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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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Methodology

• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, 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 analyze the case, other interesting articles.

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

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

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.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

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

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

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.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

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

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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 analyze 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.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

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What is case study research?

Last updated

8 February 2023

Reviewed by

Cathy Heath

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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• 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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• 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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• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

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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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• 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

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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.

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This chapter reviews the strengths and limitations of case study as a research method in social sciences. It provides an account of an evidence base to justify why a case study is best suitable for some research questions and why not for some other research questions. Case study designing around the research context, defining the structure and modality, conducting the study, collecting the data through triangulation mode, analysing the data, and interpreting the data and theory building at the end give a holistic view of it. In addition, the chapter also focuses on the types of case study and when and where to use case study as a research method in social science research.

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Channaveer, R.M., Baikady, R. (2022). Case Study. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_21

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• Case Study | Definition, Examples & Methods

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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Case Study Research Method in Psychology

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Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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Case study research and causal inference

Judith green.

1 Wellcome Centre for Cultures & Environments of Health, University of Exeter, Exeter, UK

Benjamin Hanckel

2 Institute for Culture and Society, Western Sydney University, Sydney, Australia

Mark Petticrew

3 Department of Public Health, Environments & Society, London School of Hygiene & Tropical Medicine, London, UK

Sara Paparini

4 Wolfson Institute of Population Health, Queen Mary University of London, London, UK

5 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

Associated Data

Not applicable; no new data generated in this study.

For the purpose of open access, the author has applied a ‘Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

Case study methodology is widely used in health research, but has had a marginal role in evaluative studies, given it is often assumed that case studies offer little for making causal inferences. We undertook a narrative review of examples of case study research from public health and health services evaluations, with a focus on interventions addressing health inequalities. We identified five types of contribution these case studies made to evidence for causal relationships. These contributions relate to: (1) evidence about system actors’ own theories of causality; (2) demonstrative examples of causal relationships; (3) evidence about causal mechanisms; (4) evidence about the conditions under which causal mechanisms operate; and (5) inference about causality in complex systems. Case studies can and do contribute to understanding causal relationships. More transparency in the reporting of case studies would enhance their discoverability, and aid the development of a robust and pluralistic evidence base for public health and health services interventions. To strengthen the contribution that case studies make to that evidence base, researchers could: draw on wider methods from the political and social sciences, in particular on methods for robust analysis; carefully consider what population their case is a case ‘of’; and explicate the rationale used for making causal inferences.

Case study research is widely used in studies of context in public health and health services research to make sense of implementation and service delivery as enacted across complex systems. A recent meta-narrative review identified four broad, overlapping traditions in this body of work: developing and testing complex interventions; analysing change in organisations; undertaking realist evaluations; and studying complex change naturalistically [ 1 ]. Case studies can provide essential thick description of interventions, context and systems; qualitative understanding of the mechanisms of interventions; and evidence of how interventions are adapted in the ‘real’ world [ 2 , 3 ].

However, in evaluative health research, case study designs remain relegated to a minor, supporting role [ 4 , 5 ], typically at the bottom of evidence hierarchies. This relegation is largely due to assumptions that they offer little for making the kinds of causal claims that are essential to evaluating the effects of interventions. The strengths of deep, thick studies of specific cases are conventionally set against the benefits of ‘variable-based’ designs, with the former positioned as descriptive, exploratory or illustrative, and the latter as providing the strongest evidence for making causal claims about the links between interventions and outcomes. In conventional hierarchies of evidence, the primary evidence for making causal claims comes from randomised controlled trials (RCTs), in which the linear relationship between a change in one phenomenon and a later change in another can be delineated from other causal factors. The classic account of causality drawn on in epidemiology requires identifying that the relationship between two phenomena is characterised by co-variation; time order; a plausible relationship; and a lack of competing explanations [ 6 ]. The theoretical and pragmatic limitations of RCT designs for robust and generalizable evaluation of interventions in complex systems are now well-rehearsed [ 2 , 7 – 10 ]. In theory, though, random selection from a population to intervention exposure maximises ability to make causal claims: randomisation minimises risks of confounding, and enables both an unbiased estimate of the effect size of the intervention and extrapolation to the larger population [ 6 ]. Guidance for evaluations in which the intervention cannot be manipulated, such as in natural experiments, therefore typically focuses on methods for addressing threats to validity from non-random allocation in order to strengthen the credibility of probabilistic causal effect estimates [ 4 , 11 ].

