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Case Study – Methods, Examples and Guide

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A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• Last Updated: Jul 22, 2024 8:15 PM
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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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What is case study research?

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Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

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Dovetail streamlines case study research to help you uncover and share actionable insights

• What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

• When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

• What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

• What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

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• Volume 21, Issue 1
• What is a case study?
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• Roberta Heale 1 ,
• Alison Twycross 2
• 1 School of Nursing , Laurentian University , Sudbury , Ontario , Canada
• 2 School of Health and Social Care , London South Bank University , London , UK
• Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Sudbury, ON P3E2C6, Canada; rheale{at}laurentian.ca

https://doi.org/10.1136/eb-2017-102845

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What is it?

Case study is a research methodology, typically seen in social and life sciences. There is no one definition of case study research. 1 However, very simply… ‘a case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. 1 A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 2

Often there are several similar cases to consider such as educational or social service programmes that are delivered from a number of locations. Although similar, they are complex and have unique features. In these circumstances, the evaluation of several, similar cases will provide a better answer to a research question than if only one case is examined, hence the multiple-case study. Stake asserts that the cases are grouped and viewed as one entity, called the quintain . 6  ‘We study what is similar and different about the cases to understand the quintain better’. 6

The steps when using case study methodology are the same as for other types of research. 6 The first step is defining the single case or identifying a group of similar cases that can then be incorporated into a multiple-case study. A search to determine what is known about the case(s) is typically conducted. This may include a review of the literature, grey literature, media, reports and more, which serves to establish a basic understanding of the cases and informs the development of research questions. Data in case studies are often, but not exclusively, qualitative in nature. In multiple-case studies, analysis within cases and across cases is conducted. Themes arise from the analyses and assertions about the cases as a whole, or the quintain, emerge. 6

Benefits and limitations of case studies

If a researcher wants to study a specific phenomenon arising from a particular entity, then a single-case study is warranted and will allow for a in-depth understanding of the single phenomenon and, as discussed above, would involve collecting several different types of data. This is illustrated in example 1 below.

Using a multiple-case research study allows for a more in-depth understanding of the cases as a unit, through comparison of similarities and differences of the individual cases embedded within the quintain. Evidence arising from multiple-case studies is often stronger and more reliable than from single-case research. Multiple-case studies allow for more comprehensive exploration of research questions and theory development. 6

Despite the advantages of case studies, there are limitations. The sheer volume of data is difficult to organise and data analysis and integration strategies need to be carefully thought through. There is also sometimes a temptation to veer away from the research focus. 2 Reporting of findings from multiple-case research studies is also challenging at times, 1 particularly in relation to the word limits for some journal papers.

Examples of case studies

Example 1: nurses’ paediatric pain management practices.

One of the authors of this paper (AT) has used a case study approach to explore nurses’ paediatric pain management practices. This involved collecting several datasets:

Observational data to gain a picture about actual pain management practices.

Questionnaire data about nurses’ knowledge about paediatric pain management practices and how well they felt they managed pain in children.

Questionnaire data about how critical nurses perceived pain management tasks to be.

These datasets were analysed separately and then compared 7–9 and demonstrated that nurses’ level of theoretical did not impact on the quality of their pain management practices. 7 Nor did individual nurse’s perceptions of how critical a task was effect the likelihood of them carrying out this task in practice. 8 There was also a difference in self-reported and observed practices 9 ; actual (observed) practices did not confirm to best practice guidelines, whereas self-reported practices tended to.

Example 2: quality of care for complex patients at Nurse Practitioner-Led Clinics (NPLCs)

The other author of this paper (RH) has conducted a multiple-case study to determine the quality of care for patients with complex clinical presentations in NPLCs in Ontario, Canada. 10 Five NPLCs served as individual cases that, together, represented the quatrain. Three types of data were collected including:

Review of documentation related to the NPLC model (media, annual reports, research articles, grey literature and regulatory legislation).

Interviews with nurse practitioners (NPs) practising at the five NPLCs to determine their perceptions of the impact of the NPLC model on the quality of care provided to patients with multimorbidity.

Chart audits conducted at the five NPLCs to determine the extent to which evidence-based guidelines were followed for patients with diabetes and at least one other chronic condition.

The three sources of data collected from the five NPLCs were analysed and themes arose related to the quality of care for complex patients at NPLCs. The multiple-case study confirmed that nurse practitioners are the primary care providers at the NPLCs, and this positively impacts the quality of care for patients with multimorbidity. Healthcare policy, such as lack of an increase in salary for NPs for 10 years, has resulted in issues in recruitment and retention of NPs at NPLCs. This, along with insufficient resources in the communities where NPLCs are located and high patient vulnerability at NPLCs, have a negative impact on the quality of care. 10

These examples illustrate how collecting data about a single case or multiple cases helps us to better understand the phenomenon in question. Case study methodology serves to provide a framework for evaluation and analysis of complex issues. It shines a light on the holistic nature of nursing practice and offers a perspective that informs improved patient care.

• Gustafsson J
• Calanzaro M
• Sandelowski M

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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Methodology or method? A critical review of qualitative case study reports

Despite on-going debate about credibility, and reported limitations in comparison to other approaches, case study is an increasingly popular approach among qualitative researchers. We critically analysed the methodological descriptions of published case studies. Three high-impact qualitative methods journals were searched to locate case studies published in the past 5 years; 34 were selected for analysis. Articles were categorized as health and health services ( n= 12), social sciences and anthropology ( n= 7), or methods ( n= 15) case studies. The articles were reviewed using an adapted version of established criteria to determine whether adequate methodological justification was present, and if study aims, methods, and reported findings were consistent with a qualitative case study approach. Findings were grouped into five themes outlining key methodological issues: case study methodology or method, case of something particular and case selection, contextually bound case study, researcher and case interactions and triangulation, and study design inconsistent with methodology reported. Improved reporting of case studies by qualitative researchers will advance the methodology for the benefit of researchers and practitioners.

Case study research is an increasingly popular approach among qualitative researchers (Thomas, 2011 ). Several prominent authors have contributed to methodological developments, which has increased the popularity of case study approaches across disciplines (Creswell, 2013b ; Denzin & Lincoln, 2011b ; Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Current qualitative case study approaches are shaped by paradigm, study design, and selection of methods, and, as a result, case studies in the published literature vary. Differences between published case studies can make it difficult for researchers to define and understand case study as a methodology.

Experienced qualitative researchers have identified case study research as a stand-alone qualitative approach (Denzin & Lincoln, 2011b ). Case study research has a level of flexibility that is not readily offered by other qualitative approaches such as grounded theory or phenomenology. Case studies are designed to suit the case and research question and published case studies demonstrate wide diversity in study design. There are two popular case study approaches in qualitative research. The first, proposed by Stake ( 1995 ) and Merriam ( 2009 ), is situated in a social constructivist paradigm, whereas the second, by Yin ( 2012 ), Flyvbjerg ( 2011 ), and Eisenhardt ( 1989 ), approaches case study from a post-positivist viewpoint. Scholarship from both schools of inquiry has contributed to the popularity of case study and development of theoretical frameworks and principles that characterize the methodology.

The diversity of case studies reported in the published literature, and on-going debates about credibility and the use of case study in qualitative research practice, suggests that differences in perspectives on case study methodology may prevent researchers from developing a mutual understanding of practice and rigour. In addition, discussion about case study limitations has led some authors to query whether case study is indeed a methodology (Luck, Jackson, & Usher, 2006 ; Meyer, 2001 ; Thomas, 2010 ; Tight, 2010 ). Methodological discussion of qualitative case study research is timely, and a review is required to analyse and understand how this methodology is applied in the qualitative research literature. The aims of this study were to review methodological descriptions of published qualitative case studies, to review how the case study methodological approach was applied, and to identify issues that need to be addressed by researchers, editors, and reviewers. An outline of the current definitions of case study and an overview of the issues proposed in the qualitative methodological literature are provided to set the scene for the review.

Definitions of qualitative case study research

Case study research is an investigation and analysis of a single or collective case, intended to capture the complexity of the object of study (Stake, 1995 ). Qualitative case study research, as described by Stake ( 1995 ), draws together “naturalistic, holistic, ethnographic, phenomenological, and biographic research methods” in a bricoleur design, or in his words, “a palette of methods” (Stake, 1995 , pp. xi–xii). Case study methodology maintains deep connections to core values and intentions and is “particularistic, descriptive and heuristic” (Merriam, 2009 , p. 46).

As a study design, case study is defined by interest in individual cases rather than the methods of inquiry used. The selection of methods is informed by researcher and case intuition and makes use of naturally occurring sources of knowledge, such as people or observations of interactions that occur in the physical space (Stake, 1998 ). Thomas ( 2011 ) suggested that “analytical eclecticism” is a defining factor (p. 512). Multiple data collection and analysis methods are adopted to further develop and understand the case, shaped by context and emergent data (Stake, 1995 ). This qualitative approach “explores a real-life, contemporary bounded system (a case ) or multiple bounded systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information … and reports a case description and case themes ” (Creswell, 2013b , p. 97). Case study research has been defined by the unit of analysis, the process of study, and the outcome or end product, all essentially the case (Merriam, 2009 ).

The case is an object to be studied for an identified reason that is peculiar or particular. Classification of the case and case selection procedures informs development of the study design and clarifies the research question. Stake ( 1995 ) proposed three types of cases and study design frameworks. These include the intrinsic case, the instrumental case, and the collective instrumental case. The intrinsic case is used to understand the particulars of a single case, rather than what it represents. An instrumental case study provides insight on an issue or is used to refine theory. The case is selected to advance understanding of the object of interest. A collective refers to an instrumental case which is studied as multiple, nested cases, observed in unison, parallel, or sequential order. More than one case can be simultaneously studied; however, each case study is a concentrated, single inquiry, studied holistically in its own entirety (Stake, 1995 , 1998 ).

Researchers who use case study are urged to seek out what is common and what is particular about the case. This involves careful and in-depth consideration of the nature of the case, historical background, physical setting, and other institutional and political contextual factors (Stake, 1998 ). An interpretive or social constructivist approach to qualitative case study research supports a transactional method of inquiry, where the researcher has a personal interaction with the case. The case is developed in a relationship between the researcher and informants, and presented to engage the reader, inviting them to join in this interaction and in case discovery (Stake, 1995 ). A postpositivist approach to case study involves developing a clear case study protocol with careful consideration of validity and potential bias, which might involve an exploratory or pilot phase, and ensures that all elements of the case are measured and adequately described (Yin, 2009 , 2012 ).

Current methodological issues in qualitative case study research

The future of qualitative research will be influenced and constructed by the way research is conducted, and by what is reviewed and published in academic journals (Morse, 2011 ). If case study research is to further develop as a principal qualitative methodological approach, and make a valued contribution to the field of qualitative inquiry, issues related to methodological credibility must be considered. Researchers are required to demonstrate rigour through adequate descriptions of methodological foundations. Case studies published without sufficient detail for the reader to understand the study design, and without rationale for key methodological decisions, may lead to research being interpreted as lacking in quality or credibility (Hallberg, 2013 ; Morse, 2011 ).

There is a level of artistic license that is embraced by qualitative researchers and distinguishes practice, which nurtures creativity, innovation, and reflexivity (Denzin & Lincoln, 2011b ; Morse, 2009 ). Qualitative research is “inherently multimethod” (Denzin & Lincoln, 2011a , p. 5); however, with this creative freedom, it is important for researchers to provide adequate description for methodological justification (Meyer, 2001 ). This includes paradigm and theoretical perspectives that have influenced study design. Without adequate description, study design might not be understood by the reader, and can appear to be dishonest or inaccurate. Reviewers and readers might be confused by the inconsistent or inappropriate terms used to describe case study research approach and methods, and be distracted from important study findings (Sandelowski, 2000 ). This issue extends beyond case study research, and others have noted inconsistencies in reporting of methodology and method by qualitative researchers. Sandelowski ( 2000 , 2010 ) argued for accurate identification of qualitative description as a research approach. She recommended that the selected methodology should be harmonious with the study design, and be reflected in methods and analysis techniques. Similarly, Webb and Kevern ( 2000 ) uncovered inconsistencies in qualitative nursing research with focus group methods, recommending that methodological procedures must cite seminal authors and be applied with respect to the selected theoretical framework. Incorrect labelling using case study might stem from the flexibility in case study design and non-directional character relative to other approaches (Rosenberg & Yates, 2007 ). Methodological integrity is required in design of qualitative studies, including case study, to ensure study rigour and to enhance credibility of the field (Morse, 2011 ).

Case study has been unnecessarily devalued by comparisons with statistical methods (Eisenhardt, 1989 ; Flyvbjerg, 2006 , 2011 ; Jensen & Rodgers, 2001 ; Piekkari, Welch, & Paavilainen, 2009 ; Tight, 2010 ; Yin, 1999 ). It is reputed to be the “the weak sibling” in comparison to other, more rigorous, approaches (Yin, 2009 , p. xiii). Case study is not an inherently comparative approach to research. The objective is not statistical research, and the aim is not to produce outcomes that are generalizable to all populations (Thomas, 2011 ). Comparisons between case study and statistical research do little to advance this qualitative approach, and fail to recognize its inherent value, which can be better understood from the interpretive or social constructionist viewpoint of other authors (Merriam, 2009 ; Stake, 1995 ). Building on discussions relating to “fuzzy” (Bassey, 2001 ), or naturalistic generalizations (Stake, 1978 ), or transference of concepts and theories (Ayres, Kavanaugh, & Knafl, 2003 ; Morse et al., 2011 ) would have more relevance.

Case study research has been used as a catch-all design to justify or add weight to fundamental qualitative descriptive studies that do not fit with other traditional frameworks (Merriam, 2009 ). A case study has been a “convenient label for our research—when we ‘can't think of anything ‘better”—in an attempt to give it [qualitative methodology] some added respectability” (Tight, 2010 , p. 337). Qualitative case study research is a pliable approach (Merriam, 2009 ; Meyer, 2001 ; Stake, 1995 ), and has been likened to a “curious methodological limbo” (Gerring, 2004 , p. 341) or “paradigmatic bridge” (Luck et al., 2006 , p. 104), that is on the borderline between postpositivist and constructionist interpretations. This has resulted in inconsistency in application, which indicates that flexibility comes with limitations (Meyer, 2001 ), and the open nature of case study research might be off-putting to novice researchers (Thomas, 2011 ). The development of a well-(in)formed theoretical framework to guide a case study should improve consistency, rigour, and trust in studies published in qualitative research journals (Meyer, 2001 ).

Assessment of rigour

The purpose of this study was to analyse the methodological descriptions of case studies published in qualitative methods journals. To do this we needed to develop a suitable framework, which used existing, established criteria for appraising qualitative case study research rigour (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ). A number of qualitative authors have developed concepts and criteria that are used to determine whether a study is rigorous (Denzin & Lincoln, 2011b ; Lincoln, 1995 ; Sandelowski & Barroso, 2002 ). The criteria proposed by Stake ( 1995 ) provide a framework for readers and reviewers to make judgements regarding case study quality, and identify key characteristics essential for good methodological rigour. Although each of the factors listed in Stake's criteria could enhance the quality of a qualitative research report, in Table I we present an adapted criteria used in this study, which integrates more recent work by Merriam ( 2009 ) and Creswell ( 2013b ). Stake's ( 1995 ) original criteria were separated into two categories. The first list of general criteria is “relevant for all qualitative research.” The second list, “high relevance to qualitative case study research,” was the criteria that we decided had higher relevance to case study research. This second list was the main criteria used to assess the methodological descriptions of the case studies reviewed. The complete table has been preserved so that the reader can determine how the original criteria were adapted.

Framework for assessing quality in qualitative case study research.

Adapted from Stake ( 1995 , p. 131).

Study design

The critical review method described by Grant and Booth ( 2009 ) was used, which is appropriate for the assessment of research quality, and is used for literature analysis to inform research and practice. This type of review goes beyond the mapping and description of scoping or rapid reviews, to include “analysis and conceptual innovation” (Grant & Booth, 2009 , p. 93). A critical review is used to develop existing, or produce new, hypotheses or models. This is different to systematic reviews that answer clinical questions. It is used to evaluate existing research and competing ideas, to provide a “launch pad” for conceptual development and “subsequent testing” (Grant & Booth, 2009 , p. 93).

Qualitative methods journals were located by a search of the 2011 ISI Journal Citation Reports in Social Science, via the database Web of Knowledge (see m.webofknowledge.com). No “qualitative research methods” category existed in the citation reports; therefore, a search of all categories was performed using the term “qualitative.” In Table II , we present the qualitative methods journals located, ranked by impact factor. The highest ranked journals were selected for searching. We acknowledge that the impact factor ranking system might not be the best measure of journal quality (Cheek, Garnham, & Quan, 2006 ); however, this was the most appropriate and accessible method available.

International Journal of Qualitative Studies on Health and Well-being.

Search strategy

In March 2013, searches of the journals, Qualitative Health Research , Qualitative Research , and Qualitative Inquiry were completed to retrieve studies with “case study” in the abstract field. The search was limited to the past 5 years (1 January 2008 to 1 March 2013). The objective was to locate published qualitative case studies suitable for assessment using the adapted criterion. Viewpoints, commentaries, and other article types were excluded from review. Title and abstracts of the 45 retrieved articles were read by the first author, who identified 34 empirical case studies for review. All authors reviewed the 34 studies to confirm selection and categorization. In Table III , we present the 34 case studies grouped by journal, and categorized by research topic, including health sciences, social sciences and anthropology, and methods research. There was a discrepancy in categorization of one article on pedagogy and a new teaching method published in Qualitative Inquiry (Jorrín-Abellán, Rubia-Avi, Anguita-Martínez, Gómez-Sánchez, & Martínez-Mones, 2008 ). Consensus was to allocate to the methods category.

Outcomes of search of qualitative methods journals.

In Table III , the number of studies located, and final numbers selected for review have been reported. Qualitative Health Research published the most empirical case studies ( n= 16). In the health category, there were 12 case studies of health conditions, health services, and health policy issues, all published in Qualitative Health Research . Seven case studies were categorized as social sciences and anthropology research, which combined case study with biography and ethnography methodologies. All three journals published case studies on methods research to illustrate a data collection or analysis technique, methodological procedure, or related issue.

The methodological descriptions of 34 case studies were critically reviewed using the adapted criteria. All articles reviewed contained a description of study methods; however, the length, amount of detail, and position of the description in the article varied. Few studies provided an accurate description and rationale for using a qualitative case study approach. In the 34 case studies reviewed, three described a theoretical framework informed by Stake ( 1995 ), two by Yin ( 2009 ), and three provided a mixed framework informed by various authors, which might have included both Yin and Stake. Few studies described their case study design, or included a rationale that explained why they excluded or added further procedures, and whether this was to enhance the study design, or to better suit the research question. In 26 of the studies no reference was provided to principal case study authors. From reviewing the description of methods, few authors provided a description or justification of case study methodology that demonstrated how their study was informed by the methodological literature that exists on this approach.

The methodological descriptions of each study were reviewed using the adapted criteria, and the following issues were identified: case study methodology or method; case of something particular and case selection; contextually bound case study; researcher and case interactions and triangulation; and, study design inconsistent with methodology. An outline of how the issues were developed from the critical review is provided, followed by a discussion of how these relate to the current methodological literature.

Case study methodology or method

A third of the case studies reviewed appeared to use a case report method, not case study methodology as described by principal authors (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). Case studies were identified as a case report because of missing methodological detail and by review of the study aims and purpose. These reports presented data for small samples of no more than three people, places or phenomenon. Four studies, or “case reports” were single cases selected retrospectively from larger studies (Bronken, Kirkevold, Martinsen, & Kvigne, 2012 ; Coltart & Henwood, 2012 ; Hooghe, Neimeyer, & Rober, 2012 ; Roscigno et al., 2012 ). Case reports were not a case of something, instead were a case demonstration or an example presented in a report. These reports presented outcomes, and reported on how the case could be generalized. Descriptions focussed on the phenomena, rather than the case itself, and did not appear to study the case in its entirety.

Case reports had minimal in-text references to case study methodology, and were informed by other qualitative traditions or secondary sources (Adamson & Holloway, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nagar-Ron & Motzafi-Haller, 2011 ). This does not suggest that case study methodology cannot be multimethod, however, methodology should be consistent in design, be clearly described (Meyer, 2001 ; Stake, 1995 ), and maintain focus on the case (Creswell, 2013b ).

To demonstrate how case reports were identified, three examples are provided. The first, Yeh ( 2013 ) described their study as, “the examination of the emergence of vegetarianism in Victorian England serves as a case study to reveal the relationships between boundaries and entities” (p. 306). The findings were a historical case report, which resulted from an ethnographic study of vegetarianism. Cunsolo Willox, Harper, Edge, ‘My Word’: Storytelling and Digital Media Lab, and Rigolet Inuit Community Government (2013) used “a case study that illustrates the usage of digital storytelling within an Inuit community” (p. 130). This case study reported how digital storytelling can be used with indigenous communities as a participatory method to illuminate the benefits of this method for other studies. This “case study was conducted in the Inuit community” but did not include the Inuit community in case analysis (Cunsolo Willox et al., 2013 , p. 130). Bronken et al. ( 2012 ) provided a single case report to demonstrate issues observed in a larger clinical study of aphasia and stroke, without adequate case description or analysis.

