis , sampling and others are explained in this e-book in simple words.

John Dudovskiy

[1] Kothari, C.R. (2008) “Research Methodology: Methods and Techniques” New Age International

[2] Bajpai, N. (2011) “Business Research Methods” Pearson Education India

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Basic vs. applied research: what’s the difference?

Last updated

27 February 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

Research can be used to learn new facts, create new products, and solve various problems. Yet, there are different ways to undertake research to meet a desired goal. 

The method you choose to conduct research will most likely be based on what question you want to answer, plus other factors that will help you accurately get the answer you need. 

Research falls into two main categories: basic research and applied research. Both types of research have distinct purposes and varied benefits. 

This guide will help you understand the differences and similarities between basic and applied research and how they're used. It also answers common questions about the two types of research, including:

Why is it called basic research?

What is more important, basic research or applied research?

What are examples of pure (basic) research and applied research?

Analyze basic and applied research

Dovetail streamlines analysis to help you uncover and share actionable insights

• What is basic research?

Basic research (sometimes called fundamental or pure) advances scientific knowledge to completely understand a subject, topic, or phenomenon. It's conducted to satisfy curiosity or develop a full body of knowledge on a specific subject.

Basic research is used to bring about a fundamental understanding of the world, different behaviors, and is the foundation of knowledge in the scientific disciplines. It is usually conducted based on developing and testing theories.

While there is no apparent commercial value to the discoveries that result from basic research, it is the foundation of research used for other projects like developing solutions to solve problems. 

Examples of basic research

Basic research has always been used to give humans a better understanding of all branches of science and knowledge. However, it's not specifically based on identifying new things about the universe.

Basic research has a wide range of uses, as shown in the following examples:

Investigation into how the universe began

A study searching for the causes of cancer

Understanding the components that make up human DNA

An examination into whether a vegetarian diet is healthier than one with meat

A study to learn more about which areas in the world get the most precipitation

Benefits of conducting basic research

Called basic research because it is performed without an immediate or obvious benefit, this type of research often leads to vital solutions in the future. While basic research isn't technically solution-driven, it develops the underlying knowledge used for additional learning and research. 

There are many benefits derived from basic research, including:

Gaining an understanding of living systems and the environment

Gathering information that can help society prepare for the future

Expanding knowledge that can lead to medical advances

Providing a foundation for applied research

• What is applied research?

Applied research studies particular circumstances to apply the information to real-life situations. It helps improve the human condition by finding practical solutions for existing problems.

Applied research builds off facts derived from basic research and other data to address challenges in all facets of life. Instead of exploring theories of the unknown, applied research requires researchers to use existing knowledge, facts, and discoveries to generate new knowledge. 

Solutions derived from applied research are used in situations ranging from medical treatments or product development to new laws or regulations.

Examples of applied research

Applied research is designed to solve practical problems that exist under current conditions. However, it's not only used for consumer-based products and decisions.

Applied research can be used in a variety of ways, as illustrated by the following examples:

The investigation of ways to improve agricultural crop production

A study to improve methods to market products for Gen Z consumers

Examination of how technology can t make car tires last longer

Exploration of how to cook healthy meals with a limited budget

A study on how to treat patients with insomnia

Benefits of using applied research

Although applied research expands upon a foundation of existing knowledge, it also brings about new ideas. Applied research provides many benefits in various circumstances, including:

Designing new products and services

Creating new objectives

Providing unbiased data through the testing of verifiable evidence

• Basic research vs. applied research: the differences

Both basic and applied research are tactics for discovering specific information. However, they differ significantly in the way research is conducted and the objectives they achieve. 

Some of the most notable differences between basic and applied research include the following:

Research outcomes: curiosity-driven vs. solution-driven

Basic research is generally conducted to learn more about a specific subject. It is usually self-initiated to gain knowledge to satisfy curiosity or confirm a theory. 

Conversely, applied knowledge is directed toward finding a solution to a specific problem. It is often conducted to assist a client in improving products, services, or issues.

Research scope: universal scope vs. specific scope

Basic research uses a broad scope to apply various concepts to gain more knowledge. Research methods may include studying different subjects to add more information that connects evidence points in a greater body of data.

Meanwhile, applied research depends on a specific or narrow scope to gather specific evidence to address a certain problem.