This is, however, not the only kind of causal logic. Case study research typically draws on other logics for understanding causation and making causal inferences. We illustrate some of the contributions made by case studies, drawing on a narrative review of research relating to one particularly enduring and complex problem: inequalities in health. The causal chains linking interventions to equity outcomes are long and complex, with recognised limitations in the evidence base for ‘what works’ [ 12 ]. Case study research, we argue, has a critical role to play in making claims about whether, how and why interventions reduce, mitigate, or exacerbate inequalities. Our examples are drawn from a broader review of case study research [ 1 ] and supporting literature reviews [ 5 ], from which we focused on cases which had an explanatory aim, and which shed light on how interventions in public health or health services might reduce, create or sustain inequality. In this paper, we: i) outline some different kinds of evidence relevant to causal relationships that can be  derived from case study research; ii) outline what is needed for case study research to contribute to explanatory, as well as exploratory claims; and iii) advocate for greater clarity in reporting case study research to foster discoverability.

Cases and causes

There are considerable challenges in defining case study designs or approaches in ways that adequately delineate them from other research designs. Yin [ 13 ], for instance, one of the most highly cited source texts on case studies in health research [ 1 ], resists providing a definition, instead suggesting case study research is more a strategy for doing empirical research. Gerring [ 14 ] defines case study research as: “ an intensive study of a single unit for the purpose of understanding a larger class of (similar) units ” (p342, emphasis in original). This definition is useful in suggesting the basis for the inferences drawn from cases, and the need to consider the relationships between the ‘case’ (and phenomena observed within it) and the population from which it is drawn. Gerring notes that studies of single cases may have a greater “affinity” for descriptive aims, but that they can furnish “evidence for causal propositions” ( [ 14 ], p347). Case studies are, he suggests, more likely to be useful in elucidating deterministic causes: those conditions that are necessary and/or sufficient for an outcome, whereas variable based designs have advantages for demonstrating probabilistic causation, where the aim is to estimate the likelihood of two phenomena being causally related. Case studies provide evidence for the mechanisms of causal relationships (e.g. through process tracing, through observing two variables interacting in the real world) and corroboration of causal relationships (for instance, through pattern matching).

Gerring’s argument, drawing on political science examples, is that there is nothing epistemologically distinct about research using the case study: rather, it has particular affinities with certain styles of causal modelling. We take this as a point of departure to consider not whether case studies can furnish evidence to help with causal inference in health research, but rather how they have done this. From our examples on case study research on inequalities in health, we identify the kinds of claims that relate to causality that were made. We note that some relate to (1) Actors’ accounts of causality : that is, the theories of those studied about if, how and why interventions work. Other types of claim use various kinds of comparative analytic logic to elucidate evidence of causal relationships between phenomena. These claims include: (2) Demonstrations of causal relationships – in which evidence from one case is sufficient for identifying a plausible causal relationship; (3) Mechanisms – evidence of the mechanisms through which causal relationships work; (4) Conditions —evidence of the conditions under which such mechanisms operate; and (5) Complex causality —evidence for outcomes that arise from complex causality within a system. This list is neither mutually exclusive, nor exhaustive: many case studies aim to do several of these (and some more). It is also a pragmatic rather than theoretical list, focusing on the kinds of evidence claimed by researchers rather than the formal methodological underpinnings of causal claims (for a discussion of the latter, see Rohlfing [ 15 ]).

What kinds of causal evidence do case studies provide?

Actors’ accounts of causality.

This is perhaps the most common kind of evidence provided by case study research. Case studies, through in-depth research on the actors within systems, can generate evidence about how those actors themselves account for causal relationships between interventions and outcomes. This is an overt aim of many realist evaluation studies, which focus on real forces or processes that exist in the world that can provide insight into causal mechanisms for change.

Ford and colleagues [ 16 ], for example, used a series of five case studies of local health systems to explore socio-economic inequalities in unplanned hospital admission. Cases were selected on the basis of either narrowing or widening inequalities in admission, with a realist evaluation focused on delineating the context-mechanisms-outcome (CMO) configurations in each setting, to develop a broader theory of change for addressing inequalities. The case study approach used a mix of methods, including drawing on documentary data to assess the credibility of mechanisms proposed by health providers. The authors identified 17 distinct CMO configurations; and five factors that were related to trends for inequalities in emergency admissions, including health service factors (primary care workforce challenges, case finding and proactive case management) and those external to the health service (e.g., financial constraints on public services, residential gentrification). Ford and colleagues noted that none of the CMO configurations were clearly associated with improved or worsening trends in inequalities in admission.