Case study of something particular and case selection

Case selection is a precursor to case analysis, which needs to be presented as a convincing argument (Merriam, 2009 ). Descriptions of the case were often not adequate to ascertain why the case was selected, or whether it was a particular exemplar or outlier (Thomas, 2011 ). In a number of case studies in the health and social science categories, it was not explicit whether the case was of something particular, or peculiar to their discipline or field (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson, Botelho, Welch, Joseph, & Tennstedt, 2012 ; Mawn et al., 2010 ; Snyder-Young, 2011 ). There were exceptions in the methods category ( Table III ), where cases were selected by researchers to report on a new or innovative method. The cases emerged through heuristic study, and were reported to be particular, relative to the existing methods literature (Ajodhia-Andrews & Berman, 2009 ; Buckley & Waring, 2013 ; Cunsolo Willox et al., 2013 ; De Haene, Grietens, & Verschueren, 2010 ; Gratton & O'Donnell, 2011 ; Sumsion, 2013 ; Wimpenny & Savin-Baden, 2012 ).

Case selection processes were sometimes insufficient to understand why the case was selected from the global population of cases, or what study of this case would contribute to knowledge as compared with other possible cases (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson et al., 2012 ; Mawn et al., 2010 ). In two studies, local cases were selected (Barone, 2010 ; Fourie & Theron, 2012 ) because the researcher was familiar with and had access to the case. Possible limitations of a convenience sample were not acknowledged. Purposeful sampling was used to recruit participants within the case of one study, but not of the case itself (Gallagher et al., 2013 ). Random sampling was completed for case selection in two studies (Colón-Emeric et al., 2010 ; Jackson et al., 2012 ), which has limited meaning in interpretive qualitative research.

To demonstrate how researchers provided a good justification for the selection of case study approaches, four examples are provided. The first, cases of residential care homes, were selected because of reported occurrences of mistreatment, which included residents being locked in rooms at night (Rytterström, Unosson, & Arman, 2013 ). Roscigno et al. ( 2012 ) selected cases of parents who were admitted for early hospitalization in neonatal intensive care with a threatened preterm delivery before 26 weeks. Hooghe et al. ( 2012 ) used random sampling to select 20 couples that had experienced the death of a child; however, the case study was of one couple and a particular metaphor described only by them. The final example, Coltart and Henwood ( 2012 ), provided a detailed account of how they selected two cases from a sample of 46 fathers based on personal characteristics and beliefs. They described how the analysis of the two cases would contribute to their larger study on first time fathers and parenting.

Contextually bound case study

The limits or boundaries of the case are a defining factor of case study methodology (Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Adequate contextual description is required to understand the setting or context in which the case is revealed. In the health category, case studies were used to illustrate a clinical phenomenon or issue such as compliance and health behaviour (Colón-Emeric et al., 2010 ; D'Enbeau, Buzzanell, & Duckworth, 2010 ; Gallagher et al., 2013 ; Hooghe et al., 2012 ; Jackson et al., 2012 ; Roscigno et al., 2012 ). In these case studies, contextual boundaries, such as physical and institutional descriptions, were not sufficient to understand the case as a holistic system, for example, the general practitioner (GP) clinic in Gallagher et al. ( 2013 ), or the nursing home in Colón-Emeric et al. ( 2010 ). Similarly, in the social science and methods categories, attention was paid to some components of the case context, but not others, missing important information required to understand the case as a holistic system (Alexander, Moreira, & Kumar, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nairn & Panelli, 2009 ; Wimpenny & Savin-Baden, 2012 ).

In two studies, vicarious experience or vignettes (Nairn & Panelli, 2009 ) and images (Jorrín-Abellán et al., 2008 ) were effective to support description of context, and might have been a useful addition for other case studies. Missing contextual boundaries suggests that the case might not be adequately defined. Additional information, such as the physical, institutional, political, and community context, would improve understanding of the case (Stake, 1998 ). In Boxes 1 and 2 , we present brief synopses of two studies that were reviewed, which demonstrated a well bounded case. In Box 1 , Ledderer ( 2011 ) used a qualitative case study design informed by Stake's tradition. In Box 2 , Gillard, Witt, and Watts ( 2011 ) were informed by Yin's tradition. By providing a brief outline of the case studies in Boxes 1 and 2 , we demonstrate how effective case boundaries can be constructed and reported, which may be of particular interest to prospective case study researchers.

Article synopsis of case study research using Stake's tradition

Ledderer ( 2011 ) used a qualitative case study research design, informed by modern ethnography. The study is bounded to 10 general practice clinics in Denmark, who had received federal funding to implement preventative care services based on a Motivational Interviewing intervention. The researcher question focussed on “why is it so difficult to create change in medical practice?” (Ledderer, 2011 , p. 27). The study context was adequately described, providing detail on the general practitioner (GP) clinics and relevant political and economic influences. Methodological decisions are described in first person narrative, providing insight on researcher perspectives and interaction with the case. Forty-four interviews were conducted, which focussed on how GPs conducted consultations, and the form, nature and content, rather than asking their opinion or experience (Ledderer, 2011 , p. 30). The duration and intensity of researcher immersion in the case enhanced depth of description and trustworthiness of study findings. Analysis was consistent with Stake's tradition, and the researcher provided examples of inquiry techniques used to challenge assumptions about emerging themes. Several other seminal qualitative works were cited. The themes and typology constructed are rich in narrative data and storytelling by clinic staff, demonstrating individual clinic experiences as well as shared meanings and understandings about changing from a biomedical to psychological approach to preventative health intervention. Conclusions make note of social and cultural meanings and lessons learned, which might not have been uncovered using a different methodology.

Article synopsis of case study research using Yin's tradition

Gillard et al. ( 2011 ) study of camps for adolescents living with HIV/AIDs provided a good example of Yin's interpretive case study approach. The context of the case is bounded by the three summer camps of which the researchers had prior professional involvement. A case study protocol was developed that used multiple methods to gather information at three data collection points coinciding with three youth camps (Teen Forum, Discover Camp, and Camp Strong). Gillard and colleagues followed Yin's ( 2009 ) principles, using a consistent data protocol that enhanced cross-case analysis. Data described the young people, the camp physical environment, camp schedule, objectives and outcomes, and the staff of three youth camps. The findings provided a detailed description of the context, with less detail of individual participants, including insight into researcher's interpretations and methodological decisions throughout the data collection and analysis process. Findings provided the reader with a sense of “being there,” and are discovered through constant comparison of the case with the research issues; the case is the unit of analysis. There is evidence of researcher immersion in the case, and Gillard reports spending significant time in the field in a naturalistic and integrated youth mentor role.

This case study is not intended to have a significant impact on broader health policy, although does have implications for health professionals working with adolescents. Study conclusions will inform future camps for young people with chronic disease, and practitioners are able to compare similarities between this case and their own practice (for knowledge translation). No limitations of this article were reported. Limitations related to publication of this case study were that it was 20 pages long and used three tables to provide sufficient description of the camp and program components, and relationships with the research issue.

Researcher and case interactions and triangulation

Researcher and case interactions and transactions are a defining feature of case study methodology (Stake, 1995 ). Narrative stories, vignettes, and thick description are used to provoke vicarious experience and a sense of being there with the researcher in their interaction with the case. Few of the case studies reviewed provided details of the researcher's relationship with the case, researcher–case interactions, and how these influenced the development of the case study (Buzzanell & D'Enbeau, 2009 ; D'Enbeau et al., 2010 ; Gallagher et al., 2013 ; Gillard et al., 2011 ; Ledderer, 2011 ; Nagar-Ron & Motzafi-Haller, 2011 ). The role and position of the researcher needed to be self-examined and understood by readers, to understand how this influenced interactions with participants, and to determine what triangulation is needed (Merriam, 2009 ; Stake, 1995 ).

Gillard et al. ( 2011 ) provided a good example of triangulation, comparing data sources in a table (p. 1513). Triangulation of sources was used to reveal as much depth as possible in the study by Nagar-Ron and Motzafi-Haller ( 2011 ), while also enhancing confirmation validity. There were several case studies that would have benefited from improved range and use of data sources, and descriptions of researcher–case interactions (Ajodhia-Andrews & Berman, 2009 ; Bronken et al., 2012 ; Fincham, Scourfield, & Langer, 2008 ; Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Snyder-Young, 2011 ; Yeh, 2013 ).

Study design inconsistent with methodology

Good, rigorous case studies require a strong methodological justification (Meyer, 2001 ) and a logical and coherent argument that defines paradigm, methodological position, and selection of study methods (Denzin & Lincoln, 2011b ). Methodological justification was insufficient in several of the studies reviewed (Barone, 2010 ; Bronken et al., 2012 ; Hooghe et al., 2012 ; Mawn et al., 2010 ; Roscigno et al., 2012 ; Yeh, 2013 ). This was judged by the absence, or inadequate or inconsistent reference to case study methodology in-text.

In six studies, the methodological justification provided did not relate to case study. There were common issues identified. Secondary sources were used as primary methodological references indicating that study design might not have been theoretically sound (Colón-Emeric et al., 2010 ; Coltart & Henwood, 2012 ; Roscigno et al., 2012 ; Snyder-Young, 2011 ). Authors and sources cited in methodological descriptions were inconsistent with the actual study design and practices used (Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Jorrín-Abellán et al., 2008 ; Mawn et al., 2010 ; Rytterström et al., 2013 ; Wimpenny & Savin-Baden, 2012 ). This occurred when researchers cited Stake or Yin, or both (Mawn et al., 2010 ; Rytterström et al., 2013 ), although did not follow their paradigmatic or methodological approach. In 26 studies there were no citations for a case study methodological approach.

The findings of this study have highlighted a number of issues for researchers. A considerable number of case studies reviewed were missing key elements that define qualitative case study methodology and the tradition cited. A significant number of studies did not provide a clear methodological description or justification relevant to case study. Case studies in health and social sciences did not provide sufficient information for the reader to understand case selection, and why this case was chosen above others. The context of the cases were not described in adequate detail to understand all relevant elements of the case context, which indicated that cases may have not been contextually bounded. There were inconsistencies between reported methodology, study design, and paradigmatic approach in case studies reviewed, which made it difficult to understand the study methodology and theoretical foundations. These issues have implications for methodological integrity and honesty when reporting study design, which are values of the qualitative research tradition and are ethical requirements (Wager & Kleinert, 2010a ). Poorly described methodological descriptions may lead the reader to misinterpret or discredit study findings, which limits the impact of the study, and, as a collective, hinders advancements in the broader qualitative research field.

The issues highlighted in our review build on current debates in the case study literature, and queries about the value of this methodology. Case study research can be situated within different paradigms or designed with an array of methods. In order to maintain the creativity and flexibility that is valued in this methodology, clearer descriptions of paradigm and theoretical position and methods should be provided so that study findings are not undervalued or discredited. Case study research is an interdisciplinary practice, which means that clear methodological descriptions might be more important for this approach than other methodologies that are predominantly driven by fewer disciplines (Creswell, 2013b ).

Authors frequently omit elements of methodologies and include others to strengthen study design, and we do not propose a rigid or purist ideology in this paper. On the contrary, we encourage new ideas about using case study, together with adequate reporting, which will advance the value and practice of case study. The implications of unclear methodological descriptions in the studies reviewed were that study design appeared to be inconsistent with reported methodology, and key elements required for making judgements of rigour were missing. It was not clear whether the deviations from methodological tradition were made by researchers to strengthen the study design, or because of misinterpretations. Morse ( 2011 ) recommended that innovations and deviations from practice are best made by experienced researchers, and that a novice might be unaware of the issues involved with making these changes. To perpetuate the tradition of case study research, applications in the published literature should have consistencies with traditional methodological constructions, and deviations should be described with a rationale that is inherent in study conduct and findings. Providing methodological descriptions that demonstrate a strong theoretical foundation and coherent study design will add credibility to the study, while ensuring the intrinsic meaning of case study is maintained.

The value of this review is that it contributes to discussion of whether case study is a methodology or method. We propose possible reasons why researchers might make this misinterpretation. Researchers may interchange the terms methods and methodology, and conduct research without adequate attention to epistemology and historical tradition (Carter & Little, 2007 ; Sandelowski, 2010 ). If the rich meaning that naming a qualitative methodology brings to the study is not recognized, a case study might appear to be inconsistent with the traditional approaches described by principal authors (Creswell, 2013a ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). If case studies are not methodologically and theoretically situated, then they might appear to be a case report.

Case reports are promoted by university and medical journals as a method of reporting on medical or scientific cases; guidelines for case reports are publicly available on websites ( http://www.hopkinsmedicine.org/institutional_review_board/guidelines_policies/guidelines/case_report.html ). The various case report guidelines provide a general criteria for case reports, which describes that this form of report does not meet the criteria of research, is used for retrospective analysis of up to three clinical cases, and is primarily illustrative and for educational purposes. Case reports can be published in academic journals, but do not require approval from a human research ethics committee. Traditionally, case reports describe a single case, to explain how and what occurred in a selected setting, for example, to illustrate a new phenomenon that has emerged from a larger study. A case report is not necessarily particular or the study of a case in its entirety, and the larger study would usually be guided by a different research methodology.

This description of a case report is similar to what was provided in some studies reviewed. This form of report lacks methodological grounding and qualities of research rigour. The case report has publication value in demonstrating an example and for dissemination of knowledge (Flanagan, 1999 ). However, case reports have different meaning and purpose to case study, which needs to be distinguished. Findings of our review suggest that the medical understanding of a case report has been confused with qualitative case study approaches.

In this review, a number of case studies did not have methodological descriptions that included key characteristics of case study listed in the adapted criteria, and several issues have been discussed. There have been calls for improvements in publication quality of qualitative research (Morse, 2011 ), and for improvements in peer review of submitted manuscripts (Carter & Little, 2007 ; Jasper, Vaismoradi, Bondas, & Turunen, 2013 ). The challenging nature of editor and reviewers responsibilities are acknowledged in the literature (Hames, 2013 ; Wager & Kleinert, 2010b ); however, review of case study methodology should be prioritized because of disputes on methodological value.

Authors using case study approaches are recommended to describe their theoretical framework and methods clearly, and to seek and follow specialist methodological advice when needed (Wager & Kleinert, 2010a ). Adequate page space for case study description would contribute to better publications (Gillard et al., 2011 ). Capitalizing on the ability to publish complementary resources should be considered.

Limitations of the review

There is a level of subjectivity involved in this type of review and this should be considered when interpreting study findings. Qualitative methods journals were selected because the aims and scope of these journals are to publish studies that contribute to methodological discussion and development of qualitative research. Generalist health and social science journals were excluded that might have contained good quality case studies. Journals in business or education were also excluded, although a review of case studies in international business journals has been published elsewhere (Piekkari et al., 2009 ).

The criteria used to assess the quality of the case studies were a set of qualitative indicators. A numerical or ranking system might have resulted in different results. Stake's ( 1995 ) criteria have been referenced elsewhere, and was deemed the best available (Creswell, 2013b ; Crowe et al., 2011 ). Not all qualitative studies are reported in a consistent way and some authors choose to report findings in a narrative form in comparison to a typical biomedical report style (Sandelowski & Barroso, 2002 ), if misinterpretations were made this may have affected the review.

Case study research is an increasingly popular approach among qualitative researchers, which provides methodological flexibility through the incorporation of different paradigmatic positions, study designs, and methods. However, whereas flexibility can be an advantage, a myriad of different interpretations has resulted in critics questioning the use of case study as a methodology. Using an adaptation of established criteria, we aimed to identify and assess the methodological descriptions of case studies in high impact, qualitative methods journals. Few articles were identified that applied qualitative case study approaches as described by experts in case study design. There were inconsistencies in methodology and study design, which indicated that researchers were confused whether case study was a methodology or a method. Commonly, there appeared to be confusion between case studies and case reports. Without clear understanding and application of the principles and key elements of case study methodology, there is a risk that the flexibility of the approach will result in haphazard reporting, and will limit its global application as a valuable, theoretically supported methodology that can be rigorously applied across disciplines and fields.

Conflict of interest and funding

The authors have not received any funding or benefits from industry or elsewhere to conduct this study.