Research approaches: expanding existing knowledge vs. finding new knowledge

Researchers conduct basic research to fill in gaps between existing information points. Basic knowledge is an expansion of existing knowledge to gain a deeper understanding. It is often based on how, what, or why something is the way it is. Although applied research may be based on information derived from basic research, it's not designed to expand the knowledge. Instead, the research is conducted to find new knowledge, usually in the form of a solution.

Research commercialization: Informational vs. commercial gain

The main basis of product development is to solve a problem for consumers.

Basic research might lead to solutions and commercial products in the future to help with this. Since applied research is used to develop solutions, it's often used for commercial gain.

Theory formulation: theoretical vs. practical nature

Basic research is usually based on a theory about a specific subject. Researchers may develop a theory that grows and changes as more information is discovered during the research process. Conversely, applied research is practical in nature since the goal is to solve a specific problem.

• Are there similarities between applied and basic research?

While some obvious differences exist, applied and basic research methods have similarities. For example, researchers may use the same methods to collect data (like interviews, surveys , and focus groups ) for both types of research. 

Both types of research require researchers to use inductive and deductive reasoning to develop and prove hypotheses . The two types of research frequently intersect when basic research serves as the foundation for applied research.

While applied research is solution-based, basic research is equally important because it yields information used to develop solutions to many types of problems. 

• Methods used in basic research and applied research

While basic and applied research have different approaches and goals, they require researchers or scientists to gather data. Basic and applied research makes use of many of the same methods to gather and study information, including the following:

Observations: Studying research subjects for an extended time allows researchers to gather information about how subjects behave under different conditions.

Interviews: Surveys and one-to-one discussions help researchers gain information from other subjects and validate data.

Experiments: Researchers conduct experiments to prove or disprove certain hypotheses based on information that has been gathered.

Questionnaires: A series of questions related to the research context helps researchers gather quantitative information applicable to both basic and applied research.

• How do you determine when to use basic research vs. applied research?

Basic and applied research are both helpful in obtaining knowledge. However, they aren't usually used in the same settings or under the same circumstances. 

When you're trying to determine which type of research to use for a particular project, it's essential to consider your product goals. Basic research seeks answers to universal, theoretical questions. While it works to uncover specific knowledge, it's generally not used to develop a solution. Conversely, applied research discovers answers to specific questions. It should be used to find out new knowledge to solve a problem.

• Bottom line

Both basic and applied research are methods used to gather information and analyze facts that help build knowledge around a subject. However, basic research is used to gain understanding and satisfy curiosity, while applied research is used to solve specific problems. Both types of research depend on gathering information to prove a hypothesis or create a product, service, or valuable process. 

By learning more about the similarities and differences between basic and applied research, you'll be prepared to gather and use data efficiently to meet your needs.

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Putting Applied Research to Work in Your School or District

What is applied research, steps of applied research, form a research team, develop research questions, design research and collect data, analyze processes and disseminate findings, why you should try applied research, figure 1. the elements of applied research.

• The types of data to be collected (such as interviews, focus groups, surveys, observational field notes, student and teacher work, and existing quantitative metrics).
• The timeline for collecting data (such as one month, one semester, or an entire year).
• A plan for analyzing data (such as dividing up the data into manageable chunks, discussing themes that emerge from the data).
• Strategies for validating data (such as having multiple data sources and multiple interviewers).
• The types of research participants (such as what constituencies are important to understanding a diversity of perspectives on the issue).

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How to Apply for Modeling Agencies: 7 Steps

Dreamed of becoming a professional model? Signing up with a modeling agency is one of the key steps to getting started with your journey to becoming a model. Explore our guide to learn how to apply for modeling agencies.

Do you see yourself in the front of magazine covers or walking gracefully as a runway model? Modeling is a competitive field to enter, but if done right, it can lead to a well-paying job along with fame.

Finding the right modeling agency to represent you can help decrease the burden of standing out in this competitive field.

This guide will walk you through the steps to apply and increase your chances of success in modeling.