Clearly, actors’ accounts of causality are not in themselves evidence of causality. Ford and colleagues noted that they interrogated accounts for plausibility (e.g. that interventions mentioned were prior to effects claimed) and triangulated these accounts with other sources of data, but that inability to empirically corroborate the hypothesized CMO links limited their ability to make claims about causal inference. This is crucial: actors in a system may be aware of the forces and processes shaping change but unaware of counterfactuals, and they are unlikely to have any privileged insight into whether factors are causal or simply co-occurring (see, for instance, Milton et. al. [ 17 ] on how commonly cited ‘barriers’ in accounts of not doing evaluations are also evident in actors’ accounts of doing successful evaluations). Over-interpretation of qualitative accounts of insiders’ claims about causal relationships as if they provide conclusive evidence of causal relationships is poor methodology.

This does not mean that actors’ accounts are not of value. First, in realist evaluation, as in Ford and colleagues’ study [ 16 ], these accounts provide the initial theories of change for thinking about the potential causal pathways in logic models of interventions. Second, insiders’ accounts of causality are part of the system that is being explained. An example comes from Mead and colleagues [ 18 ], who used a case study drawing largely on qualitative interviews to explore “how local actors from public health, and the wider workforce, make sense of and work on social inequalities in health” ( [ 18 ] p168). This used a case study of a partnership in northwest England to address an enduring challenge in inequalities policy: the tendency for policies that address upstream health determinants to transform, in practice, to focus more on behavioural and individual level factors . Local public health actors in the partnership recognised the structural causes of unequal health outcomes, yet discourses of policy action tended to focus only on the downstream, more individualising levels of health, and on personal choice and agency as targets for intervention. Professionals conceptualised action on inequality as relating only to the health of the poorest, rather than as a problem of a gradient in health outcomes across society. There was a geographical localism in their approach, which framed particular places as constellations of health and social problems. Drawing on theory from figurational sociology, Mead and colleagues note that actors’ own accounts are the starting point of an analysis, which then puts those accounts into play with theory about how such discourses are reproduced. The researchers suggest that partnership working itself exacerbated the individualising frameworks used to orient action, as it became a hegemonic framing, reducing the possibilities for partnerships to transform health inequalities. Here, then, a case study approach is used to shed light on the causes of a common failure in policies addressing inequalities. The authors take seriously the divergence of actors’ own accounts of causality and those of other sources, and analyse these as part of the system.

Finally, insider accounts should be taken seriously as contributing to evidence about causal inference through shedding light on the complex looping effects of theoretical models of causality and public accounts. For instance, Smith and Anderson [ 19 ], drawing on a meta-ethnographic literature review of ‘lay theorising’ about health inequalities, note that, counter to common assumptions, public understanding of the structural causes of health inequalities is sophisticated: but that it may be disavowed to avoid stigma and shame and to reassert some agency. This is an important finding for informing knowledge exchange, suggesting that further ‘awareness raising’ may be unnecessary for policy change, and counter-productive in needlessly increasing stigma and shame.

Demonstrations of causal relationships

When strategically sampled, and rooted in a sound theoretical framework, studies of single cases can provide evidence for generalizable causal inferences. The strongest examples are perhaps those that operate as ‘black swans’ for deterministic claims, in that one case may be all that is needed to show that a commonly held assumption is not generalizable. That is, a case study can demonstrate unequivocally that one phenomenon is not inevitably related to another. These can come from cases sampled because they are extreme or unusual. Prior’s [ 20 ] study of a single man in a psychiatric institution in Northern Ireland, for instance, showed that, counter to Goffman’s [ 21 ] original theory of how ‘total institutions’ lead to stigmatisation and depersonalisation, the effects of institutionalisation depended on context—in this case, how the institution related to the local community and the availability of alternative sources of self-worth available to residents.

Strategically sampled typical cases can also provide demonstrative evidence of causal relationships. To take the enduring health services challenge of inequalities in self-referral to emergency care, Hudgins and Rising’s [ 22 ] case study of a single patient is used to debunk a common assumption that high use of emergency care is related to inappropriate care-seeking by low-income patients. They look in detail at the case of “a 51-year-old low-income, recently insured, African American man in Philadelphia (USA) who had two recent ED [emergency department] visits for evaluation of frequent headaches and described fear of being at risk for a stroke.” ( [ 22 ] p50). Drawing on theories of structural violence and patient subjectivity, they use this single case to shed light on why emergency department use may appear inappropriate to providers. They analyse the interplay of gender roles, employment, and insurance status in generating competing drivers of health seeking, and point to the ways in which current policies deterring self-referral do not align well with micro- and macro-level determinants of service use. The study authors also note that because their methods generate data on ‘why’ as well ‘what’ people do, they can “lay the groundwork” ( [ 22 ], p54] for developing future interventions. Here, again, a single case is sufficient. In understanding the causal pathways that led to this patient’s use of emergency care, it is clear why policies addressing inequalities through deterring low-income users would be unlikely to work.