• Adamson S, Holloway M. Negotiating sensitivities and grappling with intangibles: Experiences from a study of spirituality and funerals. Qualitative Research. 2012; 12 (6):735–752. doi: 10.1177/1468794112439008. [ CrossRef ] [ Google Scholar ]
• Ajodhia-Andrews A, Berman R. Exploring school life from the lens of a child who does not use speech to communicate. Qualitative Inquiry. 2009; 15 (5):931–951. doi: 10.1177/1077800408322789. [ CrossRef ] [ Google Scholar ]
• Alexander B. K, Moreira C, Kumar H. S. Resisting (resistance) stories: A tri-autoethnographic exploration of father narratives across shades of difference. Qualitative Inquiry. 2012; 18 (2):121–133. doi: 10.1177/1077800411429087. [ CrossRef ] [ Google Scholar ]
• Austin W, Park C, Goble E. From interdisciplinary to transdisciplinary research: A case study. Qualitative Health Research. 2008; 18 (4):557–564. doi: 10.1177/1049732307308514. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ayres L, Kavanaugh K, Knafl K. A. Within-case and across-case approaches to qualitative data analysis. Qualitative Health Research. 2003; 13 (6):871–883. doi: 10.1177/1049732303013006008. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barone T. L. Culturally sensitive care 1969–2000: The Indian Chicano Health Center. Qualitative Health Research. 2010; 20 (4):453–464. doi: 10.1177/1049732310361893. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bassey M. A solution to the problem of generalisation in educational research: Fuzzy prediction. Oxford Review of Education. 2001; 27 (1):5–22. doi: 10.1080/03054980123773. [ CrossRef ] [ Google Scholar ]
• Bronken B. A, Kirkevold M, Martinsen R, Kvigne K. The aphasic storyteller: Coconstructing stories to promote psychosocial well-being after stroke. Qualitative Health Research. 2012; 22 (10):1303–1316. doi: 10.1177/1049732312450366. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Broyles L. M, Rodriguez K. L, Price P. A, Bayliss N. K, Sevick M. A. Overcoming barriers to the recruitment of nurses as participants in health care research. Qualitative Health Research. 2011; 21 (12):1705–1718. doi: 10.1177/1049732311417727. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Buckley C. A, Waring M. J. Using diagrams to support the research process: Examples from grounded theory. Qualitative Research. 2013; 13 (2):148–172. doi: 10.1177/1468794112472280. [ CrossRef ] [ Google Scholar ]
• Buzzanell P. M, D'Enbeau S. Stories of caregiving: Intersections of academic research and women's everyday experiences. Qualitative Inquiry. 2009; 15 (7):1199–1224. doi: 10.1177/1077800409338025. [ CrossRef ] [ Google Scholar ]
• Carter S. M, Little M. Justifying knowledge, justifying method, taking action: Epistemologies, methodologies, and methods in qualitative research. Qualitative Health Research. 2007; 17 (10):1316–1328. doi: 10.1177/1049732307306927. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cheek J, Garnham B, Quan J. What's in a number? Issues in providing evidence of impact and quality of research(ers) Qualitative Health Research. 2006; 16 (3):423–435. doi: 10.1177/1049732305285701. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Colón-Emeric C. S, Plowman D, Bailey D, Corazzini K, Utley-Smith Q, Ammarell N, et al. Regulation and mindful resident care in nursing homes. Qualitative Health Research. 2010; 20 (9):1283–1294. doi: 10.1177/1049732310369337. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Coltart C, Henwood K. On paternal subjectivity: A qualitative longitudinal and psychosocial case analysis of men's classed positions and transitions to first-time fatherhood. Qualitative Research. 2012; 12 (1):35–52. doi: 10.1177/1468794111426224. [ CrossRef ] [ Google Scholar ]
• Creswell J. W. Five qualitative approaches to inquiry. In: Creswell J. W, editor. Qualitative inquiry and research design: Choosing among five approaches. 3rd ed. Thousand Oaks, CA: Sage; 2013a. pp. 53–84. [ Google Scholar ]
• Creswell J. W. Qualitative inquiry and research design: Choosing among five approaches. 3rd ed. Thousand Oaks, CA: Sage; 2013b. [ Google Scholar ]
• Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach. BMC Medical Research Methodology. 2011; 11 (1):1–9. doi: 10.1186/1471-2288-11-100. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cunsolo Willox A, Harper S. L, Edge V. L, ‘My Word’: Storytelling and Digital Media Lab, & Rigolet Inuit Community Government Storytelling in a digital age: Digital storytelling as an emerging narrative method for preserving and promoting indigenous oral wisdom. Qualitative Research. 2013; 13 (2):127–147. doi: 10.1177/1468794112446105. [ CrossRef ] [ Google Scholar ]
• De Haene L, Grietens H, Verschueren K. Holding harm: Narrative methods in mental health research on refugee trauma. Qualitative Health Research. 2010; 20 (12):1664–1676. doi: 10.1177/1049732310376521. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• D'Enbeau S, Buzzanell P. M, Duckworth J. Problematizing classed identities in fatherhood: Development of integrative case studies for analysis and praxis. Qualitative Inquiry. 2010; 16 (9):709–720. doi: 10.1177/1077800410374183. [ CrossRef ] [ Google Scholar ]
• Denzin N. K, Lincoln Y. S. Introduction: Disciplining the practice of qualitative research. In: Denzin N. K, Lincoln Y. S, editors. The SAGE handbook of qualitative research. 4th ed. Thousand Oaks, CA: Sage; 2011a. pp. 1–6. [ Google Scholar ]
• Denzin N. K, Lincoln Y. S, editors. The SAGE handbook of qualitative research. 4th ed. Thousand Oaks, CA: Sage; 2011b. [ Google Scholar ]
• Edwards R, Weller S. Shifting analytic ontology: Using I-poems in qualitative longitudinal research. Qualitative Research. 2012; 12 (2):202–217. doi: 10.1177/1468794111422040. [ CrossRef ] [ Google Scholar ]
• Eisenhardt K. M. Building theories from case study research. The Academy of Management Review. 1989; 14 (4):532–550. doi: 10.2307/258557. [ CrossRef ] [ Google Scholar ]
• Fincham B, Scourfield J, Langer S. The impact of working with disturbing secondary data: Reading suicide files in a coroner's office. Qualitative Health Research. 2008; 18 (6):853–862. doi: 10.1177/1049732307308945. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Flanagan J. Public participation in the design of educational programmes for cancer nurses: A case report. European Journal of Cancer Care. 1999; 8 (2):107–112. doi: 10.1046/j.1365-2354.1999.00141.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Flyvbjerg B. Five misunderstandings about case-study research. Qualitative Inquiry. 2006; 12 (2):219–245. doi: 10.1177/1077800405284.363. [ CrossRef ] [ Google Scholar ]
• Flyvbjerg B. Case study. In: Denzin N. K, Lincoln Y. S, editors. The SAGE handbook of qualitative research. 4th ed. Thousand Oaks, CA: Sage; 2011. pp. 301–316. [ Google Scholar ]
• Fourie C. L, Theron L. C. Resilience in the face of fragile X syndrome. Qualitative Health Research. 2012; 22 (10):1355–1368. doi: 10.1177/1049732312451871. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gallagher N, MacFarlane A, Murphy A. W, Freeman G. K, Glynn L. G, Bradley C. P. Service users’ and caregivers’ perspectives on continuity of care in out-of-hours primary care. Qualitative Health Research. 2013; 23 (3):407–421. doi: 10.1177/1049732312470521. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gerring J. What is a case study and what is it good for? American Political Science Review. 2004; 98 (2):341–354. doi: 10.1017/S0003055404001182. [ CrossRef ] [ Google Scholar ]
• Gillard A, Witt P. A, Watts C. E. Outcomes and processes at a camp for youth with HIV/AIDS. Qualitative Health Research. 2011; 21 (11):1508–1526. doi: 10.1177/1049732311413907. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grant M, Booth A. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal. 2009; 26 :91–108. doi: 10.1111/j.1471-1842.2009.00848.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gratton M.-F, O'Donnell S. Communication technologies for focus groups with remote communities: A case study of research with First Nations in Canada. Qualitative Research. 2011; 11 (2):159–175. doi: 10.1177/1468794110394068. [ CrossRef ] [ Google Scholar ]
• Hallberg L. Quality criteria and generalization of results from qualitative studies. International Journal of Qualitative Studies on Health and Wellbeing. 2013; 8 :1. doi: 10.3402/qhw.v8i0.20647. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hames I. Committee on Publication Ethics, 1. 2013, March. COPE Ethical guidelines for peer reviewers. Retrieved April 7, 2013, from http://publicationethics.org/resources/guidelines . [ Google Scholar ]
• Hooghe A, Neimeyer R. A, Rober P. “Cycling around an emotional core of sadness”: Emotion regulation in a couple after the loss of a child. Qualitative Health Research. 2012; 22 (9):1220–1231. doi: 10.1177/1049732312449209. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jackson C. B, Botelho E. M, Welch L. C, Joseph J, Tennstedt S. L. Talking with others about stigmatized health conditions: Implications for managing symptoms. Qualitative Health Research. 2012; 22 (11):1468–1475. doi: 10.1177/1049732312450323. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jasper M, Vaismoradi M, Bondas T, Turunen H. Validity and reliability of the scientific review process in nursing journals—time for a rethink? Nursing Inquiry. 2013 doi: 10.1111/nin.12030. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jensen J. L, Rodgers R. Cumulating the intellectual gold of case study research. Public Administration Review. 2001; 61 (2):235–246. doi: 10.1111/0033-3352.00025. [ CrossRef ] [ Google Scholar ]
• Jorrín-Abellán I. M, Rubia-Avi B, Anguita-Martínez R, Gómez-Sánchez E, Martínez-Mones A. Bouncing between the dark and bright sides: Can technology help qualitative research? Qualitative Inquiry. 2008; 14 (7):1187–1204. doi: 10.1177/1077800408318435. [ CrossRef ] [ Google Scholar ]
• Ledderer L. Understanding change in medical practice: The role of shared meaning in preventive treatment. Qualitative Health Research. 2011; 21 (1):27–40. doi: 10.1177/1049732310377451. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lincoln Y. S. Emerging criteria for quality in qualitative and interpretive research. Qualitative Inquiry. 1995; 1 (3):275–289. doi: 10.1177/107780049500100301. [ CrossRef ] [ Google Scholar ]
• Luck L, Jackson D, Usher K. Case study: A bridge across the paradigms. Nursing Inquiry. 2006; 13 (2):103–109. doi: 10.1111/j.1440-1800.2006.00309.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mawn B, Siqueira E, Koren A, Slatin C, Devereaux Melillo K, Pearce C, et al. Health disparities among health care workers. Qualitative Health Research. 2010; 20 (1):68–80. doi: 10.1177/1049732309355590. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Merriam S. B. Qualitative research: A guide to design and implementation. 3rd ed. San Francisco, CA: Jossey-Bass; 2009. [ Google Scholar ]
• Meyer C. B. A case in case study methodology. Field Methods. 2001; 13 (4):329–352. doi: 10.1177/1525822x0101300402. [ CrossRef ] [ Google Scholar ]
• Morse J. M. Mixing qualitative methods. Qualitative Health Research. 2009; 19 (11):1523–1524. doi: 10.1177/1049732309349360. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morse J. M. Molding qualitative health research. Qualitative Health Research. 2011; 21 (8):1019–1021. doi: 10.1177/1049732311404706. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morse J. M, Dimitroff L. J, Harper R, Koontz A, Kumra S, Matthew-Maich N, et al. Considering the qualitative–quantitative language divide. Qualitative Health Research. 2011; 21 (9):1302–1303. doi: 10.1177/1049732310392386. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nagar-Ron S, Motzafi-Haller P. “My life? There is not much to tell”: On voice, silence and agency in interviews with first-generation Mizrahi Jewish women immigrants to Israel. Qualitative Inquiry. 2011; 17 (7):653–663. doi: 10.1177/1077800411414007. [ CrossRef ] [ Google Scholar ]
• Nairn K, Panelli R. Using fiction to make meaning in research with young people in rural New Zealand. Qualitative Inquiry. 2009; 15 (1):96–112. doi: 10.1177/1077800408318314. [ CrossRef ] [ Google Scholar ]
• Nespor J. The afterlife of “teachers’ beliefs”: Qualitative methodology and the textline. Qualitative Inquiry. 2012; 18 (5):449–460. doi: 10.1177/1077800412439530. [ CrossRef ] [ Google Scholar ]
• Piekkari R, Welch C, Paavilainen E. The case study as disciplinary convention: Evidence from international business journals. Organizational Research Methods. 2009; 12 (3):567–589. doi: 10.1177/1094428108319905. [ CrossRef ] [ Google Scholar ]
• Ragin C. C, Becker H. S. What is a case?: Exploring the foundations of social inquiry. Cambridge: Cambridge University Press; 1992. [ Google Scholar ]
• Roscigno C. I, Savage T. A, Kavanaugh K, Moro T. T, Kilpatrick S. J, Strassner H. T, et al. Divergent views of hope influencing communications between parents and hospital providers. Qualitative Health Research. 2012; 22 (9):1232–1246. doi: 10.1177/1049732312449210. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosenberg J. P, Yates P. M. Schematic representation of case study research designs. Journal of Advanced Nursing. 2007; 60 (4):447–452. doi: 10.1111/j.1365-2648.2007.04385.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rytterström P, Unosson M, Arman M. Care culture as a meaning- making process: A study of a mistreatment investigation. Qualitative Health Research. 2013; 23 :1179–1187. doi: 10.1177/1049732312470760. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sandelowski M. Whatever happened to qualitative description? Research in Nursing & Health. 2000; 23 (4):334–340. doi: 10.1002/1098-240X. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sandelowski M. What's in a name? Qualitative description revisited. Research in Nursing & Health. 2010; 33 (1):77–84. doi: 10.1002/nur.20362. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sandelowski M, Barroso J. Reading qualitative studies. International Journal of Qualitative Methods. 2002; 1 (1):74–108. [ Google Scholar ]
• Snyder-Young D. “Here to tell her story”: Analyzing the autoethnographic performances of others. Qualitative Inquiry. 2011; 17 (10):943–951. doi: 10.1177/1077800411425149. [ CrossRef ] [ Google Scholar ]
• Stake R. E. The case study method in social inquiry. Educational Researcher. 1978; 7 (2):5–8. [ Google Scholar ]
• Stake R. E. The art of case study research. Thousand Oaks, CA: Sage; 1995. [ Google Scholar ]
• Stake R. E. Case studies. In: Denzin N. K, Lincoln Y. S, editors. Strategies of qualitative inquiry. Thousand Oaks, CA: Sage; 1998. pp. 86–109. [ Google Scholar ]
• Sumsion J. Opening up possibilities through team research: Investigating infants’ experiences of early childhood education and care. Qualitative Research. 2013; 14 (2):149–165. doi: 10.1177/1468794112468471.. [ CrossRef ] [ Google Scholar ]
• Thomas G. Doing case study: Abduction not induction, phronesis not theory. Qualitative Inquiry. 2010; 16 (7):575–582. doi: 10.1177/1077800410372601. [ CrossRef ] [ Google Scholar ]
• Thomas G. A typology for the case study in social science following a review of definition, discourse, and structure. Qualitative Inquiry. 2011; 17 (6):511–521. doi: 10.1177/1077800411409884. [ CrossRef ] [ Google Scholar ]
• Tight M. The curious case of case study: A viewpoint. International Journal of Social Research Methodology. 2010; 13 (4):329–339. doi: 10.1080/13645570903187181. [ CrossRef ] [ Google Scholar ]
• Wager E, Kleinert S. Responsible research publication: International standards for authors. A position statement developed at the 2nd World Conference on Research Integrity, Singapore, July 22–24, 2010. In: Mayer T, Steneck N, editors. Promoting research integrity in a global environment. Singapore: Imperial College Press/World Scientific; 2010a. pp. 309–316. [ Google Scholar ]
• Wager E, Kleinert S. Responsible research publication: International standards for editors. A position statement developed at the 2nd World Conference on Research Integrity, Singapore, July 22–24, 2010. In: Mayer T, Steneck N, editors. Promoting research integrity in a global environment. Singapore: Imperial College Press/World Scientific; 2010b. pp. 317–328. [ Google Scholar ]
• Webb C, Kevern J. Focus groups as a research method: A critique of some aspects of their use in nursing research. Journal of Advanced Nursing. 2000; 33 (6):798–805. doi: 10.1046/j.1365-2648.2001.01720.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wimpenny K, Savin-Baden M. Exploring and implementing participatory action synthesis. Qualitative Inquiry. 2012; 18 (8):689–698. doi: 10.1177/1077800412452854. [ CrossRef ] [ Google Scholar ]
• Yeh H.-Y. Boundaries, entities, and modern vegetarianism: Examining the emergence of the first vegetarian organization. Qualitative Inquiry. 2013; 19 (4):298–309. doi: 10.1177/1077800412471516. [ CrossRef ] [ Google Scholar ]
• Yin R. K. Enhancing the quality of case studies in health services research. Health Services Research. 1999; 34 (5 Pt 2):1209–1224. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Yin R. K. Case study research: Design and methods. 4th ed. Thousand Oaks, CA: Sage; 2009. [ Google Scholar ]
• Yin R. K. Applications of case study research. 3rd ed. Thousand Oaks, CA: Sage; 2012. [ Google Scholar ]

Case Study vs. Research: What's the Difference?

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Case Study vs. Experiment

What's the difference.

Case studies and experiments are both research methods used in various fields to gather data and draw conclusions. However, they differ in their approach and purpose. A case study involves in-depth analysis of a particular individual, group, or situation, aiming to provide a detailed understanding of a specific phenomenon. On the other hand, an experiment involves manipulating variables and observing the effects on a sample population, aiming to establish cause-and-effect relationships. While case studies provide rich qualitative data, experiments provide quantitative data that can be statistically analyzed. Ultimately, the choice between these methods depends on the research question and the desired outcomes.

Further Detail

Introduction.

When conducting research, there are various methods available to gather data and analyze phenomena. Two commonly used approaches are case study and experiment. While both methods aim to provide insights and answers to research questions, they differ in their design, implementation, and the type of data they generate. In this article, we will explore the attributes of case study and experiment, highlighting their strengths and limitations.

A case study is an in-depth investigation of a particular individual, group, or phenomenon. It involves collecting and analyzing detailed information from multiple sources, such as interviews, observations, documents, and archival records. Case studies are often used in social sciences, psychology, and business research to gain a deep understanding of complex and unique situations.

One of the key attributes of a case study is its ability to provide rich and detailed data. Researchers can gather a wide range of information, allowing for a comprehensive analysis of the case. This depth of data enables researchers to explore complex relationships, identify patterns, and generate new hypotheses.

Furthermore, case studies are particularly useful when studying rare or unique phenomena. Since they focus on specific cases, they can provide valuable insights into situations that are not easily replicated or observed in controlled experiments. This attribute makes case studies highly relevant in fields where generalizability is not the primary goal.

However, it is important to note that case studies have limitations. Due to their qualitative nature, the findings may lack generalizability to broader populations or contexts. The small sample size and the subjective interpretation of data can also introduce bias. Additionally, case studies are time-consuming and resource-intensive, requiring extensive data collection and analysis.

An experiment is a research method that involves manipulating variables and measuring their effects on outcomes. It aims to establish cause-and-effect relationships by controlling and manipulating independent variables while keeping other factors constant. Experiments are commonly used in natural sciences, psychology, and medicine to test hypotheses and determine the impact of specific interventions or treatments.

One of the key attributes of an experiment is its ability to establish causal relationships. By controlling variables and randomly assigning participants to different conditions, researchers can confidently attribute any observed effects to the manipulated variables. This attribute allows for strong internal validity, making experiments a powerful tool for drawing causal conclusions.

Moreover, experiments often provide quantitative data, allowing for statistical analysis and objective comparisons. This attribute enhances the precision and replicability of findings, enabling researchers to draw more robust conclusions. The ability to replicate experiments also contributes to the cumulative nature of scientific knowledge.

However, experiments also have limitations. They are often conducted in controlled laboratory settings, which may limit the generalizability of findings to real-world contexts. Ethical considerations may also restrict the manipulation of certain variables or the use of certain interventions. Additionally, experiments can be time-consuming and costly, especially when involving large sample sizes or long-term follow-ups.

While case studies and experiments have distinct attributes, they can complement each other in research. Case studies provide in-depth insights and a rich understanding of complex phenomena, while experiments offer controlled conditions and the ability to establish causal relationships. By combining these methods, researchers can gain a more comprehensive understanding of the research question at hand.

When deciding between case study and experiment, researchers should consider the nature of their research question, the available resources, and the desired level of control and generalizability. Case studies are particularly suitable when exploring unique or rare phenomena, aiming for depth rather than breadth, and when resources allow for extensive data collection and analysis. On the other hand, experiments are ideal for establishing causal relationships, testing specific hypotheses, and when control over variables is crucial.

In conclusion, case study and experiment are two valuable research methods with their own attributes and limitations. Both approaches contribute to the advancement of knowledge in various fields, and their selection depends on the research question, available resources, and desired outcomes. By understanding the strengths and weaknesses of each method, researchers can make informed decisions and conduct rigorous and impactful research.

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Frequently asked questions

What’s the difference between action research and a case study.

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

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 .

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.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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 several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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The power of combining real and synthetic respondents in market research.

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Abigail Stuart, with 20+ years in brand and market research, drives innovation and champions AI. Connect on LinkedIn .

In the 2013 science fiction film Her , set in the near future, a lonely writer named Theodore develops an unexpected and profound relationship with an artificially intelligent virtual assistant designed to meet his every need. Theodore is captivated by her ability to learn, adapt and exhibit human-like psychological growth. This portrayal of human-AI interaction is no longer confined to science fiction. The rise of artificial intelligence and machine learning has led to the creation and use of synthetic humans —digital beings engineered through artificial intelligence to resemble and behave like humans in appearance, personality and intelligence.

This evolution in AI technology extends beyond personal relationships and into various industries, including market research. Just as synthetic humans can mimic real people in terms of interaction and behavior, synthetic respondents have the potential to revolutionize the field of market research. These virtual beings can simulate the opinions, preferences and responses of real people, providing new opportunities and insights for researchers.

However, synthetic respondents are not without controversy and are being hotly debated within the market research community. Examining the comments left on LinkedIn posts about these innovations reveals a clear divide in opinions. Proponents are enthusiastic about the potential for this innovation to deliver cost-effective and efficient ways of gathering customer feedback. Critics argue about the authenticity and reliability of data derived from synthetic respondents, and there is a growing concern among market research professionals that synthetic respondents might overshadow or even replace traditional methodologies. A recent article published by Raconteur summarizes some of the key points.

These perspectives both miss the broader point: Synthetic respondents should be seen as complementary tools that enhance and augment real respondents, not as replacements. Synthetic data is generated based on existing patterns and trends, meaning it cannot capture novel behaviors or emerging trends that have not been previously recorded. Furthermore, synthetic data, no matter how well-crafted, lacks the nuanced insights that come from engaging with real people.

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Ukrainian jets join the invasion of russia’s kursk oblast—and blow up a russian command post, northern lights alert: where the aurora borealis may be visible tonight, the reality: complementary strengths.

To fully harness the power of synthetic respondents, they should always be integrated with traditional market research. Here’s why:

Learning Loops

One of the significant advantages of synthetic respondents is their ability to expedite the research process through the creation of learning loops. When used alongside real respondents, synthetic respondents help accelerate the research timeline by providing initial insights that can be rapidly tested and refined. This iterative process, combining synthetic and real data, allows researchers to learn and adapt quickly, ensuring that the depth and reliability of insights are not compromised but enhanced, ultimately leading to faster and more robust conclusions.

Data Diversity

Much of today's market research is conducted online with samples drawn from panels of market research respondents, often leading to a natural bias in the sample. Synthetic respondents offer the opportunity to reach niche audiences and uncover opinions from more diverse populations. By simulating a wide range of demographic and psychographic profiles, synthetic respondents help mitigate sample bias and provide insights from a broader spectrum of perspectives, enriching the overall quality and inclusiveness of your research.

Of course, synthetic respondents are no substitute for engaging with real people in niche audiences and diverse populations. Synthetic data can replicate existing opinions and behaviors, but it is less effective at predicting new behaviors. Therefore, it is essential to validate synthetic responses with the views and opinions of real individuals. However, this can often be accomplished with a smaller sample size than what is typically required in traditional market research.

No Respondent Fatigue

Synthetic respondents have limitless capacity to answer your questions. Unlike real respondents, synthetic respondents never experience fatigue or boredom, allowing you to explore a wider array of queries and test a multitude of ideas. This opens up the opportunity to gather feedback on hundreds of concepts without worrying about respondent fatigue.

Case Study: Exploring The Patient Journey Of Those With A Rare Disease

To illustrate the power of combining synthetic and real respondents, consider a recent project we conducted for a pharmaceutical client. Gathering insights from patients suffering from rare diseases can be particularly challenging, often requiring weeks or months and considerable effort and cost to speak to a handful of patients. When one of our pharma clients tasked us with conducting patient journey research in acute myeloid leukemia (a rare cancer affecting a small percentage of the U.S. population), we needed to be creative.

We initiated the research using ChatGPT, creating patient personas that mirrored the characteristics of real patients. Through in-depth simulated interviews, we explored their experiences and key milestones along their journey. These simulations allowed our client to quickly gain a broad understanding of patient needs, pain points and desires.

Next, we focused on the most relevant areas identified in the simulations and conducted interviews with real patients to validate the insights and enrich the findings with their unique experiences and detailed stories. This approach provided a comprehensive and nuanced view of the patient journey, blending the efficiency of synthetic data with the depth of real patient insights.