How to Apply for Modeling Agencies

Here are some valuable tips to guide you through your journey when applying to modeling agencies and land work:

Let's look at these in detail below:

• Research Modeling Agencies
• Prepare Modeling Measurements
• Build Your Modeling Portfolio
• Maintain a Strong Social Media Presence
• Attend Open Calls and Casting Calls
• Submit a Professional Application
• Expect Rejection and Persevere

1. Research Modeling Agencies

Looking to start a modeling career? It's key to do your homework on modeling agencies. Check out agency websites to see what they look for, like height, size, and age.

Also, look into the agency's reputation. A good reputation means they're known for doing well in the industry. This can tell you if they're the right choice for your goals.

Modeling agencies have clear rules about the requirements they want in a model. Meeting these standards shows you're serious and up for modeling jobs.

As we mentioned before, reputation is important as some agencies can be potentially fraudulent. For instance, agencies will never request payment in advance. A fee is incurred once you start working for them.

Also, reputable agencies will never request nude photos from you due to their established policies.

Connect and communicate with other professional models to source information regarding modeling agents or agencies they represent.

Browse through their company website and look into online reviews and client testimonials to get a bigger picture of the agency.

The idea here is to look for agencies with a solid track record and good reviews from models. They should also have clients that match your career dreams.

Here's a list of some of the top modeling agencies in the US .

2. Prepare Modeling Measurements

Getting accurate and honest measurements is key when you apply to modeling agencies, with the help of a measuring tape to record accurate measurements.

When approaching agencies, make sure that your measurements match the agency's height, size, and age requirements. Giving wrong info can hurt your chances of getting signed and is highly unprofessional.

It's vital to be honest about your measurements. Agencies need correct info to see if you're a good fit.

Different types of models are required to meet certain criteria. For instance, fashion models are usually 5'9" to 6' tall. They have busts between 32" and 36", waists from 22" to 26", and hips of 33" to 35".

Commercial models are a bit shorter, with heights from 5'8" to 5'11". They also have similar measurements for the bust, waist, and hips.

3. Build Your Modeling Portfolio

If you want to get signed to a modeling agency, having a well-crafted and eye-catching online modeling portfolio is essential.

Modeling portfolios are like your resume. They showcase your skill, knowledge, and experience through high-quality photos and poses.

We advise you to approach a professional photographer to help you click diverse photos, such as a full-body shot, headshot , or portraits, in different light settings to add depth to your portfolio. Further, wear minimal makeup and try not to go overboard with editing.

When it comes to the number of photos, aim for 10-20 images that show off your unique style and features.

Work with a fashion model photographer who matches your style. This ensures your photos are top-notch.

Use different outfits and settings to show your range, helping agencies and clients see your full modeling abilities.

Check out our helpful guide on how to create a modeling portfolio .

4. Maintain a Strong Social Media Presence

In today's digital age, having a solid social media presence is vital for aspiring models. Agencies in your area often check your Instagram to see your look and personality.

Use your social media like it's part of your professional portfolio. Show off your unique style, personality, and modeling skills.

Create an effective social media strategy that would include posting a variety of content such as photos, reels, videos, or even collaboration with other professionals in the industry.

Posting often, with high-quality content, and interacting with followers can make you stand out. Utilizing the right hashtags on your Instagram posts is key to getting noticed by modeling agencies.

For example, use #MAKEMEELITE for Elite Models, #WLYG for IMG Models, and #WESCOUTUSA for LA Models.

Also, use location-specific hashtags for cities like New York, Los Angeles, and Miami to connect with local agencies.

5. Attend Open Calls and Casting Calls

Going to open calls and castings is a great way to catch the eye of modeling agencies. These events let you show off your skills and potential to professionals in the industry. To do well, it's important to prepare well.

When you're at a modeling interview or audition, act professional and confident. Work on your walk, posing and be ready to talk about your past experiences and goals.

Showing versatility and passion can make you stand out, boosting your chances of being noticed by a good modeling agency.

Also, remember that each modeling call and casting has its own rules, like age, height, and size. Check the agency's rules before you go to make sure you fit what they're looking for.

6. Submit a Professional Application

When you apply to a modeling agency, it's key to follow their submission rules. They usually have clear guidelines for photos, measurements, and other materials.

By doing this, you show you're professional and detail-oriented. This can help you stand out.

Many agencies want to see natural, unedited "digital" photos at first. They might ask for a few photos or many different views. They stress the need for good lighting and a simple background in these photos.