Mechanisms: how causal relationships operate

A strength of case study approaches compared with variable-based designs is furnishing evidence of how causal relationships operate, deriving from both direct observations of causal processes and from analysis of comparisons within and between cases. All cases contain multiple observations; variations can be observed over time and space, across or within cases [ 14 ]. Observing regularities, co-variation and deviant or surprising findings, and then using processes of analytic induction [ 23 ] or abductive logic [ 24 ] to derive, develop and test causal theories using observations from the case, can build a picture of causal pathways.

Process tracing is one formal qualitative methodology for doing this. Widely used in political and policy studies, but less in health evaluations [ 25 ], process tracing links outcomes with their causes, focusing on the mechanisms that link events on causal pathways, and on the strength of evidence for making connections on that causal chain. This requires sound theoretical knowledge (such that credible hypotheses can be developed), well described cases (ideally at different time points), observed causal processes (the activities that transfer causes to effects), and careful assessment of evidence against tests of varying strength for the necessity and sufficiency for accepting or rejecting a candidate hypothesis [ 26 , 27 ]. In health policy, process tracing methods have been combined to good effect with quantitative measures to examine casual processes leading to outcomes of interest. Campbell et. al. [ 28 ], for instance, used process tracing to look at four case studies of countries that had made progress towards universal health coverage (measured through routine data on maternal and neonatal health indicators), to identify key causal factors related to health care workforce.

An example of the use of process tracing in evaluation comes from Lohmann and colleagues’ [ 25 ] case study of a single country, Burkina Faso, to examine why performance based financing (PBF) fails to improve equity. PBF, coupled with interventions to improve health care take up among the poor, aims to improve health equity in low and middle-income countries, yet impact evaluations suggest that these benefits are typically not realised. This case study drew on data from the quantitative impact assessment; programme documentation; the intervention process evaluation; and primary qualitative research for the process tracing, in the light of the theory of change of the intervention. Lohmann and colleagues [ 25 ] identified that a number of conditions that would have been necessary for the intervention to work had not been met (such as eligible patients not receiving the card needed to access health care or providers not receiving timely reimbursement). A key finding was that although implementation challenges were a partial cause of policy failure, other causal conditions were external to the intervention, such as lack of attention to the non-health care costs incurred by the poorest to access care. Again, a single case, if there are good grounds for extrapolating to similar contexts (i.e., those in which transport is required to access health care), is enough to demonstrate a necessary part of the causal pathway between PBF and intended equity outcomes.

Conditions under which causal mechanisms operate

The example of ‘transport access’ as a necessary condition for PBF interventions to ‘work’ also illustrates a fourth type of causal evidence: that relating to the transferability of interventions. Transferable causal claims are essential for useful evidence: “(f)or policy and practice we do not need to know ‘it works somewhere’. We need evidence for ‘it-will-work-for-us’ claims: the treatment will produce the desired outcome in our situation as implemented there” ( [ 8 ] p1401). Some causal mechanisms operate widely (using a parachute will reduce injury from jumping from a plane; taking aspirin will relieve pain); others less so. In the context of health services and public health research, few interventions are likely to be widely generalizable, as the mechanisms will operate differently across contexts [ 7 ]. This context dependency is at the heart of realist evaluations, with the assumption that underlying causal mechanisms require particular contexts in order to operate, hence the focus on ‘how, where, and for whom’ interventions work [ 29 ]. Making useful claims therefore requires other kinds of evidence, relating to what Cartwright and Munro [ 30 ] call the ‘capacities’ of the intervention: what power it has to work reliably, what stops it working, what other conditions are needed for it to work. This evidence is critical for assessing whether an intervention is likely to work in a given context and to assess the intended and unintended consequences of intervention adoption and implementation. Cartwright and Munro’s recommendation is therefore to study causal powers rather than causes. That is, as well as interrogating whether the intervention ‘causes’ a particular outcome, it is also necessary to address the potential for and stability of that causal effect. To do that entails addressing a broader range of questions about the causal relationship, such as how the intervention operates in order to bring about changes in outcomes; what other conditions need to be present; what might constrain this effect; what other factors within the system also promote or constrain those effects; and what happens when different capacities interact? [ 30 ]. Case study research can be vital in providing this kind of evidence on the capacities of interventions [ 31 ].