Let’s stop pitting synthetic respondents against insights gleaned from real people. In reality, they can be incredibly powerful when used together. AI simulations are not replacements for the continuous discovery of insights from real individuals; rather, they are tools to enhance creativity and guide human research.

We've found that combining AI-powered simulations with focused human discovery leads to sharper insights and quicker iterations. Synthetic respondents can accelerate the initial stages of research, provide broad and diverse perspectives, and help formulate hypotheses. When these synthetic insights are validated and enriched with real-world data from actual respondents, the results are both comprehensive and nuanced.

The greatest successes will come to those who can thoughtfully integrate human and machine intelligence. It's not about picking one or the other but harnessing the strengths of both. By leveraging the power of synthetic respondents alongside traditional research methods, we can achieve a deeper, more holistic understanding of our subjects, ultimately driving more effective and impactful strategies in market research.

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Development Studies Association » News & Events » CDS University of Bath » Postdoctoral scholarships to promote careers in development studies: CDS experience and wider issues

Postdoctoral scholarships to promote careers in development studies: CDS experience and wider issues

In this blog summary of a recent University of Bath BRID Working Paper , Asha Amirali from the Centre for Development Studies (CDS) shares conclusions from a study of postdoctoral researcher experience at the University of Bath. Co-authored by herself and James Copestake, the paper reflects on how to support development studies research activities and careers. It reaffirms the case for helping promising early career researchers to build upon and move beyond their doctoral work, the value of research autonomy at this stage, and balancing research autonomy with membership of relevant specialist clusters and networks.

Now is not a good time to be an early career researcher in development studies. Quite apart from the on-going ontological flux in the field, we have a labour market problem on our hands: the number of PhDs seeking university employment far outstrips the number of jobs available, and it is the marketisation of higher education over the last twenty years that is largely to blame. Practising academic birth control and limiting doctoral intake is not the most feasible nor the most desirable solution because of the very real problem that development studies – already defined and debated by an elite intellectual community – would become even more exclusive. Limiting PhDs also assumes that we have too many PhDs – a question worth debating – rather than a major shortfall in public funding for universities and an absence of doctoral training programs fostering skill transfer.

Prompted by two simultaneous concerns – the crisis of employability and inclusion in the field and how best to ensure mutual benefit to postdocs and development studies institutions – we conducted an evaluation of a two year postdoctoral fellowship program at the University of Bath’s Centre for Development Studies (CDS). The purpose was to see how a) these fellowships had enabled researchers to develop their career chances and b) generate feedback on how future postdoctoral fellowships might be better managed by CDS. Our interest subsequently grew in the wider relevance of our experience for development studies as an academic field, both in the UK and more widely. We also recognized the potential value of sharing findings more widely, including with students still completing their doctorate or contemplating the idea of doing one.

The CDS postdoctoral program is a rare opportunity to pursue independent postdoctoral research in an increasingly project-dominated field. Since 2012, it has offered six successive postdocs – including myself – two years of paid research time with a small (0.1 FTE) administrative requirement. We spoke to the CDS directors, a small number of senior development studies colleagues around the UK, and each of the postdocs for this exercise. Four former postdocs have taken up tenured and non-tenured positions in UK development studies, one took up a senior position in an Indian consultancy firm before his tragic death in 2021 during the Covid-19 pandemic, and I am still in post at CDS.

Reflecting on the experiences gathered led us to the following conclusions:

Fellowships are gold dust

Fellowships enabling independent research are unambiguously valuable for recipients – ‘gold dust’ as a postdoc described it. Most postdoctoral posts are currently explicitly linked to specific research projects, and this can help to ensure that early career researchers receive adequate intellectual mentorship and are not intellectually isolated. But it is important also to ensure that postdoctoral opportunities do not overly limit the autonomy of early career researchers.

Research autonomy is perhaps particularly useful in development studies because development studies addresses problems that are multifaceted, lack clearly defined system boundaries, and are subject to rapid change. In contrast to more established and stable academic fields therefore, there are particularly strong gains – to intellectual flexibility, interdisciplinarity, and originality for example – to be had from relatively unstructured research positions. All postdocs greatly appreciated the freedom and used it to consolidate and/or expand their doctoral work, develop new research directions, expand their networks, and recover from and integrate what was often an intense doctoral experience.

Research fit matters

Research fit matters, both for postdocs and the institution . Postdocs’ network development was quite variable and shaped by the degree of overlap between on-going work at CDS and their research interests. Greater overlap generally led to closer and more collaborative connections within CDS and Bath. The strength of guidance and support each postdoc secured within CDS also varied according to their research interests; those with complementary and closely-aligned research interests found themselves generally better supported.

For CDS too, strong overlaps led to more collaborations while injecting energy into already-existing research clusters. Although selection criteria for the fellowship were extensively debated before recruitment, with the benefit of hindsight we conclude that the program worked best when recruiting highly promising individuals whose interests closely aligned with established areas of CDS expertise. This approach does however risk crowding out less widely funded and thus ‘minority’ research themes and approaches (an issue that we did not have scope to take up in this study but that is obviously important).

Administrative tasks can be useful for career development

Administrative tasks are not always bad . They may be nobody’s favorite, but administrative tasks that foster relevant skills are in fact often useful for career development, e.g.: co-managing and editing working papers series’, helping organize workshops and seminars, participation in academic committees, and so on. Postdocs in our study were glad to have had these experiences – along with small bits of teaching – as part of the 0.1 FTE requirement and were of the opinion it bolstered their capability within academic environments. However, there is a clear danger of over-burdening early career researchers with excessive and unhelpful administrative support work, and researchers, their managers, and senior staff must ensure that the balance does not tip in this direction.

What to prioritise?

Decisions to prioritise applying for funding versus publishing need careful consideration at postdoctoral stage. All postdocs faced the dilemma of what to prioritise. Grant writing is often a riskier proposition compared to bolstering academic job prospects through publication, but the research and career gains of winning grants are strong. Each postdoc found a different balance and it is impossible to say what works best, except that taking stock of priorities at regular intervals and discussing them with peers and mentors – in person whenever possible – is vital.

More than one pathway

Postdoctoral opportunities such as this one are not always pathways to success. The fact remains that there are large and growing numbers of PhDs chasing a small number of jobs. The reflective/reflexive exercise that we undertook led us to think that there is need for further research into postdoctoral career pathways and the balance between funding of doctoral and postdoctoral positions. If the number of available development studies posts is lagging far behind the number of PhDs wishing to join as university-based scholars, then perhaps we need to radically rethink the nature of early career research training. This is linked to discussion over the balance between traditional and academic career-oriented PhD programmes, other forms of doctoral provision including professional doctorates, and non-doctorate based research, training and collaboration between universities and development organisations including placements and knowledge transfer partnerships. Additionally, the possibility and implications of shifting some financial support from doctoral to postdoctoral positions needs consideration.

Ours was a small effort to generate insight into the experience of the CDS postdoctoral program and cannot as such speak to the many complex problems of development studies. As we write, ‘thinking about development studies often resembles the task of peeling off multiple layers of a large onion. In this case layers include:

• interpersonal relationships between early and late career researchers;
• intraorganizational issues, including the corporate interests of large universities and the only partly autonomous research groupings within them;
• wider institutional issues concerning who benefits, and how, from current norms for doctoral and postdoctoral research as stepping stones towards academic careers;
• the epistemic identity, status, limitations, and influence of different disciplines and their contribution to global understanding; and
• the geo-politics of all the above as played out across richer and poorer countries in the Global North and South’.

Conversations abound on many but not all of these, and the lens of postdoctoral experience and the question of who gets to join the development studies intellectual community brings them all into focus. We hope to initiate discussion on how these issues shape the social network of development studies and thus the field itself, having drawn attention to the precarity of doctoral and postdoctoral pathways and reiterated the case for funded autonomous postdoctoral research.

This article gives the views of the author/academic featured and does not represent the views of the Development Studies Association as a whole.

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“unity in diversity reflecting on the future of development studies”, call for book chapters following dsa2024 presentation, cidt – a small institute punching above its weight, soas august news, support for masters’ applicants affected by displacement, odid, oxford, august 2024.

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1982: plyler v. doe.

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"By denying these children a basic education, we deny them the ability to live within the structure of our civic institutions, and foreclose any realistic possibility that they will contribute in even the smallest way to the progress of our Nation." —Justice Brennan

In 1975, the State of Texas enacted section 21.031 of the Texas Education Code permitting public school districts to deny admission or charge tuition to undocumented children. However, a 5-4 U.S. Supreme Court decision in ; Plyler v. Doe struck down this section and ruled all children—regardless of their citizenship—were entitled to free public education under the Equal Protection clause of the 14th Amendment

In1977, Tyler Independent School District charged $1000 per year to each child enrolled within the district who did not provide documentation of American citizenship. The Texas legislature’s section 21.031 justified this decision, stating that free public education was only accessible to children residing legally within the state’s borders. Peter Roos and Vilma Martinez from the Mexican American Legal Defense and Educational Fund (MALDEF) brought a class action suit to the district court.

According to the State of Texas, undocumented children were not “within the jurisdiction” of the state and thus they were disqualified from public education. William Wayne Justice, the federal district judge, declared Texas’ rationale from section 21.031 unconstitutional. Tyler ISD appealed to the fifth circuit court of appeals, which reaffirmed the lower court’s decision by finding a violation of rights and equal protection. The school district disagreed with the court's ruling and appealed to the U.S. Supreme Court.

On June 15, 1982, Justice Brennan wrote the majority decision, which ruled against Texas’ policy of denying or charging admission to undocumented children, and noted that the Equal Protection under the 14th Amendment are provisions that are “universal in their application, to all persons within the territorial jurisdiction, without regard to any differences of race, color, or nationality”. Additionally, the court held that states could not deprive children from an education, request citizenship documentation,or hold children liable for their parent’s actions.

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• U.S. Reports: Plyler v. Doe, 457 U.S. 202 (1982) View a digitized version of the Plyler v. Doe decision (U.S. Reports Collection, Law Library of Congress).

Staff in the Hispanic Reading Room can provide access to these books at the Library of Congress. If you cannot visit the Library in person, please contact us using Ask a Librarian for assistance. In many cases, you can also find these materials at your local library.

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The following external websites can be useful for expanding your research on Plyler v. Doe.

• Plyler v. Doe (Website) External Summary of a Fourteenth Amendment Landmark case: Plyler v. Doe by United States Courts
• Plyler v. Doe: The Landmark MALDEF Case That Changed Education in America (Website) External Overview and timeline of Plyler v. Doe by MALDEF
• Plyler v. Doe Decision (Website) External Transcript and decision of the Plyler. Doe Decision provided by Cornell Law
• Public Education for Immigrant Students: Understanding Plyler v. Doe (Website) External Understanding Plyler v. Doe Fact Sheet by the American Immigration Council
• A Supreme Court case 35 years ago yields a supply of emboldened DACA students today (Website) External Interviews with individuals involved in Plyler v. Doe by APM Reports
• H.Res.168 - A resolution that United States District Court Judge William Wayne Justice is impeached of high crimes and misdemeanors (Website) External Legislation on the impeachment of Judge Justice following his rulings against racial segregation in schools provided by Congress

Texas. General Land Office, W. C Walsh, and August Gast & Co. Map of Tyler County, Texas. 1879. Library of Congress Geography and Map Division

Carol Highsmith, photographer. Courtroom. U.S. Post Office and Courthouse, Tyler, Texas. 2014. Library of Congress Hispanic Division

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Rape-Related Pregnancies in the 14 US States With Total Abortion Bans

• 1 Planned Parenthood of Montana, Billings, Montana
• 2 Resound Research for Reproductive Health, Austin, Texas
• 3 Hunter College, City University of New York, New York
• 4 Department of Medicine, Cambridge Health Alliance, Cambridge, Massachusetts
• 5 Department of Medicine, University of California, San Francisco
• Editor's Note Access to Safe Abortion for Survivors of Rape Deborah Grady, MD, MPH; Sharon K. Inouye, MD, MPH; Mitchell H. Katz, MD JAMA Internal Medicine
• Medical News in Brief 65 000 Rape-Related Pregnancies Took Place in US States With Abortion Bans Emily Harris JAMA
• Correction Error in Methods, Results, and Table 2 JAMA Internal Medicine

Many US women report experiencing sexual violence, and many seek abortion for rape-related pregnancies. 1 Following the US Supreme Court’s 2022 Dobbs v Jackson Women’s Health Organization ( Dobbs ) decision overturning Roe v Wade , 14 states have outlawed abortion at any gestational duration. 2 Although 5 of these states allow exceptions for rape-related pregnancies, stringent gestational duration limits apply, and survivors must report the rape to law enforcement, a requirement likely to disqualify most survivors of rape, of whom only 21% report their rape to police. 3

• Editor's Note Access to Safe Abortion for Survivors of Rape JAMA Internal Medicine

Read More About

Dickman SL , White K , Himmelstein DU , Lupez E , Schrier E , Woolhandler S. Rape-Related Pregnancies in the 14 US States With Total Abortion Bans. JAMA Intern Med. 2024;184(3):330–332. doi:10.1001/jamainternmed.2024.0014

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Graph Neural Network Approach to Predict the Effects of Road Capacity Reduction Policies: A Case Study for Paris, France † † thanks: Paper submitted for presentation at the 104 th Annual Meeting of the Transportation Research Board, Washington D.C., Jan. 2025

Rapid urbanization and growing urban populations worldwide present significant challenges for cities, including increased traffic congestion and air pollution. Effective strategies are needed to manage traffic volumes and reduce emissions. In practice, traditional traffic flow simulations are used to test those strategies. However, high computational intensity usually limits their applicability in investigating a magnitude of different scenarios to evaluate best policies. This paper introduces an innovative approach to assess the effects of traffic policies using Graph Neural Networks (GNN). By incorporating complex transport network structures directly into the neural network, this approach could enable rapid testing of various policies without the delays associated with traditional simulations. We provide a proof of concept that GNNs can learn and predict changes in car volume resulting from capacity reduction policies. We train a GNN model based on a training set generated with a MATSim simulation for Paris, France. We analyze the model’s performance across different road types and scenarios, finding that the GNN is generally able to learn the effects on edge-based traffic volume induced by policies. The model is especially successful in predicting changes on major streets. Nevertheless, the evaluation also showed that the current model has problems in predicting impacts of spatially small policies and changes in traffic volume in regions where no policy is applied due to spillovers and/or relocation of traffic.

1 Introduction

The problem of urbanization presents significant challenges for cities worldwide, including increased traffic congestion and severe air pollution. These issues are exacerbated as urban populations grow, with 55% of the world’s population currently living in urban areas, a figure expected to rise to 68% by 2050 [ 1 ] . This rapid urbanization, combined with overall population growth, could add another 2.5 billion people to urban areas by mid-century [ 2 ] , further intensifying these problems. As cities continue to expand, they face the daunting task of managing increasing traffic volumes and the associated rise in emissions. Effective strategies are needed to reduce car use while ensuring flexibility for residents. Commonly employed policies include congestion charging, parking and traffic control measures, the establishment of limited traffic zones or rededicate space allocated for cars.

Therefore, authorities must carefully decide which policy to introduce in which region of the city. In practice, traffic flow simulations are used to evaluate the impact of those policies. However, these simulations are typically unavailable for small to medium-sized cities as they require extensive data as input and expert knowledge for creation. Additionally, significant computational effort is required to run single simulations. This limitation hampers the ability to test a wide range of policies and determine the most effective ones, especially when considering district-specific implementations in large cities.

In this paper, we introduce a novel machine learning approach to assess the effects of traffic policies. By training a Graph Neural Network (GNN), complex network structures of the transportation system are directly incorporated into the neural network. The vision of this approach is to enable rapid testing of various policies without the lengthy delays associated with traditional simulations. By narrowing down the space of possibilities, a machine learning based model can offer a practical and efficient alternative for urban planners, facilitating more effective and timely decision-making in the management of urban transportation systems.

The goal of this paper is to provide a proof of concept if GNNs are capable of learning the impacts of policies, while a generalized model will be provided in future research. We train and test the model on a transport simulation of Paris. By conducting extensive simulations with capacity reduction policies in place, a database is created to learn resulting traffic flows on edge level in the network. The results demonstrate that the trained GNN can accurately predict traffic flow under policies, proofing the applicability of GNNs for this task. Nevertheless, the GNN sometimes fails in predicting displacement traffic flow in regions outside applied policies.

This paper is organized as follows: The next section presents the literature review. Subsequently, we describe the developed method, i.e., the architecture of the GNN and used evaluation metrics. We then describe the case-study based on a MATSim simulation for Paris, France to test the method. Following this, we present the results. Finally, the conclusion highlights the insights gained, limitations of the proposed method, and future research directions.

2 Literature Review

To decrease car usage and thereby emissions and noise, numerous European cities implemented regulations either on neighborhood or even city scale  [ 3 ] . These regulations range from low emission zones, urban road tolls, parking restrictions or rededicating lanes to other modes of transport like public transport, bicycles, and pedestrian pathways. London and Stockholm are well known examples for urban toll systems in Europe, while the introduction of the tolling system for Manhattan, New York recently received general attention   [ 4 ] . In Paris, the city introduced numerous cycleways under the “Vélo I and II”, consistently reducing at least one car lane to create “Pistes cyclables”  [ 5 ] . Especially in the European Union, further regulation can be expected as new strict limits on air pollution have been set  [ 6 ] . To cope with these challenges, methods need to be developed to evaluate the impacts of different regulations to sketch out ways to a sustainable urban mobility without restricting it.

A popular method to evaluate the impacts of regulations are agent-based simulations, like MATSim  [ 7 ] , Polaris  [ 8 ] , SimMobility  [ 9 ] or mobiTopp  [ 10 ] . The advantage of agent-based simulations is that the complex interaction between demand and supply in mobility systems can be represented with single agents being able to adopt their trip, i.e. by re-routing or mode choice adoptions if supply or demand configurations are changed to reach a new user equilibrium. Therefore, applications range from the evaluation of parking pricing schemes for Zurich, Switzerland  [ 11 ] , low traffic zones for Paris, France  [ 12 ] , Superblocks (traffic management schemes in dense urban neighborhoods) in Vienna, Austria  [ 13 ] and Barcelona, Spain   [ 14 ] , or perimeter control measures for Christ Church, New Zealand. Also congestion pricing or tolling systems have been studied extensively, for example for New York, US  [ 15 ] or Munich, Germany  [ 16 ] .

The disadvantage of agent-based simulations is a high run-time of usually several hours, limiting the amount of scenarios that can be tested. An alternative is to apply optimization based models to directly evaluate optimal policies (or design strategies) for a given network, usually referred to as Network Design Problem (NDP)  [ 17 , 18 ] traditionally evaluating the effect of network size on congestion and traveler choices with further extensions to public transport networks (e.g.  [ 19 , 20 , 21 ] ). A lot of research focus has been put in recent years in improving and extending these models. This includes optimizing urban road space for multimodal networks (e.g.  [ 22 ] ), and developing optimal road pricing schemes (e.g.  [ 23 , 24 ] ). Nevertheless, as the these optimization-based models are notoriously hard to solve, strong modelling assumptions on user behavior simplifying the problem or rather small system sizes are applied. To combine a structured search for optimized policies with the advantages of agent-based simulations, Bayesian Optimization has been suggested to limit the search space for simulation runs (e.g.  [ 25 , 26 ] ).

Shulajkovska et al. developed an open-data, open-source smart-city framework designed to enhance decision-making in European cities. Similar to this paper, their approach utilizes MATSim simulation outputs as a foundation for their machine learning algorithms. One of their key innovations is significantly speeding up policy testing for decision-makers, reducing the time required for a single policy verification from 3 hours to approximately 10 seconds. [ 27 ]

The machine learning algorithms they tested include logistic regression, decision trees, and Bayesian ridge regression. However, these algorithms do not fully leverage the complex structures of urban networks. This study proposes using Graph Neural Networks (GNNs) to effectively manage intricate network structures for predicting traffic behavior at the edge level.

Jiang et al.’s surveyed recent studies on traffic forecasting with GNNs, offering resources and identifying research challenges and opportunities in the field [ 28 ] . Li et al. used Graph Convolutional Neural Networks to predict short-term citywide traffic demand using Graph Convolutional Networks (GCNNs). They developed a data-driven graph convolutional network (DDGCNN) that outperforms other predictors, particularly with weighted adjacency matrices, in capturing correlations between sub-regions [ 29 ] . Lastly, Liu and colleagues introduce SimST, a spatio-temporal learning approach that models spatial correlations and achieves comparable performance to Spatio-Temporal Graph Neural Networks (STGNNs) while improving prediction throughput. This suggests that GNNs may not be the only effective option for spatial modeling in traffic forecasting [ 30 ] .

While GNNs gained attention in recent years, they have been mainly applied for short-term traffic prediction. This study evaluates the feasibility of learning long-term effects of traffic policies with GNNs potentially providing significant advantages for transportation planners in the evaluation of possible policies in a city.

We propose the following method, as illustrated in Figure 1 : An agent-based demand model (in this case, a MATSim simulation model) is used to train the Graph Neural Network (GNN). The road network defines the input graph to the GNN. Static (scenario independent) edge information include road type of each edge, coordinates of the midpoint to provide spatial information, and the car volume in the status quo, i.e. in the scenario without any policy applied.