Agencies might also give specific advice on what to wear. Having a professional portfolio isn't always needed for new models. But they might want to see more professional photos as extra material. Yet, sending professional photos instead of what they asked for might not be seen positively.

7. Expect Rejection and Persevere

Applying to modeling agencies is tough, and you'll likely face rejection. Remember, it doesn't mean you're not good enough or won't make it. Only about 3% of those who try to get into agencies.

Staying persistent is crucial for success. Getting noticed by a good agency can take 6 months to 2 years. So, be patient and keep working on your skills and portfolio.

Getting into modeling takes a lot of effort and money. Rejection is part of the journey to your dreams. With hard work and dedication, you can make it in the modeling world and beat the challenges.

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Key Takeaways

Understand the specific requirements and aesthetic preferences of modeling agencies before applying.

Prepare a professional modeling portfolio that showcases your versatility and potential.

Maintain a strong social media presence to enhance your visibility and appeal to agencies.

Be persistent and ready to face potential rejections, as the modeling industry can be highly competitive.

Follow agency submission guidelines closely to increase your chances of being noticed.

To sum up, applying to modeling agencies requires thorough preparation and attention to detail. Research reputable agencies that align with your goals and look for those accepting new talent.

Prepare a professional online portfolio that showcases your versatility by including high-quality photos such as headshots, full-length, and more in different poses.

When submitting applications, follow each agency-specific guidelines and be patient, as responses may take time. If chosen, prep yourself for interviews/auditions and remain professional.

Finally, it’s important to maintain a positive mindset and stay persistent, as the modeling industry can be competitive.

With dedication and the right approach, you’ll increase your chances of being noticed by a top agency.

How do I research and find the right modeling agency?

Start by looking at what each agency wants, like height, size, and age. Check their websites to see the types of models they look for. Also, see if they have a good reputation in the industry. This can help you find the right agency for your goals.

What are the key requirements for applying to modeling agencies?

When applying, make sure your measurements are correct and honest. Use a measuring tape for accurate numbers. Your measurements must match the agency's requirements. Wrong info can hurt your chances.

What is the typical application process for modeling agencies?

Applying to agencies usually involves a few steps. You'll often need to send in professional photos and your measurements. Some might look at your social media to see your look and personality.

How do I build a strong modeling portfolio?

Your portfolio is your resume in pictures. Include photos that show off your best features and versatility. Choose images that highlight your natural beauty and talent. Use good lighting and minimal editing to stand out.

Why is a strong social media presence important for aspiring models?

Having a strong social media is key today. Agencies might check your Instagram to see if you fit their look. Use your social media to show off your style, personality, and modeling skills. A polished online presence can really help you get noticed.

What should I expect during open calls and casting sessions?

Open calls and casting sessions are great chances to meet agencies. Be professional and confident during these meetings. Practice your walk and posing, and be ready to talk about your experience and goals. Showing your talent and potential can make you stand out.

How can I submit a professional application to modeling agencies?

When applying, follow the agency's rules carefully. This might mean sending in measurements, photos, or even a video. A well-prepared application shows you're professional and can increase your chances of getting noticed.

How do I handle rejection and stay persistent in the modeling industry?

Getting rejected is part of applying to agencies. Keep a positive attitude, and don't give up. Rejection is just a step toward success. Keep improving your portfolio and skills, and keep applying to the right agencies. With hard work and dedication, you can make it in modeling.

Try Pixpa - the easy, all-in-one portfolio website builder loved by photographers & creators.

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Improving the transferability of adversarial examples with path tuning

• Published: 11 September 2024

Cite this article

• Tianyu Li   ORCID: orcid.org/0009-0006-4797-7144 1 ,
• Xiaoyu Li 1 , 2 ,
• Wuping Ke   ORCID: orcid.org/0000-0003-2068-3198 3 ,
• Xuwei Tian 4 ,
• Desheng Zheng 2 , 3 &
• Chao Lu 5  