To evaluate the general methodology, we focus on testing a policy involving the reduction of road capacity for motorized vehicle traffic. Capacity reduction can have different interpretations for real world implementation: For instance, that traffic-calming measures such as bumpers or street bays have been implemented. Link capacities can also be reduced by removing lanes, for example to rededicate space for additional bike lanes. Other measures could be the change of traffic light intervals, the introduction of crosswalks, the addition of parking spots, or the reduction of maximum speed. A reduction of link capacities can, hence, be seen as a generic representation of various traffic-related policies.

To create training data, different sets of policy scenarios (in our case, spatial variations of edge capacity reductions) are created, and the agent-based simulation is run. Under the active policy, agents in the simulation can take other routes and/or use different modes of transport for their trips to minimize their travel costs. Therefore, the simulation model can capture impacts of the policy, including but not limited to changes in car volumes per edge.

Next to static edge features, the active policy per edge (in our case the reduction in edge capacity) is a variable (policy scenario dependent) input to the GNN. The goal of the GNN is to predict car volumes per edge. While the simulation output for different policy scenarios are used to train the GNN, the goal is to predict edge car volumes for unseen policy scenarios.

The approach generally allows learning the impacts of various types of policies by extending corresponding edge and/or node input attributes, as long as it is possible to implement it in the simulation model.

A reduced capacity can mean, for instance, that traffic-calming measures such as bumpers or street bays have been implemented. Link capacities can also be reduced by removing lanes, for instance, for the installation of bike lanes. Other measures could be the change of traffic light intervals, the introduction of crosswalks, the addition of parking spots, or the reduction of maximum speed. A reduction of link capacities can, hence, be seen as a generic representation of various traffic-related policies.

3.1 Network Representation

Graph Neural Networks operate by taking multiple graphs as input; nodes represent entities and edges represent relationships between these entities. Through a series of message-passing layers, nodes aggregate information from their neighbors. This iterative process allows nodes to update their feature representations based on the structure of the graph and the features of their connected nodes, ultimately leading to a comprehensive embedding that captures both local and global graph information. These embeddings can then be used for various tasks such as node classification, link prediction, and graph classification.

In our use case, we aim to predict the change of car volume on all edges due to a given policy. Road networks of cities form a natural graph, where edges represent roads and nodes represent intersections. Since Graph Neural Network message-passing layers primarily focus on predicting node features rather than edge features, we propose using the line graph (or “dual”) of the street graph. In this line graph, nodes correspond to road segments, and edges represent the connectivity between these road segments. The goal is to predict the change in car volume at the edge level after implementing specific policies. The steps are as follows:

Policy Selection: Decide on the policies to test and identify the districts where these policies will be applied.

Scenario Generation: Generate scenarios based on these policies to be simulated with a simulation tool of choice.

Simulation Execution: Run these scenarios using a simulation tool, such as MATSim.

Graph Representation: For each simulation output corresponding to a policy implementation, create a graph representation of the dual of the resulting street network. These graph representations together form the resulting dataset.

GNN Training and Validation: Split the dataset into training, testing, and evaluation sets. Feed the training and validation sets into the Graph Neural Network (GNN) for training and validation.

GNN Testing: Use the trained GNN to test other scenarios and predict the changes in car volumes on road segments due to the implemented policies.

3.2 Evaluation metrics

For training the Graph Neural Network and validating the results, we use two evaluation metrics: Mean Squared Error (MSE) and the coefficient of determination ( R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ). We optimize the loss using MSE during training, while R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT is used for additionally evaluating the model’s performance. Both metrics are commonly used for regression tasks. Table 1 lists the variables used.

3.2.1 Mean Squared Error

The MSE for the change in car volume is defined in the following way:

M ⁢ S ⁢ E ⁢ ( v , v ^ ) 𝑀 𝑆 𝐸 𝑣 ^ 𝑣 MSE(v,\hat{v}) italic_M italic_S italic_E ( italic_v , over^ start_ARG italic_v end_ARG ) refers to the error predicting the overall car volume on edge level. Note that M ⁢ S ⁢ E ⁢ ( y , y ^ ) 𝑀 𝑆 𝐸 𝑦 ^ 𝑦 MSE(y,\hat{y}) italic_M italic_S italic_E ( italic_y , over^ start_ARG italic_y end_ARG ) is equivalent to M ⁢ S ⁢ E ⁢ ( v , v ^ ) 𝑀 𝑆 𝐸 𝑣 ^ 𝑣 MSE(v,\hat{v}) italic_M italic_S italic_E ( italic_v , over^ start_ARG italic_v end_ARG ) :

In other words, the error in predicting the change in car volume due to an intervention is equivalent to the error in predicting the absolute car volume after the intervention.

3.2.2 Coefficient of determination ( R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT )

We call the differences between the observed values and the mean of the observed values S ⁢ S t ⁢ o ⁢ t 𝑆 subscript 𝑆 𝑡 𝑜 𝑡 SS_{tot} italic_S italic_S start_POSTSUBSCRIPT italic_t italic_o italic_t end_POSTSUBSCRIPT :

The ratio of these two quantities defines how well the predictions are in comparison to the mean. In its most general form, R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT is defined as:

It is evident that if y ^ e = y e subscript ^ 𝑦 𝑒 subscript 𝑦 𝑒 \hat{y}_{e}=y_{e} over^ start_ARG italic_y end_ARG start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT = italic_y start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT for all e ∈ E 𝑒 𝐸 e\in E italic_e ∈ italic_E , then R 2 = 1 superscript 𝑅 2 1 R^{2}=1 italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT = 1 . Conversely, if y ^ e = y ¯ subscript ^ 𝑦 𝑒 ¯ 𝑦 \hat{y}_{e}=\overline{y} over^ start_ARG italic_y end_ARG start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT = over¯ start_ARG italic_y end_ARG for all e ∈ E 𝑒 𝐸 e\in E italic_e ∈ italic_E , then R 2 = 0 superscript 𝑅 2 0 R^{2}=0 italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT = 0 . Generally, if R 2 > 0 superscript 𝑅 2 0 R^{2}>0 italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT > 0 , the model predicts values more accurately than the mean of y 𝑦 y italic_y . Conversely, if R 2 < 0 superscript 𝑅 2 0 R^{2}<0 italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT < 0 , the model’s predictions are less accurate than the mean of y 𝑦 y italic_y .

In general, a higher R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT value indicates a better fit for the model. However, it is important to note that high R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT values do not necessarily imply the model is the best. Therefore, we use R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT in conjunction with the MSE to assess model performance comprehensively.

3.2.3 Baseline

We have outlined the criteria for evaluating our model’s predictions, focusing on MSE and R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT . A baseline serves as a reference point for comparing model performance. It sets the minimum performance standard that a predictive model should meet to be considered effective. Baselines are typically derived from simple methods and vary based on the problem type. Since this is the first approach to this topic, no specific baseline has been established yet. Therefore, we use the mean baseline for both MSE and R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT . The baseline for R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT is 0 0 : If y ^ e = y ¯ subscript ^ 𝑦 𝑒 ¯ 𝑦 \hat{y}_{e}=\overline{y} over^ start_ARG italic_y end_ARG start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT = over¯ start_ARG italic_y end_ARG for all e ∈ E 𝑒 𝐸 e\in E italic_e ∈ italic_E , then R 2 = 0 superscript 𝑅 2 0 R^{2}=0 italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT = 0 . For MSE, the baseline is:

Observe that this is equivalent to M ⁢ S ⁢ E ⁢ ( v ¯ , v ^ ) 𝑀 𝑆 𝐸 ¯ 𝑣 ^ 𝑣 MSE(\overline{v},\hat{v}) italic_M italic_S italic_E ( over¯ start_ARG italic_v end_ARG , over^ start_ARG italic_v end_ARG ) (see Equation 2 ).

3.2.4 Variance of Squared Difference

The variance of the MSE measures the diversity of changes in car volume across all edges due to the introduced policy. Given y e subscript 𝑦 𝑒 y_{e} italic_y start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT as the actual values for all e ∈ E 𝑒 𝐸 e\in E italic_e ∈ italic_E , and y ¯ ¯ 𝑦 \overline{y} over¯ start_ARG italic_y end_ARG as the mean of these. Then

d e = ( y e − y ¯ ) 2 subscript 𝑑 𝑒 superscript subscript 𝑦 𝑒 ¯ 𝑦 2 d_{e}=(y_{e}-\overline{y})^{2} italic_d start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT = ( italic_y start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT - over¯ start_ARG italic_y end_ARG ) start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT is the squared difference for each edge e ∈ E 𝑒 𝐸 e\in E italic_e ∈ italic_E .

d ¯ = 1 | E | ⁢ ∑ e ∈ E d e ¯ 𝑑 1 𝐸 subscript 𝑒 𝐸 subscript 𝑑 𝑒 \overline{d}=\frac{1}{|E|}\sum_{e\in E}d_{e} over¯ start_ARG italic_d end_ARG = divide start_ARG 1 end_ARG start_ARG | italic_E | end_ARG ∑ start_POSTSUBSCRIPT italic_e ∈ italic_E end_POSTSUBSCRIPT italic_d start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT is the mean of the squared differences.

Then the variance of the squared difference is:

A high variance indicates that the policy causes a wide range of changes in car volume on different edges. Conversely, a low variance suggests that the policy’s effect on car volume is more uniform across the network. This implies that the policy has a similar impact on all edges. Since the policy is applied only to a few neighboring districts and streets, a uniform effect across the network is not expected. With a small impact of a policy, the variance (and the baseline MSE) is small. In such cases, machine learning models typically struggle to learn effects because clear signals are missing.

4 Case-study

We evaluated our model based on a MATSim simulation for Paris, France. In our study, the policy implemented reduces the capacity of higher-order roads by 50%. Higher order roads are those roads which are classified as “primary”, “secondary” or “tertiary” in OpenStreetMap, and the correspondings links, i.e. “primary_link”. The reduction of capacities along the network link can be interpreted in multiple ways in terms of real-world implementations.

In the following, first the simulation is described, followed by the generation of training data and finally the set-up of the GNN.

4.1 MATSim simulation

The presented learning method can be used with various simulation tools. In the present case, the agent-based transport simulation framework MATSim [ 7 ] has been chosen, as it is widely used for detailed transport modelling and standardized simulation data is available for a wide range of openly accessible used cases, such as for Paris [ 31 ] or Berlin [ 32 ] . Furthermore, MATSim is a highly flexible and modular framework that provides the functionality to simulate various elements of the transport system from specific services (on-demand mobility, micromobility, shared mobility, intermodality, …) and policies (road pricing, access restrictions, …) including their impacts and requirements related to traffic and travel behavior. MATSim performs simulations iteratively in two phases: The first phase performs a traffic simulation obtaining key information such as travel times per trip for all individual agent trips, while the second phase lets agents perform decisions related to their mobility plans. The present paper makes use of a specific configuration of MATSim in which discrete-choice models are used to simulate mode decisions of the agents [ 33 ] . The MATSim-based simulation originates from a large-scale agent-based transport simulation for the Île-de-France region around Paris, from which only the city perimeter has been cut. It is based on a standardized and replicable open-data process for the generation of a synthetic population for the region [ 33 ] . This synthetic population consisting of households, persons, and their daily activity patterns is transformed into a MATSim simulation including a detailed mode choice model based on French survey data [ 34 ] . Instantiations of the simulation have been used to study, for instance, the impact of autonomous taxis [ 35 ] and low traffic zones [ 12 ] . Since the purpose of the present paper is to present a first proof-of-concept of the learning process, down-sampled simulations of 0.1% of the households with accordingly scaled network capacities are used, which take about 20 minutes to run on a standard machine. OpenStreetMap (OSM) provides as a foundation for the MATSim road network. Based on OSM data, the considered region comprises approximately 31,000 edges and 20,000 nodes.

4.2 Generation of Training Data

Additionally to the policy scenarios, 50 simulation runs with different random seeds of the status quo scenario without policy are performed to evaluate a mean baseline car volume on each network edge. Note that due to computational limits for each policy scenario, only one seed is computed, resulting in inherent stochastic variation of the training data because of using a 0.1% travel demand subsample.

The different scenarios with their respective MATSim simulation outputs are randomly split into training, validation, and test sets with a ratio of 80%, 15%, and 5%, respectively.

4.3 Model set up and training process

The task of the GNN is to predict the change in car volume on an edge level due to a policy intervention. The features we use for each edge are as follows:

Car volume in the base scenario

Capacity in the base scenario

Highway classification, encoded appropriately

Geographic position

Capacity reduction due to the policy

We train our Graph Neural Network (GNN) using a carefully designed process to ensure stability and optimal performance. First, Standard Scaling normalization is applied to all feature variables, transforming the data so that each feature has a mean of 0 and a standard deviation of 1, which ensures consistent scaling across all features. The architecture of the network features a PointNetConvolution layer [ 36 , 37 ] with two perceptrons: one for local features (consisting of a single linear layer of size 256) and one multilayer perceptron for global features (comprising four linear layers of sizes 256, 512, 256, and 512). This is followed by Graph Attention Network (GAT) layers [ 38 ] with hidden sizes of 512, 512, 256, 128, and 64. The activation function we use is ReLu [ 39 ] . Overall, the resulting model has 833,411 parameters 1 1 1 The code for the Graph Neural Network can be found on https://github.com/enatterer/gnn_predicting_effects_of_traffic_policies .

To ensure stability and avoid issues like vanishing or exploding gradients, we begin with weight initialization. We initialize the linear and GAT layers with Xavier Normal [ 40 ] , and the PointNetConv with Kaiming Normal [ 41 ] . The learning rate increases linearly during a warm-up phase of 20,000 steps to 0.001. After the warmup phase, we apply a cosine decay schedule [ 42 ] to smoothly decrease the learning rate, which ensures stable training.

To manage memory constraints, we use a batch size of 8 and employ gradient accumulation, updating gradients every third step to simulate a larger batch size. Additionally, we use gradient clipping with a maximum norm of 1 to prevent exploding gradients.

We optimize the model using the AdamW optimizer [ 43 ] , with a weight decay (set at 1e-3) for better regularization.

4.3.1 Training process

The training runs for up to 2000 epochs, with early stopping if there is no improvement for 50 consecutive epochs, ensuring that the training concludes only when the model is fully optimized. We evaluate the method outlined in section 3 using the case study described in section 4 . The baseline for the model is 3.94 for the MSE, as stated in Equation 6 , and 0 0 for the coefficient of determination R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT (see section 3.2.3 ).

The training process is depicted in Figure 3 . Initially, the validation loss is around 4 4 4 4 , similar to the baseline. It drops to 3 3 3 3 after 60 60 60 60 epochs and to 2.6 2.6 2.6 2.6 after 220 220 220 220 epochs. Training slows, reaching 2.5 2.5 2.5 2.5 by 400 400 400 400 epochs and stabilizing at 2.4 2.4 2.4 2.4 by 880 880 880 880 epochs. The R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT score starts at 0 0 , increasing to 0.3 0.3 0.3 0.3 after 90 90 90 90 epochs, 0.35 0.35 0.35 0.35 after 300 300 300 300 epochs, and flattening at 0.4 0.4 0.4 0.4 by 750 750 750 750 epochs. The learning rate begins at 0 0 , reaching 0.001 0.001 0.001 0.001 by epoch 60 60 60 60 and then decreases due to the cosine decay scheduler, dropping to 0.0006803 0.0006803 0.0006803 0.0006803 after 800 800 800 800 epochs.

The training process converges after 880 880 880 880 epochs with a final validation loss of 2.40 2.40 2.40 2.40 and a final R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT score of 0.41 0.41 0.41 0.41 . The training lasted for 26 26 26 26 hours, on a single NVIDIA RTX A5000.

5.1 General insights

Applying the training process to our test set, we achieve an MSE of 2.35 2.35 2.35 2.35 and an coefficient of determination R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.41. Table 2 presents the evaluation metrics for the model’s performance across various scenarios. The table includes six columns: The road subset, on which the model is evaluated, the length (in km) of roads where a capacity reduction has been implemented, the variance of that road subset, the baseline MSE, the MSE that our model achieves, and R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT . The values for columns 2 - 6 are each averaged over the 245 graph observations in the test set.

The “Variance” column shows the variance of the data in each road set. The “MSE: Baseline” column presents the MSE for the baseline (see Equation 6 ), while the “MSE: Model” column presents the MSE for the trained model. The “ R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ” column shows the coefficient of determination R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT , our metric used for evaluating how well a regression model fits the actual data. Note that for all scenarios, the baseline R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT is 0 0 (see section 3.2.3 ).

For the entire road network (2,833 km), the variance is 311.06, with a baseline MSE of 4.00 and a model MSE of 2.36, resulting in an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.41. In primary roads (356 km) with a high variance of 726.39, the baseline and model MSEs are 10.15 and 5.27, respectively, yielding an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.48. Higher variance improves R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT by clarifying the impact of capacity changes. Secondary roads (299 km) show a variance of 106.29, with baseline and model MSEs of 4.23 and 2.97, and an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.30. Tertiary roads (284 km) have a variance of 98.88, baseline MSE of 3.38, model MSE of 2.28, and an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.32. Combined primary, secondary, and tertiary roads (940 km) have a variance of 364.55, baseline MSE of 6.33, model MSE of 3.68, and an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.42. Roads of other types (1894 km) show a variance of 260.41, baseline MSE of 2.29, model MSE of 1.39, and an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.39. For roads with capacity reduction (329 km, variance 130.12), baseline and model MSEs are 5.31 and 3.73, with an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.30. Roads without capacity reduction (2505 km, variance 350.49) have baseline and model MSEs of 3.78 and 2.12, and an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.43, showing better performance without capacity changes. In primary, secondary, and tertiary roads with capacity reduction (329 km, variance 130.11), baseline and model MSEs are 5.31 and 3.73, with an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.30. Those without capacity reduction (611 km, variance 486.56) have baseline and model MSEs of 6.85 and 3.65, with an R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.47, highlighting improved model performance with higher variance and no capacity reduction.

In conclusion, the model performs best on primary roads, likely due to the higher traffic volume, which show higher impacts from applied policies and allows for more accurate predictions considering less stochastic variation due to downscaled 0.1% simulations. It is also expected that the model achieves better performance on roads without capacity reduction, as only limited changes in traffic volume from the applied policies are observed for these sections. Generally, higher baseline MSE and variance correlate with increased prediction accuracy, indicating that the model’s predictions surpass the baseline. This is anticipated, as greater variance suggests that the effects of policy changes are more distinctly captured in the simulation outputs.

5.2 Insights for selected zones

In this section, we demonstrate the results for three selected zones: Zone 1 encompasses Arrondissements 1 to 4. These central districts, situated north of the Seine, represent the centre of Paris. Zone 2 comprises Arrondissements 5 to 7. Located south of the Seine, these districts are also quite central and have undergone significant revitalization efforts in the last decade Lastly, Zone 3 consists of Arrondissements 1, 2, 3, 4, 6, 8, 9, 11, 14 and 20. This combination spans across the central, eastern, and southern parts of the city. In recent years, Paris has experienced substantial changes of its road network supply, with the goal of drastically reducing emissions [ 44 , 5 ] . In reality, Zone 1 is the epitome of many interventions: it has been the focal point of Parisian policy interventions over the past decade. It was also one of the first zones to implement measures such as car-free Sundays in 2020, representing a “progressive transformation” of the city. Zone 2, located south of the Seine, has also undergone significant revitalization efforts, embodying transformation endeavors that reflect broader city improvements. Zone 3 is a unique combination of districts from the center, east, and south of the city, and is an example of the results of a random combination of districts, found in the test set, to provide a diverse perspective.

5.2.1 Visual Insights

In Figures 4 , 5 , and 6 , we compare the simulation output with the predictions of our model. The entire Paris street network is depicted in each image. The zone where a capacity reduction was implemented is outlined in black. The linewidth indicates the size of the street: trunk and primary streets have a thickness of 5, secondary streets have a thickness of 3, tertiary streets have a thickness of 2, and all other streets have a thickness of 1. The color indicates the percentage difference in car volume compared to the base case. For each zone, we used the same scale for both the actual simulation output and our predictions: -3% to 3% for Zones 1 and 2, and -5% to 5% for Zone 3. Values falling outside this range are represented by the color at the nearest scale boundary.

In all zones, the following observations can be made: Overall, it is evident that our model can learn the changes in car volume. The trend (whether positive or negative, represented in the plot by red or blue) is recognizable on most streets. The largest deviations between actual and predicted values are that the trends in the actual simulation output are usually stronger. This can be clearly seen as the colors in the “actual predictions” are more pronounced. It is also noticeable that the GNN finds it easier to identify a reduction in car volume compared to an increase in car volume. This can be explained by reductions in car volume occuring primarily on roads where the policy was directly implemented, making it a more straightforward pattern to detect, while increases in traffic volume yielding from spillovers and/or relocation of traffic by agents taking different routes. Note also that the magnitude of change in car volume (-3 to 3%, resp. -5 to 5%) is not particularly high, probably posing challenges for the model.

5.2.2 Numerical Insights

In Table 3 , we numerically evaluate the predictions for policies in the three example zones using the metrics MSE and R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT . As in Table 2 , we present the length of capacity reductions in kilometers, along with details on network and road scope, variance, baseline MSE, model MSE, and the model’s R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT . The network scope specifies whether the assessment covers all roads in Paris or is limited to those within the selected zone. The road scope indicates whether predictions are made for all roads, only primary roads, or exclusively primary, secondary, and tertiary roads.

In a first observation, the variance for the exemplary use cases for zone 1 and 2 is considerable lower compared to the variance in the whole data set (Table  2 A small baseline MSE, together with a low variance suggests that the policy’s impact in these use cases is small in comparison to the whole data set likely due to a bias of rather large zones. Consequently, our model struggles to learn a clear policy signal. The resulting decreased accuracy in predictions, also observable in Figure  4 and 5 provide a limitation of the current method. Only for primary roads with policy applied, a positive R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT can be observed.

Predictions for zone 3 on the other hand provide better results. The model’s best performance is seen for primary roads, where it achieves a positive R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT of 0.60, indicating a substantial improvement over the baseline. This underlines that the model is most effective in scenarios with higher variance, i.e. more substantial capacity changes, such as when capacity reductions are implemented across multiple zones.

6 Conclusion

This paper presents a first approach to applying Graph Neural Networks (GNNs) for predicting changes in car volume due to policy interventions, serving as a “Proof of Concept”. The results showed, it is generally possible to approximate the output of agent-based demand simulations using GNNs as in most scenarios significant improvement compared to a baseline can be observed. On the overall test set, our model achieves an MSE of 2.35 compared to a baseline of 4.0. For R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT , the baseline is 0, and our model achieves an accuracy of 0.41. Nevertheless, when policies are applied in exemplary smaller regions of the overall network, the model fails to predict policy effects accurately. On the positive side, the model performs generally better on higher road classes like primary and secondary road sections, which are often targeted, when policies are applied.

There are several challenges that are mandatory to be addressed in future work: First, due to constraints in computational time, we used down-sampled simulation representing only 0.1% of all households for this study, resulting in inherent stochastic noise in the training data. Lower sub-sampling rates and/or higher sample sizes are expected to improve the results. Second, the generation of simulation scenarios used for training the GNN can likely be improved. Our training included relatively large combinations of neighboring Arrondissements (see Section 4 ). The randomness of the selection process likely introduced a bias toward learning the effects of policies in larger district combinations. Third, in this study the whole network of Paris with over 31.000 edges is represented by the GNN. Further experiments have to be conducted whether only a subset of these sections is necessary to be represented to reduce the number of parameters in the model. Forth, the integration of further input features like spatial demand distributions or built environment might help learning the effects of policies.

Further, our machine learning model can undoubtedly benefit from further fine-tuning, which falls under the “Model Development” phase of machine learning. This includes, for example, more training data, additional loss terms and different evaluation metrics. For instance, one could incorporate different loss terms that can help capture the complexity of the task at hand, to see they change the learning process and whether they improve the model. Regarding evaluations, while MSE and R 2 superscript 𝑅 2 R^{2} italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT are standard evaluation metrics for regression tasks, they may not be the best fit for our specific use case. Exploring alternative metrics could provide more insights about model’s behaviour, which can lead to better proposed solutions. Finally, as discussed, increasing the amount of available data always improves model performance. While both computational and time expensive, increasing data richness - understood as percentage of population modelled - and data size, can give rise to better predictive performance. Further, Graph Neural Networks are an active area of research in machine learning, and the available resources, such as implemented layers in PyTorch, are continually improving. Leveraging these advancements can further enhance the performance and capabilities of our model, making it more effective in predicting the impacts of various policies.

In the long run, the goal is to provide a generalizable model capable of aiding urban planners and policymakers in designing effective traffic management strategies, which could be achieved for example by coupling the model to an optimization framework selecting the best policies to achieve a certain objective function. The GNN based model would allow fast evaluations of policies, enabling testing a magnitude of different policy scenarios. To achieve this goal, more policies beyond capacity reduction, e.g. road tolls or improvements in public transport, have to be included in the training data. Additionally, effects on other modes of transport have to be incorporated, for example by adding a public transport layer to the model.

7 Acknowledgments

We thank the German Federal Ministry of Transport and Digital Infrastructure for providing funding through the project “MINGA” with grant number 45AOV1001K. We remain responsible for all findings and opinions presented in the paper.

We thank Alejandro Tejada Lapuerta from Helmholtz Munich for fruitful discussions about setting up the model and interpreting the results. Further, we thank Dominik Fuchsgruber from the TUM School of Computation, Information and Technology for helpful discussions on finetuning the Graph Neural Network.

Chat-GBT 4.0 was used for summarizing paragraphs.

8 Author contributions

The authors confirm their contributions to the paper as follows: E. Natterer, R. Engelhardt, S. Hörl, and K. Bogenberger conceived and designed the study. S. Hörl provided the MATSim simulation for Paris. E. Natterer executed the MATSim simulations and developed the model. E. Natterer and R. Engelhardt analyzed and interpreted the results. All authors reviewed the results and approved the final version of the manuscript.

• un [2018] 68% of the world population projected to live in urban areas by 2050, says UN . https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html, 2018.
• Buchholz [2021] Buchholz, K., This is how global car use could change between now and 2050 . https://www.weforum.org/agenda/2021/11/where-car-use-is-rising-falling/, 11/2021.
• Sadler Consultants Europe GmbH [2024] Sadler Consultants Europe GmbH, Urban Access Regulations in Europe . https://urbanaccessregulations.eu/, 2024.
• MTA [2024] MTA, Congestion Pricing Program in New York - MTA . https://congestionreliefzone.mta.info/, 2024.
• Natterer et al. [2024] Natterer, E., A. Loder, and K. Bogenberger, Effects of the Plan Vélo I and II on vehicular flow in Paris - An Empirical Analysis. Arbeitsberichte Mobility Policy , Vol. 1, 2024.
• European Union [2023] European Union, Air pollution: MEPs want stricter limits to achieve zero pollution by 2050 | News | European Parliament . https://www.europarl.europa.eu/news/en/press-room/20230911IPR04915/air-pollution-meps-want-stricter-limits-to-achieve-zero-pollution-by-2050, 2023.
• Horni et al. [2016] Horni, A., K. Nagel, and K. W. Axhausen (eds.) The Multi-Agent Transport Simulation MATSim . Ubiquity Press, 2016.
• Auld et al. [2016] Auld, J., M. Hope, H. Ley, V. Sokolov, B. Xu, and K. Zhang, POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations. Transportation Research Part C: Emerging Technologies , Vol. 64, 2016, pp. 101–116.
• Yang et al. [2015] Yang, L., M. Adnan, K. Basak, F. Pereira, C. C., S. V.H., L. H., and M. Ben-Akiva, SimMobility Mid-Term Simulator: A State of the Art Integrated Agent Based Demand and Supply Model, 2015.
• Mallig et al. [2013] Mallig, N., M. Kagerbauer, and P. Vortisch, mobiTopp – A Modular Agent-based Travel Demand Modelling Framework. Procedia Computer Science , Vol. 19, 2013, pp. 854–859.
• Balac et al. [2017] Balac, M., F. Ciari, and K. W. Axhausen, Modeling the impact of parking price policy on free-floating carsharing: Case study for Zurich, Switzerland. Transportation Research Part C: Emerging Technologies , Vol. 77, 2017, pp. 207–225.
• Yin et al. [2024] Yin, B., A. O. Diallo, T. Seregina, N. Coulombel, and L. Liu, Evaluation of Low-Traffic Neighborhoods and Scale Effects: The Paris Case Study. Transportation Research Record , Vol. 2678, No. 1, 2024, pp. 88–101, publisher: SAGE Publications Inc.
• Straub et al. [2023] Straub, M., M. Stubenschrott, and A. Graser, Simulation of a full-scale implementation of Superblocks in Vienna: 15th ITS European Congress. Proceedings of the 15th ITS European Congress , 2023.
• Umweltbundesamt [2020] Umweltbundesamt, Umgestaltungen in Barcelona – Pionier der Superblocks , 2020.
• He et al. [2021] He, B. Y., J. Zhou, Z. Ma, D. Wang, D. Sha, M. Lee, J. Y. J. Chow, and K. Ozbay, A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City. Transport Policy , Vol. 101, 2021, pp. 145–161.
• Bracher and Bogenberger [2017] Bracher, B. and K. Bogenberger, A dynamic prizing scheme for a congestion charging zone based on a network fundamental diagram. In 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) , IEEE, 2017, pp. 669–674.
• Boyce [1984] Boyce, D. E., Urban Transportation Network-Equilibrium and Design Models: Recent Achievements and Future Prospects. Environment and Planning A: Economy and Space , Vol. 16, No. 11, 1984, pp. 1445–1474.
• Magnanti and Wong [1984] Magnanti, T. L. and R. T. Wong, Network Design and Transportation Planning: Models and Algorithms. Transportation Science , Vol. 18, No. 1, 1984, pp. 1–55.
• Schéele [1980] Schéele, S., A supply model for public transit services. Transportation Research Part B: Methodological , Vol. 14, No. 1-2, 1980, pp. 133–146.
• Ibarra-Rojas et al. [2015] Ibarra-Rojas, O. J., F. Delgado, R. Giesen, and J. C. Muñoz, Planning, operation, and control of bus transport systems: A literature review. Transportation Research Part B: Methodological , Vol. 77, 2015, pp. 38–75.
• Ng et al. [2024] Ng, M. T., H. S. Mahmassani, Ö. Verbas, T. Cokyasar, and R. Engelhardt, Redesigning large-scale multimodal transit networks with shared autonomous mobility services. Transportation Research Part C: Emerging Technologies , 2024, p. 104575.
• Zheng and Geroliminis [2013] Zheng, N. and N. Geroliminis, On the Distribution of Urban Road Space for Multimodal Congested Networks. Procedia - Social and Behavioral Sciences , Vol. 80, 2013, pp. 119–138.
• Tirachini et al. [2014] Tirachini, A., D. A. Hensher, and J. M. Rose, Multimodal pricing and optimal design of urban public transport: The interplay between traffic congestion and bus crowding. Transportation Research Part B: Methodological , Vol. 61, 2014, pp. 33–54.
• Loder et al. [2022] Loder, A., M. C. Bliemer, and K. W. Axhausen, Optimal pricing and investment in a multi-modal city — Introducing a macroscopic network design problem based on the MFD. Transportation Research Part A: Policy and Practice , Vol. 156, 2022, pp. 113–132.
• Huo et al. [2023] Huo, J., Z. Liu, J. Chen, Q. Cheng, and Q. Meng, Bayesian optimization for congestion pricing problems: A general framework and its instability. Transportation Research Part B: Methodological , Vol. 169, 2023, pp. 1–28.
• Dandl et al. [2021] Dandl, F., R. Engelhardt, M. Hyland, G. Tilg, K. Bogenberger, and H. S. Mahmassani, Regulating mobility-on-demand services: Tri-level model and Bayesian optimization solution approach. Transportation Research Part C: Emerging Technologies , Vol. 125, 2021, p. 103075.
• Shulajkovska et al. [2023] Shulajkovska, M., M. Smerkol, E. Dovgan, and M. Gams, A machine-learning approach to a mobility policy proposal . https://linkinghub.elsevier.com/retrieve/pii/S2405844023076016, 2023.
• Jiang et al. [2023] Jiang, W., J. Luo, M. He, and W. Gu, Graph Neural Network for Traffic Forecasting: The Research Progress . https://www.mdpi.com/2220-9964/12/3/100, 2023.
• Li and Axhausen [2020] Li, A. and K. W. Axhausen, Short-term Traffic Demand Prediction using Graph Convolutional Neural Networks . https://agile-giss.copernicus.org/articles/1/12/2020/agile-giss-1-12-2020.pdf, 2020.
• Liu et al. [2023] Liu, X., Y. Liang, C. Huang, H. Hu, C. Yushi, B. Hooi, and R. Zimmermann, Do We Really Need Graph Neural Networks for Traffic Forecasting? https://arxiv.org/pdf/2301.12603, 2023.
• Hörl and Balac [2021a] Hörl, S. and M. Balac, Introducing the eqasim pipeline: From raw data to agent-based transport simulation. Procedia Computer Science , Vol. 184, 2021a, pp. 712–719.
• Ziemke et al. [2019] Ziemke, D., I. Kaddoura, and K. Nagel, The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science , Vol. 151, 2019, pp. 870–877.
• Hörl and Balac [2021b] Hörl, S. and M. Balac, Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data. Transportation Research Part C: Emerging Technologies , Vol. 130, 2021b, p. 103291.
• Hörl [2023] Hörl, S., Towards replicable mode choice models for transport simulations in France. In 9th International Symposium on Transportation Data & Modelling (ISTDM2023) , Joint European Research Center, Ispra, Italy, 2023.
• Hörl et al. [2019] Hörl, S., M. Balac, and K. W. Axhausen, Dynamic demand estimation for an AMoD system in Paris. In 2019 IEEE Intelligent Vehicles Symposium (IV) , IEEE, Paris, France, 2019, pp. 260–266.
• Qi et al. [2017a] Qi, C. R., H. Su, K. Mo, and L. J. Guibas, PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation . https://arxiv.org/abs/1612.00593, 2017a.
• Qi et al. [2017b] Qi, C. R., H. Su, K. Mo, and L. J. Guibas, PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space . https://arxiv.org/abs/1706.02413, 2017b.
• Veličković et al. [2017] Veličković, P., G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, Graph Attention Networks . https://arxiv.org/abs/1710.10903, 2017.
• Fukushima [1969] Fukushima, K., Visual feature extraction by a multilayered network of analog threshold elements. In IEEE Transactions on Systems Science and Cybernetics. 5 (4): 322–333 , 1969.
• Glorot and Bengio [2010] Glorot, X. and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics , JMLR Workshop and Conference Proceedings, 2010, pp. 249–256.
• He et al. [2015] He, K., X. Zhang, S. Ren, and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision , 2015, pp. 1026–1034.
• Loshchilov and Hutter [2017] Loshchilov, I. and F. Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts . https://arxiv.org/abs/1608.03983, 2017.
• Loshchilov and Hutter [2019] Loshchilov, I. and F. Hutter, Decoupled Weight Decay Regularization . https://arxiv.org/abs/1711.05101, 2019.
• Natterer, Elena and Loder, Allister and Bogenberger, Klaus [2024] Natterer, Elena and Loder, Allister and Bogenberger, Klaus, Traffic Reduction and Decarbonization through Network Changes - Empirical Evidence from Paris. In Presented at 96th Annual Meeting of the Transportation Research Board , 2024.

Organizing Your Social Sciences Research Paper: Writing a Case Study

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Multiple Book Review Essay
• Reviewing Collected Essays
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Bibliography

The term case study refers to both a method of analysis and a specific research design for examining a problem, both of which are used in most circumstances to generalize across populations. This tab focuses on the latter--how to design and organize a research paper in the social sciences that analyzes a specific case.

A case study research paper examines a person, place, event, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or among more than two subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies . Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in this writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a single case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• Does the case represent an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• Does the case provide important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• Does the case challenge and offer a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in practice. A case may offer you an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to the study a case in order to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• Does the case provide an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings in order to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• Does the case offer a new direction in future research? A case study can be used as a tool for exploratory research that points to a need for further examination of the research problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of Uganda. A case study of how women contribute to saving water in a particular village can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community throughout rural regions of east Africa. The case could also point to the need for scholars to apply feminist theories of work and family to the issue of water conservation.

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work. In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What was I studying? Describe the research problem and describe the subject of analysis you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why was this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the research problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would include summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to study the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in the context of explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular subject of analysis to study and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that frames your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; c) what were the consequences of the event.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experience he or she has had that provides an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of his/her experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using him or her as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem.

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, cultural, economic, political, etc.], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, why study Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research reveals Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut? How might knowing the suppliers of these trucks from overseas reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should be linked to the findings from the literature review. Be sure to cite any prior studies that helped you determine that the case you chose was appropriate for investigating the research problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is more common to combine a description of the findings with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings It is important to remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations for the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and needs for further research.

The function of your paper's conclusion is to: 1)  restate the main argument supported by the findings from the analysis of your case; 2) clearly state the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place for you to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in and your professor's preferences, the concluding paragraph may contain your final reflections on the evidence presented applied to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were on social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood differently than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis.

Case Studies . Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent knowledge is more valuable than concrete, practical (context-dependent) knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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• URL: https://libguides.pointloma.edu/ResearchPaper

The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models

• Open access
• Published: 12 August 2024
• Volume 150 , article number  389 , ( 2024 )

Cite this article

You have full access to this open access article

• Olav Toai Duc Nguyen 1 , 2 ,
• Ioannis Fotopoulos 3 ,
• Therese Haugdahl Nøst 4 , 5 ,
• Maria Markaki 6 ,
• Vincenzo Lagani 7 , 8 , 9 ,
• Ioannis Tsamardinos 3 , 6 , 10 &
• Oluf Dimitri Røe 1 , 2 , 11  

1 Altmetric

The HUNT Lung Cancer Model (HUNT LCM) predicts individualized 6-year lung cancer (LC) risk among individuals who ever smoked cigarettes with high precision based on eight clinical variables. Can the performance be improved by adding genetic information?

A polygenic model was developed in the prospective Norwegian HUNT2 study with clinical and genotype data of individuals who ever smoked cigarettes ( n  = 30749, median follow up 15.26 years) where 160 LC were diagnosed within six years. It included the variables of the original HUNT LCM plus 22 single nucleotide polymorphisms (SNPs) highly associated with LC. External validation was performed in the prospective Norwegian Tromsø Study ( n  = 2663).

The novel HUNT Lung-SNP model significantly improved risk ranking of individuals over the HUNT LCM in both HUNT2 ( p  < 0.001) and Tromsø ( p  < 0.05) cohorts. Furthermore, detection rate (number of participants selected to detect one LC case) was significantly better for the HUNT Lung-SNP vs. HUNT LCM in both cohorts (42 vs. 48, p  = 0.003 and 11 vs. 14, p  = 0.025, respectively) as well as versus the NLST, NELSON and 2021 USPSTF criteria. The area under the receiver operating characteristic curve (AUC) was higher for the HUNT Lung-SNP in both cohorts, but significant only in HUNT2 (AUC 0.875 vs. 0.844, p  < 0.001). However, the integrated discrimination improvement index (IDI) indicates a significant improvement of LC risk stratification by the HUNT Lung-SNP in both cohorts (IDI 0.019, p  < 0.001 (HUNT2) and 0.013, p  < 0.001 (Tromsø)).

The HUNT Lung-SNP model could have a clinical impact on LC screening and has the potential to replace the HUNT LCM as well as the NLST, NELSON and 2021 USPSTF criteria in a screening setting. However, the model should be further validated in other populations and evaluated in a prospective trial setting.

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Introduction

The NLST and NELSON studies showed that computer-tomography (CT) screening of individuals that smoke can reduce lung cancer (LC) mortality by 20–24% (Aberle et al. 2011 ; de Koning et al. 2020 ). Both studies used fixed age and smoking history criteria for screening selection. However, ¾ of people developing LC do not fulfill the NLST criteria (Pinsky and Berg 2012 ). To include more at-risk individuals, the US Preventive Strategy Task Force (USPSTF) introduced wider screening criteria in 2021 (Krist et al. 2021 ). There is no international consensus on how to best select individuals for LC screening.

Various LC clinical risk prediction models have been developed, validated, and shown performance over selection criteria used in NLST, NELSON and USPSTF (Markaki et al. 2018 Røe et al. 2019 ; Tammemägi et al. 2022 ). Several studies have tried to integrate genetic susceptibility markers to further improve their performance, but no such model has shown to be superior to clinical risk models (Chien et al. 2020 ; Hoggart et al. 2012 ; Hung et al. 2021 ; Li et al. 2012 ; Marcus et al. 2016 ; Qian et al. 2016 ; Raji et al. 2010 ; Spitz et al. 2013 ; Weissfeld et al. 2015 ; Young et al. 2009 ).

In previous work, we developed and validated the HUNT Lung Cancer Model (HUNT LCM) to predict the LC risk in individuals that ever smoked with a concordance index of 0.879 and area under the receiver operating characteristic curve (AUC) of 0.87 for a 6-year LC diagnosis (Markaki et al. 2018 ). It was shown to have a superior performance compared to the NLST (Markaki et al. 2018 ), NELSON and 2021 USPSTF criteria (Nguyen et al. 2024 ).

Genome-wide association studies (GWAS) have identified specific LC susceptibility regions (McKay et al. 2008 , 2017 ). However, Single Nucleotide Polymorphisms (SNPs) alone are not predictive enough to warrant their use to identify high-risk individuals (Li et al. 2012 ; Qian et al. 2016 ). Nevertheless, SNPs carry some predictive information that could potentially increase risk prediction (Dai et al. 2019 ; McKay et al. 2017 ).