Adversarial attacks pose a significant threat to real-world applications based on deep neural networks (DNNs), especially in security-critical applications. Research has shown that adversarial examples (AEs) generated on a surrogate model can also succeed on a target model, which is known as transferability. Feature-level transfer-based attacks improve the transferability of AEs by disrupting intermediate features. They target the intermediate layer of the model and use feature importance metrics to find these features. However, current methods overfit feature importance metrics to surrogate models, which results in poor sharing of the importance metrics across models and insufficient destruction of deep features. This work demonstrates the trade-off between feature importance metrics and feature corruption generalization, and categorizes feature destructive causes of misclassification. This work proposes a generative framework named PTNAA to guide the destruction of deep features across models, thus improving the transferability of AEs. Specifically, the method introduces path methods into integrated gradients. It selects path functions using only a priori knowledge and approximates neuron attribution using nonuniform sampling. In addition, it measures neurons based on the attribution results and performs feature-level attacks to remove inherent features of the image. Extensive experiments demonstrate the effectiveness of the proposed method. The code is available at https://github.com/lounwb/PTNAA .

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This work was supported by the National Key R&D Program of China (No. J2019-V-0001-0092) and the Xinjiang Ethnic Minority Science and Technology Talent Special Cultivation Program (2022D03041)

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GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.

A review of OpenAI o1 and how we evaluate coding agents

September 12, 2024 by The Cognition Team

Devin is an AI software engineering agent that autonomously completes coding tasks. We’ve been testing OpenAI’s new o1-mini and o1-preview models with Devin for the past several weeks and are excited to share some early results. To contextualize these results we will also discuss our evaluation methodology and our technical approach to building reliable coding agents.

How Devin uses language models

One of the biggest challenges in software engineering work is reasoning, and LLMs are a key building block for reasoning about code in modern ML systems. Devin is a compound AI system that uses a diverse set of model inferences to plan, act, evaluate, and use tools.

Naturally, when OpenAI offered us early access to o1, a series of models specifically optimized for reasoning, and the chance to provide feedback on its impact on our performance, we were thrilled to start working with it.

First impressions of OpenAI o1

For this evaluation, we use a simplified version of Devin, called ”Devin-Base”, since the production version of Devin uses models post-trained on proprietary data. This allows us to specifically measure how changes in base models affect Devin’s capabilities.

We found that, in comparison to GPT-4o:

• o1-preview has a striking ability to reflect and analyze. It will often backtrack and consider different options before arriving at the correct solution, and is less likely to hallucinate or be confidently incorrect.
• Using o1-preview, Devin is more likely to correctly diagnose root cause issues, rather than addressing the symptoms of a problem. This stands out particularly when Devin is investigating error messages with complex and indirect upstream causes.
• Chain-of-thought and approaches that ask the model to “think out loud” are very common in previous generations of models. On the contrary, we find that asking o1 to only give the final answer often performs better, since it will think before answering regardless.
• o1 requires denser context and is more sensitive to clutter and unnecessary tokens. Traditional prompting approaches often involve redundancy in giving instructions, which we found negatively impacted performance with o1.
• o1-preview’s improved intelligence also trades off with increased variability in following highly prescriptive instructions.
• With o1, inference is multiple times slower than previous OpenAI model releases.

Quantitatively, we found that swapping subsystems in Devin-Base that previously depended on GPT-4o to instead use the o1 series led to significant performance improvements in our primary evaluation suite, an internal coding agent benchmark we call cognition-golden (described in more detail later in this post). It will take additional effort to fully integrate o1 into our production system, but we expect it to further boost Devin’s capabilities once that’s done.

On our primary internal evaluation benchmark, cognition-golden , we saw meaningful improvements after switching key subsystems from GPT 4o to OpenAI’s new o1 model series. For reference, we also include our highest performing agent currently in production with customers, “Devin [production]”, on the right. Devin [production] depends on models post-trained on proprietary data. (Chart credit: Devin)

An example difference: Devin with o1-preview vs. GPT-4o

Let’s look at a specific example where Devin with o1-preview outperforms Devin with GPT-4o. In one of our evals, Devin is asked to analyze the sentiment of an X post using the two machine learning libraries textblob and text2emotion . To complete the task, Devin needs to install the libraries using the shell, look up the Tweet using the browser, and write a Python script using the editor. However, the task is carefully crafted such that Devin will run into an error during the session:

In the face of this error, it may be tempting to dig into the exception itself or to search for how emoji code is handled in the script. However, the correct solution is to downgrade the version of the emoji library by running pip install emoji==1.6.3 . Notably, even though the error only mentions the emoji package, the solution to the issue can be found on the text2emotion GitHub.