In this work, we develop and validate a new polygenic model for LC risk prediction integrating selected SNPs with the original eight clinical variables of the HUNT LCM. The performance of the new model, named HUNT Lung-SNP, is compared against the HUNT LCM, as well as the NLST, NELSON and 2021 USPSTF criteria.

Discovery and validation datasets

The discovery cohort was extracted from the HUNT2 study, a Norwegian prospective population study, which includes data from questionnaires, interviews, clinical measurements, and a serum biobank for all involved individuals. The HUNT2 enrolled and examined 65,240 people aged > 20 years in 1995-97 and followed up until 31.12.2011 (Krokstad et al. 2013 ). Genotyping information was available for 56,553 individuals, and these constitute the discovery dataset (Brumpton et al. 2022 ). The remaining individuals were unsuccessfully genotyped due to low blood sample quality. Missing clinical values are present in the data, with the highest percentage of missingness being in the variable “Indoor smoke exposure in hours” (17.8%, see Table  1 ). Missing clinical values were imputed with the median value for numerical variables or the mode for categorical variables.

The validation dataset comes from a similar population-based prospective study, the Tromsø Study (see Supplementary) (Jacobsen et al. 2012 ). Genotyping information was available for 6572 individuals in the Tromsø study.

The DNA from the HUNT2 samples was genotyped using one of three different Illumina Human Core Exome arrays (see Supplementary). All missing values in the SNPs have been imputed. The imputation and quality control of the datasets is described in detail in the Supplementary Material. The LC associated SNPs were selected manually from the HUNT Fast-track catalogue (HUNT Fast Track GWAS catalogue) where all SNPs were associated with LC at the genome-wide significance threshold in published literature ( p  < 5 × 10 − 8 , Supplementary Table 1 ) by the time this study was conducted in 2018. The Tromsø cohort samples were genotyped and imputed using the same methods as described for the HUNT2 samples and the same SNPs were available in both cohorts.

Definition of the clinical outcome

The national 11-digit personal identification number of each participant was linked to the Norwegian Cancer and Death Cause Registry. The diagnosis code of the International Classification of Diseases (ICD7) 162.1 and (ICD10) C33-34, was used to identify participants that were subsequently diagnosed with LC. Controls with a diagnosis of LC before the follow-up period were excluded. Follow up information for both the HUNT2 and Tromsø studies was obtained from the national Cancer Registry, which is updated each year. Clinical outcome was defined as “diagnosis of LC within six years” in both cohorts. Participants that develop LC within this timespan from inclusion were considered LC cases, all others were considered as controls. All cancers were clinically detected and not screen detected, and thus rarely indolent. In the survival analysis, participants that died or left the study before the six-year mark were censored. Individuals that died after LC diagnosis were considered LC associated deaths.

Univariate analysis

The univariate association between LC and each of the original eight HUNT LCM clinical variables (sex, age, body-mass index (BMI), pack-years, number of cigarettes per day, quit time in years, hours of daily indoors smoke exposure and history of daily cough in periods through the year) was assessed through unpaired t-test (numerical variables) or chi-square test (categorical variables). The SNP genotypes were transformed into ordinal encodings as described in the literature (He et al. 2015 ) (see Supplementary). The association between LC and each of the 22 SNPs was evaluated through a proportional odds likelihood ratio test (Coles 2001 ).

Multivariable modeling

The model for assessing LC risk was fit using the original eight HUNT LCM clinical covariates along with the 22 SNP genotype predictors. The SNP genotypes were transformed into ordinal encodings as described in the Supplementary. The outcome was defined as mentioned above, “diagnosis of LC within six years.” To establish the final model, we use a shrinkage methodology (Steyerberg et al. 2001 ), which relies on refitting the logistic regression model 100 times, each time over resampled data. Through this bootstrapping process we estimate to what extent the coefficients of the original logistic models should be shrunk. This methodology has shown to decrease the probability of overfitting (Steyerberg et al. 2001 ), and is described in more detail in the Supplementary Material.

Model validation

The validation of the HUNT Lung-SNP model was performed as shown in Fig.  1 .

Sample-level risk scores provided by the HUNT Lung-SNP were contrasted against the predictions provided by the original HUNT LCM (algorithm in Supplementary Appendix page 5 in Markaki et al. ( 2018 )), both on the HUNT2 and Tromsø cohort. The AUC, integrated discrimination improvement index (IDI), detection rate (number of individuals needed to screen, NNS, to detect/predict one LC case) and ranking of risk were used as performance metrics. Statistical significance of the differences was assessed through non-parametric statistical tests (see Supplementary) (DeLong et al. 1988 ; Kang et al. 2015 ). Calibration, agreement between predicted and observed LC cases in the cohorts, was evaluated by predictiveness curve (Markaki et al. 2018 ).

Model validation. Model validation of the HUNT Lung-SNP model against the HUNT Lancer Model (HUNT LCM) and the criteria 2021 USPSTF, NELSON and NLST on the datasets of HUNT2 and Tromsø study. *For a fair comparison, a risk threshold selecting the same number to screen as the USPSTF 2021, NELSON and NLST criteria as a benchmark was used. AUC, area under the receiver operating characteristic curve; IDI, integrated discrimination improvement index; NPV, negative predictive value; PPV, positive predictive value; NNS, number needed to screen to identify one case of lung cancer

To stratify individuals in high- and low-risk categories according to the HUNT Lung-SNP and HUNT LCM risk scores, a cut-off for each model was derived corresponding to the top 16th percentile of their respective in-sample predictions. The cutpoint of top 16th percentile was chosen according to recommendations from Royston et al. (Markaki et al. 2018 ; Royston and Altman 2013 ). The two models were then compared both on the HUNT2 and Tromsø cohort according to sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The statistical significance for differences in these metrics was assessed through a permutation-based test (see Supplementary). Furthermore, to analyze whether the two models perform differently depending on the age of the population, a comparison was performed on the subpopulations < 60 and ≥ 60 years of age on the HUNT2 and Tromsø cohort.

For both models we used the Kolmogorov-Smirnov test to contrast participants’ risk ranking versus the cumulative number of LC diagnosed.

We further contrasted both the HUNT LCM and HUNT Lung-SNP against the NLST, NELSON and 2021 USPSTF criteria sets for LC diagnosis within six years. To be able to compare risk assessment models, a risk threshold resulting in the same number of participants to screen was set. Given this threshold, we present several metrics of predictive performance. The statistical significance of differences is assessed using a permutation-based, non-parametric approach (see Supplementary).

Finally, the overall survival between the sub-cohorts selected by the HUNT Lung-SNP, NELSON and 2021 USPSTF criteria was investigated. The survival was calculated as survival from LC diagnosis in the cases, and from the time of inclusion to time of death of the LC high-risk versus low-risk individuals. Kaplan-Meier curves were used to visualize survival functions, while the log-rank test was used to evaluate statistical significance in their differences. For all analyses p  < 0.05 was used as the statistical significance level. The R Statistical Software version 4.2.1 (2022-06-23) was used to perform the analyses.

Cost of implementing a SNP analysis

The scenario used in calculating additional cost was to apply the HUNT Lung-SNP model on all participants who ever smoked in the HUNT cohort. We calculated the cost of the SNP-analysis in terms of cost per quality-adjusted life year (QALY), to assess whether incorporating genetic variants in a LC prediction model is cost-effective. This analysis was performed using estimates of (a) the current administrative costs related to blood drawing, (b) the cost of the genetic analysis of the 22 SNPs, (c) the average years life lost (YLL) per LC case, and (d) the health-related quality of life (HRQL) score for LC.

Role of the funding sources

The funding sources had no role in study conception, design, data interpretation, writing of the report, or decision to submit the paper for publication.

SNPs characteristics

Among the 22 SNPs selected, one SNP was associated with small-cell LC, four with squamous cell carcinoma, six with lung adenocarcinoma and 13 with lung carcinoma (Supplementary Table 1 ). Furthermore, all 22 SNPs have been found significant in one or more major ethnic groups, including Latin American, African American, Caucasian, and Asian (one, three, 12 and 16 SNPs, respectively) (Supplementary Table 1 , Supplementary Fig.  1 ).

Discovery (HUNT2) and validation (Tromsø) cohorts

The discovery cohort comprised 30,749 genotyped individuals that ever smoked with near complete data on the HUNT LCM clinical variables (Table  1 ). Among these a total of 2366 was censored. After six years of follow-up, 160 had been diagnosed with LC. In univariate analysis, all of the eight clinical variables and six of the 22 SNPs were significantly associated with LC occurrence within six years (Table  1 , Supplementary Table 1 ). Most of the included participants had all HUNT LCM clinical variables measured at enrollment: sex, age, BMI, pack-years, number of cigarettes per day, quit time, hours of daily indoors smoke exposure and cough in periods through the year (Table  1 ).

Among the 6572 individuals genotyped in the Tromsø study, five never smoked, 1197 lacked smoking information, while 2707 lacked one or more of the HUNT LCM variables, leading to the inclusion of 2663 individuals who ever smoked with complete data. Among these, two were censored and 39 were diagnosed with LC within the six-year follow-up. Three of the eight clinical variables and three SNPs were significantly associated with LC in univariate analysis (Table  1 , Supplementary Table 2 ).

22 SNPs model

A logistic regression model based on the 22 SNPs alone showed a predictive power with an AUC of 0.625 (95% CI 0.583–0.666) in the HUNT2 population, discovery dataset (Supplementary Table 3 ).

Contrasting HUNT Lung-SNP and HUNT LCM

In the HUNT2 cohort, the HUNT Lung-SNP outperformed the HUNT LCM in terms of ranking HUNT2 participants with respect to their risk of developing LC within six-years: AUC 0.875 (95% confidence interval (CI) 0.854–0.896) vs. 0.844 (95% CI 0.820–0.869), p  < 0.001. In the validation cohort, the HUNT Lung-SNP also performed better than the HUNT LCM, albeit not statistically significant, AUC 0.916 (95% CI 0.880–0.948) vs. 0.876 (95% CI 0.823–0.921), p  = 0.086 (Table  2 ). Furthermore, the IDI between the two models indicates that the HUNT Lung-SNP significantly improve the LC risk stratification compared to the original HUNT LCM with an IDI of 0.019 (95% CI 0.015–0.025), p  < 0.001 and of 0.013 (95% CI 0.008–0.018), p  < 0.001 in the HUNT2 and Tromsø cohorts, respectively. Calibration was adequate for both models with predicted risk close to observed risk in both cohorts (Supplementary Fig.  2 ).

Ranking of individuals according to risk score (potential screenees) versus the cumulative number of LC diagnosed for the two models, showed that the HUNT Lung-SNP’s performance improved significantly compared to the HUNT LCM in both the HUNT2 ( p  < 0.001) and Tromsø cohort ( p  < 0.05) (Fig.  2 ).

Comparison of risk ranking between the HUNT LCM and HUNT Lung-SNP model. Comparison of ranking of individuals that ever smoked by risk score in the prospective population-based HUNT2 and Tromsø studies applying the HUNT LCM and HUNT Lung-SNP model and their capacity to identify individuals that will develop lung cancer within six years. Individuals are ranked from highest to lowest risk according to the respective model from left to right (x-axis). The cumulative number of diagnosed lung cancer is reported on the y-axis. (A) In the HUNT2 population there are n  = 30,749 individuals that ever smoked and n  = 160 lung cancers diagnosed in six years. (B) In the Tromsø population there are n  = 2663 individuals that ever smoked and n  = 39 lung cancers diagnosed in six years. Comparison of distributions by the Kolmogorov-Smirnov test, p  < 0.05 for both cohorts

When individuals were stratified as having high or low risk according to the top 16th or bottom 84th percentile risk score, respectively, the HUNT Lung-SNP model showed increased performance across all metrics on both cohorts (Supplementary Table 4 ). In the HUNT2 cohort there was a significant gain in sensitivity (73.75% vs. 64.38%, p  = 0.001), PPV (2.40% vs. 2.09%, p  = 0.004) and NPV (99.84% vs. 99.78%, p  < 0.001) while specificity was higher, albeit not significant. In the Tromsø cohort, sensitivity (76.92% vs. 61.54%, p  = 0.15) and specificity (88.76% vs. 87.92%) differences were numerically even larger, albeit not statistically significant. The PPV (6.91% vs. 5.69%, p  = 0.026) and NPV (99.62% vs. 99.35%, p  = 0.041) were significantly different in the Tromsø cohort. Furthermore, the detection or prediction rate, defined as the number of individuals needed to screen (NNS) to detect one LC case on average, was significantly lower for the HUNT Lung-SNP compared to HUNT LCM (Fig.  3 ), both in the HUNT2 (NNS of 42 vs. 48, p  = 0.003) and Tromsø cohort (NNS of 11 vs. 14, p  = 0.025).

Number needed to screen (NNS). NNS to identify one case of lung cancer in the HUNT2 and Tromsø population. NNS computed when the threshold is set at the top 16th percentile of risk score. * p  < 0.05. ** p  < 0.01

When the HUNT2 cohort was split by age into subpopulations of < 60 ( n  = 21,762) and ≥ 60 ( n  = 8987) years of age, the HUNT Lung-SNP achieved the same sensitivity (45.95% vs. 45.95%, p  = 0.625) but a higher specificity (95.66% vs. 93.84%, p  < 0.001) in the younger population (< 60 years) compared to the HUNT LCM, and better detection rate (NNS of 56 vs. 80, p  < 0.001). For the older participants (≥ 60 years), the HUNT Lung-SNP achieved higher sensitivity (82.11% vs. 69.92%, p  < 0.001) but with a lower specificity (56.45% vs. 60.75%, p  < 0.001) than the HUNT LCM, and similar detection rate (NNS of 39 vs. 41, p  = 0.273) (Supplementary Table 5 ).

Contrasting the HUNT Lung-SNP and HUNT LCM against the NLST, NELSON and USPSTF criteria

In the HUNT2 cohort, when selecting the same number of high-risk individuals as the NLST, NELSON and 2021 USPSTF criteria, the HUNT Lung-SNP outperformed all the three criteria in terms of number of detected LC and corresponding sensitivities ( p  < 0.01). Similar results were found with the HUNT LCM (Supplementary results, Supplementary Tables 6 , 7 , and 8 ).

In terms of NNS to identify one LC case, the HUNT Lung-SNP was the most well-performing model in the HUNT2 cohort, with NNS of 24 vs. 40 (NLST), 31 vs. 53 (NELSON) and 39 vs. 51 (USPSTF), p  < 0.01 for all comparisons. Similar findings were found with HUNT LCM (Supplementary Fig.  3 A-C).

By applying the top 16th percentile as a cutoff for risk stratification the HUNT Lung-SNP identified  ≈  280%, 168% and 50% more cases in the HUNT2 individuals that ever smoked in six years compared to the NLST, NELSON and USPSTF criteria, respectively (Supplementary Fig.  4 ).

Similar significant results were found in the Tromsø cohort for both the HUNT Lung-SNP and HUNT LCM, except for the HUNT Lung-SNP under the application of NLST criteria, where the increased number of detected LC and the corresponding sensitivity, as well as the lower NNS did not reach statistical significance compared to the NLST criteria (Supplementary results).

Survival analysis

The survival analysis showed non-significant differences in median survival from diagnosis of participants that developed LC within six years predicted by the HUNT Lung-SNP compared to the NLST, NELSON and USPSTF criteria (Supplementary Fig.  5 ).

Cost of SNP analysis

The administrative costs related to blood drawing in our public hospital is estimated to $14 USD per blood test, while the cost of the genetic analysis of the SNP-panel including all the 22 SNPs can be estimated to $23 USD per analysis (Illumina 2023 ). Using the top 16th percentile, the HUNT Lung-SNP predicted 21 unique cases in six years (Supplementary Tables 9 and Supplementary Fig.  6 ) and the HUNT LCM predicted 6 unique cases, thus 15 surplus LC cases were predicted by the SNP model. Based on previous publications (Brustugun et al. 2014 ; Burnet et al. 2005 ), an average of YLL per LC case is estimated to be 15 years, given survival from LC. However, it is unlikely that all LC cases will survive from LC. Based on a 5-year relative survival rate of 68% on LC stage I (Norway 2023 ), an average YLL per LC case of 10 years was applied. A health-related quality of life (HRQL) score of 0.75 (Behar Harpaz et al. 2023 ) (meaning that 3/4 of the time saved represents life in full health) was applied.

Based on the price estimates, the YLL and the HRQL score, the additional cost will be $1,137,713 USD if the SNP-analysis is performed on all individuals that ever smoked in the HUNT cohort ( n  = 30,749). By factoring in all data, the cost of the SNP-analysis will be $10,113 USD/QALY.

This study shows that the HUNT Lung-SNP performs significantly better in ranking individuals by risk and decreases the number needed to screen compared to the HUNT LCM, NLST, NELSON and USPSTF criteria. To our knowledge, the HUNT Lung-SNP is the first risk model where adding genetic information improves LC risk assessment for high-risk individuals over a validated risk model and over several clinical criteria.

HUNT Lung-SNP against HUNT LCM

Risk ranking is essential for defining the performance of a risk model. Here we found a significant improvement of risk ranking in the polygenic risk model over the clinical model, both in the discovery and validation cohort (Fig.  2 ). This translated into a lower NNS, an important metric for evaluating the effectiveness of potential screening. Specifically, we computed the average number of screenings to detect one LC (NNS) in the ranked list of risk according to each model. The NNS is significantly better in the HUNT Lung-SNP versus HUNT LCM and all the clinical screening criteria tested. This result indicates that the HUNT Lung-SNP could have a clinical impact in LC screening and replace the HUNT LCM and be an alternative to the clinical criteria in screening settings.

Polygenic risk score based on LC associated SNPs seems to have an independent risk stratification beyond age and smoking history (Dai et al. 2019 ). However, no LC risk model, based only on genetic information, has shown sufficient performance for clinical use, let alone for screening purposes (Li et al. 2012 ; Young et al. 2009 ). This is consistent with our findings where the 22 SNPs model alone had a modest AUC of 0.625 (95% CI 0.583–0.666). Several groups have added LC-associated genetic variants identified in GWAS to LC risk assessment models attempting to improve the models, but so far with disappointing results (Hung et al. 2021 ; Li et al. 2012 ; Qian et al. 2016 ; Young et al. 2009 ). Most of these studies were without external validation, all were case-control studies except for one prospective-based study (Supplementary Table 10 ). Although adding genetic information to risk models has shown limited impact on a risk model’s risk assessment so far, Hung et al. did observed that genetic information could be informative regarding assessing the individual’s age for reaching the low dose CT screening-eligible threshold (Hung et al. 2021 ). However, the study from Hung et al. lacked external validation, only performed a split-sample validation and validation in an external cohort without genetic information on each subject (Hung et al. 2021 ). To our knowledge, the present study is the first externally validated, prospective cohort study showing that genetic information can significantly improve LC risk assessment compared to a validated risk model in terms of risk ranking and detection rate.

HUNT Lung-SNP and HUNT LCM against the NLST, NELSON and USPSTF criteria

The HUNT Lung-SNP outperformed the 2021 USPSTF and NELSON criteria in both HUNT2 and Tromsø cohort when selecting the same number as the two criteria, respectively. When selecting the same number as the NLST criteria the HUNT Lung-SNP performs significantly better in the HUNT2 cohort, but not in the validation cohort (see Supplementary). This could be due to the low number of participants in the Tromsø cohort combined with the strict criteria of the NLST compared to the USPSTF and NELSON, resulting in a lower number of individuals selected by the NLST ( n  = 101) in the validation cohort (see Supplementary). The HUNT LCM showed similar results in both cohorts, but with less numerically detected LC and higher NNS compared to HUNT Lung-SNPs, except when selecting the same number as the NLST criteria (see Supplementary).

HUNT Lung-SNP model in subgroups

Younger individuals that smoke and individuals with low number of pack-years are not eligible for LC screening according to current guidelines. It is known that certain genetic predispositions have been associated with increased risk of early onset (< 51 years) LC independently of heavy smoking (Timofeeva et al. 2010 ). In line with this, Hung et al. reported that genetic information contributed to their risk model in those with younger age onset (< 51 years), albeit the AUC increased only moderately in their study with genetic information compared without (Hung et al. 2021 ). Our analysis of the HUNT2 cohort supports this, showing a significantly lower number of screenings needed per cancer detected (NNS of 56 vs. 80) for the HUNT Lung-SNP versus HUNT LCM in the younger participants (< 60 years) (Supplementary Table 5 ). This needs further validation since only two cases were below 60 years of age when included in the Tromsø cohort. Moreover, most of the patients predicted by the HUNT Lung-SNP but missed by the HUNT LCM, had very low number of smoking pack-years, as low as two pack-years but still reached a high risk score (Supplementary Table 9 ). This indicates an important role of incorporating SNPs for prediction in groups where the clinical risk model is not effective.