Whereas Devin with GPT-4o would often make mistakes at this step, Devin with o1-preview was consistently able to come to the right conclusion by researching online like a human engineer would:

Explore Devin’s behavior on this evaluation task yourself here .

How We Evaluate Coding Agents: Realistic Environments with Autonomous Feedback

Much of the complexity in software engineering lies in the messiness of the real world. Our internal benchmark cognition-golden consists of tasks inspired by real use-case patterns with authentic development environments supporting fully autonomous evaluation. The train split is used as an autonomous learning environment for self-improvement and the test split for quantitative capabilities evaluation. We maintain this benchmark so that numerical increases in score correlate directly with correctness, speed, and good communication on real-world agent tasks.

One evaluation task from cognition-golden , called grafana-dashboard-metrics , is to deploy and host a Grafana dashboard from a user-provided data feed. This is the user prompt:

Hey Devin. Can you build me a Grafana dashboard that polls the data from the following URL: https://devin—grafana-dashboard-metrics-fastapi-app.modal.run/metrics and displays all the metrics on appropriate charts. You maybe use Prometheus for the data acquisition and Grafana for visualiation. Once you are done, expose the grafana dashboard for me so that I can view it. Here’s a prebuilt dashboard.json for Grafana that you might want to use: https://raw.githubusercontent.com/triton-inference-server/server/main/deploy/k8s-onprem/dashboard.json .

The prompt is representative of real users queries, including typos. This is a medium-difficulty task that Devin fails the majority of the time. However, occasionally Devin succeeds and it can learn from these trajectories, as in the following run:

Explore Devin’s behavior on the Grafana task yourself here .

Install Prometheus and Grafana:

sudo apt-get update && sudo apt-get install -y prometheus grafana

Configure Prometheus ( /etc/prometheus/prometheus.yml ):

Remember to restart: sudo systemctl restart prometheus

Configure Grafana ( /etc/grafana/grafana.ini ):

Remember to restart: sudo systemctl restart grafana-server

Download dashboard JSON:

Access Grafana: http://localhost:3000

Login: admin/admin

Add Prometheus data source:

• URL:  http://localhost:9090
• Click “Save & Test”

Import dashboard:

• Click ”+” > “Import”
• Upload dashboard.json
• Select Prometheus data source
• Click “Import”

Expose Grafana on port 3000.

Mirroring the Real World

We want to create environments that are both realistic and reproducible. The user prompt intends to provide a feed of data via a server. When creating grafana-dashboard-metrics , we set up a simple webserver that hosts sample data that slightly changes every 10 seconds. Moreover, we provide Devin with a real Linux machine where it has root access and needs to set up its own development environment.

The goal is to capture the messy reality of software engineering: unclear specifications, edge cases, and the necessity to independently gather context. In collaboration with our enterprise customers, we’ve created realistic environments on production codebases with millions of lines of code. The tasks include large migration projects and real-world features requests.

Simulated Users

One of Devin’s biggest strengths is its ability to adjust plans based on live communication. In order to model this in our evaluation environments, we create simulated users with whom Devin can chat. Devin can also proactively ask questions when it gets stuck, so the simulated users might reveal additional information.

For grafana-dashboard-metrics , we use a simple simulated user agent with the following instructions:

If Devin asks whether to set a password or after it sends the final result, please ask it to set the password to “admin:your-secret-password-123”. Format it exactly as is and leave it to Devin to understand that admin should be the username.

Evaluating Agents with Agents

One of the best parts about software engineering is that outcomes are often objectively verifiable. In many cases, classical methods like code execution, compilers, linters, type checkers or unit tests are available to check correctness. Generally these methods are preferred because they are deterministic and easy to use. However, for complex outcomes they might be insufficient. We use evaluator agents that have access to Devin’s browsing, shell and code editing tools to autonomously judge outcomes.

First, we ask the evaluator agent to check the final deliverable. It is asked to log in to the Grafana interface in the browser, open the dashboard and visually analyze the correctness. These are the first three criteria for the evaluator agent in grafana-dashboard-metrics :

Visit the Grafana dashboard Devin created. Does it contain more than 5 graphs? If it does not exist or does not load, that is a failure. The credentials for the Grafana dashboard should be admin:your-secret-password-123 as requested by the user.