Discrimination power between the HUNT Lung-SNP and HUNT LCM

The numerical AUC differences between the HUNT Lung-SNP and HUNT LCM are arguably small, and in the validation cohort they do not reach statistical significance. However, this is probably because the AUC is computed as averages over all individuals, including a large portion of the population with very low LC risk. Furthermore, concerns have been raised on AUC ability to capture the incremental value of new markers in risk prediction in a clinical meaningful way (Kerr et al. 2011 ). The IDI has been proposed as a complementary to AUC in measuring the discrimination improvement (Kerr et al. 2011 ). The IDI between the HUNT Lung-SNP and HUNT LCM indicates a significant improvement of LC risk stratification by the HUNT Lung-SNP. Furthermore, when we examined the behavior of the models in the high-risk populations (e.g. top 16th percentile risk score), the differences of the models were more apparent.

Cost and feasibility

The approximate analysis of cost of SNP testing and cost-effectiveness was performed and showed that adding genetic test in a LC model requires some more resources than the clinical model, but still within what is both feasible and cost-effective. We found that the cost of the SNP-analysis per QALY could be $10,113 USD/QALY, which is far below the cost per QALY threshold set by many high income countries, e.g. NICE for England and Wales has set the cost per QALY threshold between £20,000 and £30,000 (=$25,000–38,000 USD) (Office for Health Improvement and Disparities 2020), the United States has set it at $50,000-100,000 USD (Ubel et al. 2003 ), and Norway 275,000-825,000 NOK (=$25,000–77,000 USD) (Magnussen 2015 ; Norheim et al. 2014 ; Ottersen et al. 2016 ). It should be noted that we expect that the cost of genetic tests to drop in the future (Wetterstrand 2021 ), becoming even more accessible. We emphasize that this is a simple cost-effectiveness calculation and that a comprehensive analysis using more detailed assumptions will be the focus of future research.

Finally, one can envision methods for optimizing the selection of patients for SNP analysis, e.g., using the clinical and SNP model successively. We plan to explore these approaches in future studies.

Strengths and limitations

There are several strengths to this study: (1) The prospective study design of both cohorts. (2) The sample size of the HUNT2 cohort, the long follow-up time, and high-quality clinical data of apparently healthy individuals in a population. The validation cohort was smaller, but compared to previous reported studies the variables and SNPs matched the qualities of the HUNT cohort closely. (3) The SNPs were analyzed in high-quality high-throughput platforms at centralized University facilities. (4) All cancers were clinically detected, and thus rarely indolent, in contrast to many screen-detected cancers (Esserman et al. 2014 ), where about 9% of screen-detected LC have been estimated to be indolent (de Koning et al. 2020 ). Results from our survival analysis supports that the HUNT Lung-SNP do identify individuals with high risk of non-indolent LC (Supplementary Fig.  5 ). (5) As far as we know, this is the first study where the SNPs in a risk model are associated with all the three main histological subgroups of LC: adenocarcinoma, squamous cell carcinoma and small-cell LC (Supplementary Table 1 ).

The HUNT Lung-SNP model, besides its predictive power, has also some apparent strengths over other models. (1) All the clinical variables in the HUNT LCM and SNP model are easily retrieved from the individuals’ memory and are not dependent on culture-specific or diagnosis-based factors as e.g. in PLCO m2012 (education, ethnicity, history of COPD or family history of LC) (Røe et al. 2019 ). However, we acknowledge that the two variables “symptoms of daily cough in periods of the year” and “hours of indoor smoke” are not as easily to answer accurately as the rest of the clinical variables in the model, and these two are often unavailable in databases from other countries. If neither of these variables are available, one may use the HUNT LCM omitting these two, or our previously published model, the “Reduced” HUNT model(Røe et al. 2019 ). (2) The relatively easy assessment of genetic information with three possible genotype combinations (homozygous for the reference, heterozygous or homozygous for the alternative allele) compared to other molecular components such as proteins or microRNAs. (3) Only one blood test is needed as SNPs do not change throughout life. (4) The SNPs included have been found significant in one or more major ethnic groups (Supplementary Table 1 , Supplementary Fig.  1 ), which can indicate validity in global populations, but could need recalibration for certain populations.

There are some limitations to be aware of: (1) Susceptibility polymorphisms identified in GWASs can vary in different ethnic populations. The HUNT Lung-SNP has only been externally validated in Scandinavian populations. (2) By the time this study was conducted in 2018, only 22 LC associated SNPs ( p  < 5 × 10 − 8 ) were available in the HUNT Fast-track catalogue (HUNT Fast Track GWAS catalogue), knowledge has evolved, and far more genome-wide significant ( p  < 5 × 10 − 8 ) LC associated SNPs have been identified since then (Long et al. 2022 ). (3) Our dataset is affected by class imbalance, with a proportion between the number of events and the number of variables (events per variable proportion, EPV) of three, quite below the recommended value EPV ≥ 10 (Steyerberg and Vergouwe 2014 ). The strategy of shrinking coefficients through bootstrapping was adopted during the training of the HUNT Lung-SNP to mitigate the issue of class imbalance while regulating the overestimation on the predictions (see Methods). (4) We recognize a potential bias issue in the validation cohort due to filtering out incomplete data, resulting in only 2663 out of 6572 individuals being included in the analysis of the Tromsø data.

Conclusions

In conclusion, our research demonstrates for the first time that a polygenic risk prediction model for LC combining clinical variables with SNP can significantly improve the performance of LC risk ranking and NNS over a validated clinical model, HUNT LCM, and over current clinical criteria. Thus, we believe that risk stratification using the HUNT Lung-SNP model followed by annual CT lung screening is feasible and would substantially reduce the over- and underdetection rate compared with the CT LC screening model based on the NLST, NELSON or 2021 USPSTF criteria. Our results support that the HUNT Lung-SNP model should be validated in populations of various ethnicities and subgroups (younger individuals that smoke and individuals with few pack-years), and tested prospectively in screening studies or programs.

Data availability

In agreement with the license agreements applicable to this study, only the named authors were given full access to the data during the study. This is to ensure that all personal and health information of the participants in the HUNT and Tromsø studies is kept confidential. Detailed information about accessing the HUNT and Tromsø studies are available on the website of the HUNT study ( https://www.ntnu.edu/hunt ) and Tromsø study ( https://uit.no/research/tromsostudy ).

Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM et al (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365:395–409. https://doi.org/10.1056/NEJMoa1102873

Article   PubMed   Google Scholar  

Behar Harpaz S, Weber MF, Wade S, Ngo PJ, Vaneckova P, Sarich PEA et al (2023) Updated cost-effectiveness analysis of lung cancer screening for Australia, capturing differences in the health economic impact of NELSON and NLST outcomes. Br J Cancer 128:91–101. https://doi.org/10.1038/s41416-022-02026-8

Brumpton BM, Graham S, Surakka I, Skogholt AH, Løset M, Fritsche LG et al (2022) The HUNT study: a population-based cohort for genetic research. Cell Genom 2:100193. https://doi.org/10.1016/j.xgen.2022.100193

Article   PubMed   PubMed Central   CAS   Google Scholar  

Brustugun OT, Møller B, Helland A (2014) Years of life lost as a measure of cancer burden on a national level. Br J Cancer 111:1014–1020. https://doi.org/10.1038/bjc.2014.364

Burnet NG, Jefferies SJ, Benson RJ, Hunt DP, Treasure FP (2005) Years of life lost (YLL) from cancer is an important measure of population burden–and should be considered when allocating research funds. Br J Cancer 92:241–245. https://doi.org/10.1038/sj.bjc.6602321

Chien LH, Chen CH, Chen TY, Chang GC, Tsai YH, Hsiao CF et al (2020) Predicting Lung Cancer Occurrence in Never-Smoking Females in Asia: TNSF-SQ, a Prediction Model. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. ;29:452-9. https://doi.org/10.1158/1055-9965.Epi-19-1221

Coles S (2001) An introduction to statistical modeling of extreme values. Springer-, London, U.K.

Book   Google Scholar  

Dai J, Lv J, Zhu M, Wang Y, Qin N, Ma H et al (2019) Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations. Lancet Respir Med 7:881–891. https://doi.org/10.1016/s2213-2600(19)30144-4

Article   PubMed   PubMed Central   Google Scholar  

de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA et al (2020) Reduced lung-Cancer mortality with volume CT screening in a Randomized Trial. N Engl J Med 382:503–513. https://doi.org/10.1056/NEJMoa1911793

DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

Article   PubMed   CAS   Google Scholar  

Esserman LJ, Thompson IM, Reid B, Nelson P, Ransohoff DF, Welch HG et al (2014) Addressing overdiagnosis and overtreatment in cancer: a prescription for change. Lancet Oncol 15:e234–e242. https://doi.org/10.1016/s1470-2045(13)70598-9

He D, Wang Z, Parida L (2015) Data-driven encoding for quantitative genetic trait prediction. BMC Bioinformatics 16:S10. https://doi.org/10.1186/1471-2105-16-S1-S10

Hoggart C, Brennan P, Tjonneland A, Vogel U, Overvad K, Østergaard JN et al (2012) A risk model for lung cancer incidence. Cancer Prev Res (Philadelphia Pa) 5:834–846. https://doi.org/10.1158/1940-6207.Capr-11-0237

Article   Google Scholar  

Hung RJ, Warkentin MT, Brhane Y, Chatterjee N, Christiani DC, Landi MT et al (2021) Assessing Lung Cancer Absolute Risk Trajectory based on a polygenic risk model. Cancer Res 81:1607–1615. https://doi.org/10.1158/0008-5472.Can-20-1237

Illumina, Cost of Next-Generation Sequencing (2023). Illumina. https://emea.illumina.com/science/technology/next-generation-sequencing/beginners/ngs-cost.html . Accessed 15 Sept 2023

Jacobsen BK, Eggen AE, Mathiesen EB, Wilsgaard T, Njølstad I (2012) Cohort profile: the Tromso Study. Int J Epidemiol 41:961–967. https://doi.org/10.1093/ije/dyr049

Kang L, Chen W, Petrick NA, Gallas BD (2015) Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med 34:685–703. https://doi.org/10.1002/sim.6370

Kerr KF, McClelland RL, Brown ER, Lumley T (2011) Evaluating the incremental value of new biomarkers with Integrated discrimination improvement. Am J Epidemiol 174:364–374. https://doi.org/10.1093/aje/kwr086

Krist AH, Davidson KW, Mangione CM, Barry MJ, Cabana M, Caughey AB et al (2021) Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA 325:962–970. https://doi.org/10.1001/jama.2021.1117

Krokstad S, Langhammer A, Hveem K, Holmen TL, Midthjell K, Stene TR et al (2013) Cohort Profile: the HUNT Study, Norway. Int J Epidemiol 42:968–977. https://doi.org/10.1093/ije/dys095

Li H, Yang L, Zhao X, Wang J, Qian J, Chen H et al (2012) Prediction of lung cancer risk in a Chinese population using a multifactorial genetic model. BMC Med Genet 13:118. https://doi.org/10.1186/1471-2350-13-118

Long E, Patel H, Byun J, Amos CI, Choi J (2022) Functional studies of lung cancer GWAS beyond association. Hum Mol Genet 31:R22–r36. https://doi.org/10.1093/hmg/ddac140

Magnussen JA, Morten; Granaas T, Magelssen M, Syse A, Celius EG, Klovning A (2015) Syversen, Iselin Dahlen. På ramme alvor Alvorlighet og prioritering. pp. 48

Marcus MW, Raji OY, Duffy SW, Young RP, Hopkins RJ, Field JK (2016) Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model. Int J Oncol 49:361–370. https://doi.org/10.3892/ijo.2016.3499

Markaki M, Tsamardinos I, Langhammer A, Lagani V, Hveem K, Roe OD (2018) A validated clinical risk prediction model for lung Cancer in smokers of all ages and exposure types: a HUNT study. EBioMedicine 31:36–46. https://doi.org/10.1016/j.ebiom.2018.03.027

McKay JD, Hung RJ, Gaborieau V, Boffetta P, Chabrier A, Byrnes G et al (2008) Lung cancer susceptibility locus at 5p15.33. Nat Genet 40:1404–1406. https://doi.org/10.1038/ng.254

McKay JD, Hung RJ, Han Y, Zong X, Carreras-Torres R, Christiani DC et al (2017) Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet 49:1126–1132. https://doi.org/10.1038/ng.3892

Nguyen OTD, Fotopoulos I, Markaki M, Tsamardinos I, Lagani V, Røe OD (2024) Improving lung cancer screening selection: the HUNT Lung Cancer Risk Model for ever-smokers versus the NELSON and 2021 USPSTF criteria in the cohort of Norway (CONOR), a Population-based prospective study. JTO Clinical and. 100660. https://doi.org/10.1016/j.jtocrr.2024.100660 . Research Reports

Norheim O, Allgott B, Gjul G, Kjellevold A, Moen A, Sjøli S et al (2014) NOU Åpent og rettferdig – prioriteringer i helsetjenesten. pp. 128

Norway, CRo (2023) Årsrapport 2022 med resultater og forbedringstiltak fra nasjonalt kvalitetsregister for lungekreft. Cancer Registry nor. https://www.kreftregisteret.no/globalassets/publikasjoner-og-rapporter/arsrapporter/publisert-2023/arsrapport-2022-nasjonalt-kvalitetsregister-for-lungekreft.pdf . Accessed 13 Aug 2023

HUNT Fast Track GWAS catalogue. NTNU/HUNT. https://www.ntnu.edu/hunt/data . Accessed 25 Oct 2023

Office for Health Improvement and Disparities (2020) Guidance cost utility analysis: health economic studies. Government of United Kingdom. https://www.gov.uk/guidance/cost-utility-analysis-health-economic-studies . Accessed 27 Aug 2023

Ottersen T, Førde R, Kakad M, Kjellevold A, Melberg HO, Moen A et al (2016) A new proposal for priority setting in Norway: open and fair. Health Policy 120:246–251. https://doi.org/10.1016/j.healthpol.2016.01.012

Pinsky PF, Berg CD (2012) Applying the National Lung Screening Trial eligibility criteria to the US population: what percent of the population and of incident lung cancers would be covered? J Med Screen 19:154–156. https://doi.org/10.1258/jms.2012.012010

Qian DC, Han Y, Byun J, Shin HR, Hung RJ, McLaughlin JR et al (2016) A novel pathway-based Approach improves Lung Cancer Risk Prediction using germline genetic variations. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 25:1208–1215. https://doi.org/10.1158/1055-9965.Epi-15-1318

Raji OY, Agbaje OF, Duffy SW, Cassidy A, Field JK (2010) Incorporation of a genetic factor into an epidemiologic model for prediction of individual risk of lung cancer: the Liverpool Lung Project. Cancer prevention research (Philadelphia. Pa) 3:664–669. https://doi.org/10.1158/1940-6207.Capr-09-0141

Røe OD, Markaki M, Tsamardinos I, Lagani V, Nguyen OTD, Pedersen JH et al (2019) Reduced’ HUNT model outperforms NLST and NELSON study criteria in predicting lung cancer in the Danish screening trial. BMJ Open Respir Res 6:e000512. https://doi.org/10.1136/bmjresp-2019-000512

Royston P, Altman DG (2013) External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol 13:33. https://doi.org/10.1186/1471-2288-13-33

Spitz MR, Amos CI, Land S, Wu X, Dong Q, Wenzlaff AS et al (2013) Role of selected genetic variants in lung cancer risk in African americans. J Thorac Oncol 8:391–397. https://doi.org/10.1097/JTO.0b013e318283da29

Steyerberg EW, Vergouwe Y (2014) Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 35:1925–1931. https://doi.org/10.1093/eurheartj/ehu207

Steyerberg EW, Eijkemans MJC, Habbema JDF (2001) Application of shrinkage techniques in logistic regression analysis: a Case Study. Stat Neerl 55:76–88. https://doi.org/10.1111/1467-9574.00157

Tammemägi MC, Ruparel M, Tremblay A, Myers R, Mayo J, Yee J et al (2022) USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study. Lancet Oncol 23:138–148. https://doi.org/10.1016/s1470-2045(21)00590-8

Timofeeva M, Kropp S, Sauter W, Beckmann L, Rosenberger A, Illig T et al (2010) Genetic polymorphisms of MPO, GSTT1, GSTM1, GSTP1, EPHX1 and NQO1 as risk factors of early-onset lung cancer. Int J Cancer 127:1547–1561. https://doi.org/10.1002/ijc.25175

Ubel PA, Hirth RA, Chernew ME, Fendrick AM (2003) What is the price of life and why doesn’t it increase at the rate of inflation? Arch Intern Med 163:1637–1641. https://doi.org/10.1001/archinte.163.14.1637

Weissfeld JL, Lin Y, Lin HM, Kurland BF, Wilson DO, Fuhrman CR et al (2015) Lung Cancer Risk Prediction using common SNPs located in GWAS-Identified susceptibility regions. J Thorac Oncol 10:1538–1545. https://doi.org/10.1097/jto.0000000000000666

Wetterstrand KA, National Human Genome Research Institute (2021) The Cost of Sequencing a Human Genome. https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost . Accessed 18 Sept 2023

Young RP, Hopkins RJ, Hay BA, Epton MJ, Mills GD, Black PN et al (2009) A gene-based risk score for lung cancer susceptibility in smokers and ex-smokers. Postgrad Med J 85:515–524. https://doi.org/10.1136/pgmj.2008.077107

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Acknowledgements

We are grateful to all participants in the HUNT and Tromsø studies. We thank Dr. Bendik Winsvold and Prof. John-Anker Zwart, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway for their contribution to the genotyping of the Tromsø Study samples. Thanks to Laurent Thomas, Anne Heidi Skogholt and Ola Løvsletten for the preparation of the data from the Tromsø Study. The genotyping was conducted at the Genomics Core Facility (GCF), Norwegian University of Science and Technology (NTNU), Trondheim, Norway. The genotype quality control and imputation has been conducted by the K.G. Jebsen center for genetic epidemiology, Department of public health and nursing, Faculty of medicine and health sciences, Norwegian University of Science and Technology (NTNU). Data from the Cancer Registry of Norway (CRN) has been used in this publication. The interpretation and reporting of these data are the sole responsibility of the authors, and no endorsement by CRN is intended nor should be inferred. Last, we want to thank Ass. Prof. Emily Annika Burger at the University of Oslo for her insightful advice on the cost analysis.

This work was supported by Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology (NTNU), National Institute of Health, University of Michigan, Norwegian Research Council.

Open access funding provided by NTNU Norwegian University of Science and Technology (incl St. Olavs Hospital - Trondheim University Hospital)

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Department of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Prinsesse Kristinas gate. 1, Trondheim, NO, 7030, Norway

Olav Toai Duc Nguyen & Oluf Dimitri Røe

Levanger Hospital, Nord-Trøndelag Hospital Trust, Cancer Clinic, Kirkegata 2, Levanger, NO, 7600, Norway

Department of Computer Science, University of Crete, Voutes Campus, Heraklion, GR, 70013, Greece

Ioannis Fotopoulos & Ioannis Tsamardinos

Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, Langnes, Tromsø, NO-9037, Norway

Therese Haugdahl Nøst

Department of Public Health and Nursing, Norwegian University of Science and Technology, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Håkon Jarls Gate 12, Trondheim, 7030, Norway

Institute of Applied and Computational Mathematics, FORTH, Heraklion, Crete, GR-700 13, Greece

Maria Markaki & Ioannis Tsamardinos

Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23952, Saudi Arabia

Vincenzo Lagani

SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence, Thuwal, 23952, Saudi Arabia

Institute of Chemical Biology, Ilia State University, Tbilisi, 0162, Georgia

JADBio Gnosis DA S.A, STEP-C, N. Plastira 100, Heraklion, 700-13, GR, Greece

Ioannis Tsamardinos

Clinical Cancer Research Center, Department of Clinical Medicine, Aalborg University Hospital, Hobrovej 18-22, Aalborg, DK-9100, Denmark

Oluf Dimitri Røe

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Contributions

All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.- Olav Toai Duc Nguyen: Conceptualisation, Investigation, Writing – Original Draft, Writing – Review & Editing, Visualization.- Ioannis Fotopoulos: Formal analysis, Validation, Data Curation, Writing – Review & Editing, Visualization - Therese Haugdahl Nøst: Writing – Review & Editing - Maria Markaki: Formal analysis, Validation, Writing – Review & Editing- Ioannis Tsamardinos: Methodology, Writing – Review & Editing - Vincenzo Lagani: Methodology, Writing – Review & Editing - Oluf Dimitri Røe: Conceptualisation, Methodology, Investigation, Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project administration.

Corresponding author

Correspondence to Oluf Dimitri Røe .

Ethics declarations

Ethical approval.

The Norwegian Data Inspectorate and the Regional Committees for Medical Research Ethics approved each individual study (Approval no 2012/168/REKnord and no. 8030.00319).

Consent to participate

Participants included in the HUNT2 and Tromsø cohorts all gave their written consent.

Competing interests

The authors declare no competing interests.

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Nguyen, O.T.D., Fotopoulos, I., Nøst, T.H. et al. The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models. J Cancer Res Clin Oncol 150 , 389 (2024). https://doi.org/10.1007/s00432-024-05909-w

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Received : 20 May 2024

Accepted : 29 July 2024

Published : 12 August 2024

DOI : https://doi.org/10.1007/s00432-024-05909-w

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