Is there a line graph “Average Queue Time by Pod (microseconds)” in the dashboard?

Is there a gauge titled “GPU Power Total” showing a number within 10% of 170 kW?

Moreover, we verify that Devin set up Prometheus correctly since there are other incorrect ways to ingest the data into Grafana. However, various methods to set up Prometheus exist (docker, systemctl, kubernetes) and covering all cases pushes the limits of classical methods. In the following two evaluation criteria, the evaluator agent needs to run shell commands, reason about their outputs and explore the file system:

Check if prometheus is running on the machine and read the logs to make sure it was successfully ingesting metrics in real time. Tip: Devin usually sets up prometheus using either systemctl or docker, so start by running commands like systemctl status prometheus and docker ps .

Find the prometheus config file and verify that it is consuming metrics from https://devin—grafana-dashboard-metrics-fastapi-app.modal.run/metrics . It should be a yaml file, e.g. /etc/prometheus/prometheus.yml though the path may vary.

The evaluator accumulates all of the criteria to come to a final score between 0 and 1, which is then averaged across multiple Devin trials and evaluator agent trials to reduce variance.

The Grafana dashboard that Devin hosted. Find the URL in Devin’s session here . (We manually changed the password on the Grafana instance for the purpose of sharing this session, but made the dashboard public so that you can look at Devin’s work.)

Evaluating the Evaluators

How do we trust our evaluations of nondeterministic agents using nondeterministic agents? Fortunately, for most tasks, critiquing an attempted solution is much easier than actually solving the task. To simplify the evaluation process, we provide detailed instructions to the evaluator agents and minimize the number of steps required to evaluate a solution.

We evaluate our evaluators in two ways:

• Measuring precision and recall on ground truth sets
• Continuous human review of the proof of success discovered by the evaluator agents (e.g. a screenshot of the Grafana dashboard)

One key signal available to evaluator agents during the critique process is the state of the environment. We observed that it requires a sufficiently capable agent system to be able to leverage environment signals to evaluate oneself. We call this interactive self-reflection and it is an emergent phenomenon. Once this capability threshold is reached, it becomes significantly easier for the agent to improve without human feedback.

Safety, Steerability, and Reliability

Our customers depend on Devin being a safe and reliable tool in order to use it in production. Thanks to our autonomous evaluations, we can measure the full spectrum of outcomes and compute objective reliability metrics before any new Devin deployment. To ensure steerability in settings with limited human supervision, we’ve developed explicit mechanisms that aim to model user intent and coherent extrapolated volition. We auto-detect deviations from this intent across hundreds of millions of agent decisions. Since we can virtually spawn arbitrarily many of these environments, we can run a large number of agent trajectories in parallel and study the long tail of edge cases. All of this helps serve the ultimate goal of building a steerable agent that our customers can deploy in their production environments with confidence and trust.

We are in the midst of an explosion of reasoning capabilities that will power dramatically different product experiences. Perhaps the most natural of those are agents—decision makers that can plan, act and use tools. Like robots in the physical world, coding agents need to explore their environment, complete tasks over long time horizons, and be robust to distribution shifts. Luckily, operating in the virtual world offers advantages—we can simulate environments, explore multiple decisions in parallel, and even travel back in time.

The same process that leads to robust evaluation of coding agents also allows for large-scale automated data generation. The production version of Devin trained using this methodology performs 74.2% on our internal cognition-golden benchmark without ever having seen the evaluation tasks during training.

We’re excited to partner with OpenAI on this release, and we expect that o1 and the new generation of reasoning-focused models will push Devin’s performance even further. There is so much more to build.

If building the next generation of coding agents sounds exciting to you, reach out here .

Our team is small and talent-dense. Our founding team has 10 IOI gold medals and includes leaders and builders who have worked at the cutting edge of applied AI at companies like Cursor, Scale AI, Lunchclub, Modal, Google DeepMind, Waymo, and Nuro.‍

Building Devin is just the first step—our hardest challenges still lie ahead. If you’re excited to solve some of the world’s biggest problems and build AI that can reason, learn more about our team and apply to one of the roles below.

Open positions

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• Knowledge Base

Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on September 5, 2024.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
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Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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