One example is from Gibson and colleagues [ 32 ], who use within-case comparisons to shed light on why a ‘social prescribing’ intervention may have different effects across socioeconomic classes. These interventions, typically entailing link workers who connect people with complex health care needs to local services and resources, are often framed as a way to address enduring health inequalities. Drawing on sociological theory on how social class is reproduced through socially structured and unequal distribution of resources (‘capitals’), and through how these shape people’s practices and dispositions, Gibson and colleagues [ 32 ] explicate how capitals and dispositions shaped encounters with the intervention. Their analysis of similarities and differences within their case (of different clients) in the context of theory enables them to abstract inferences from the case. Drawing out the ways in which more advantaged clients mobilised capital in their pursuit of health, with dispositions more closely aligned to the intervention, they unravel classed differences in ability to benefit from the intervention, with less advantaged clients inevitably having ‘shorter horizons’ focused on day to day challenges: “This challenges the claim that social prescribing can reduce inequalities, instead suggesting it has the potential to exacerbate existing inequalities” ( [ 32 ], p6).

Case studies can shed light on the capacities of interventions to improve or exacerbate inequalities, including identifying unforeseen consequences. Hanckel and colleagues [ 33 , 34 ], for example, used a case study approach to explore implementation of a physical health intervention involving whole classes of children running for 15 min each day in the playground in schools in south London, UK. This documented considerable adaption of the intervention at the level of school, class and pupil, and identified different pathways through which the intervention might impact on inequalities. In terms of access, the intervention appeared to be equitable, in that there was no evidence of disproportionate roll out to schools with more affluent pupils or to those with fewer minority ethnic pupils [ 33 ]. However, identifying the ‘capacities’ of the intervention also identified other pathways through which it could have negative equity effects. The authors found that in practice, the intervention emphasised body weight rather than physical activity, and intervention roll-out reinforced class and ethnicity-based stigmatising discourses about lower income neighbourhoods [ 34 ].

Complex causality

There is increasing recognition that the systems that reproduce unequal health outcomes are complex: that is, that they consist of multiple interacting components that cannot be studied in isolation, and that change is likely to be non-linear, characterised by, for instance, phase shifts or feedback loops [ 35 ]. This has two rather different implications. First, case study designs can be particularly beneficial for taking a system perspective on interventions. Case studies enable a focus on aspects that are not well explicated through other designs, such as how context interacts with interventions within systems [ 7 ], or on how multiple conditional pathways might link interventions and outcomes [ 36 ]. Second, when causation is not linear, but ‘emergent’, in that it is not reducible to the accumulated changes at lower levels, evaluation designs focused on only one outcome at one level (such as weight loss in individuals) may fail to identify important effects. Case studies have an invaluable role here in unpacking and surfacing these effects at different levels within the systems within which interventions and services are delivered. One example is transport systems, which have been the focus of considerable public health interest to encourage more ‘active’ modes, in which more of the population walk or cycle, and fewer drive. However, more simplistic evaluations looking at one part of a causal chain (such as that between traffic calming interventions and local mode shift) may fail to appreciate how systems are dynamic, and that causation might be emergent. This is evident in a case study of transport policy impacts from Sheller [ 37 ], who takes the case of Philadelphia, USA, to reveal how this post-car trend has racialized effects that can exacerbate inequality. Weaving in data from participant observations, historical documentary sources and statistical evidence of declining car use, Sheller documents the racialized impacts of transport policies which may have reduced car use and encouraged active modes overall, but which have largely prioritised ‘young white’ mobility in the context of local gentrification and neglect of public transit.

One approach to synthesising evidence from multiple case studies to make claims about complex causation is Qualitative Comparative Analysis (QCA), which combines quantitative methods (based on Boolean algebra) with detailed qualitative understanding of a small to medium N sample of cases. This has strengths for identifying multiple pathways to outcomes, asymmetrical sets of conditions which lead to success or failure, or ‘conjunctural causation’, whereby some conditions are only causally linked to outcomes in relation to others [ 38 ]. There is growing interest in using these approaches in evaluative health studies [ 39 ]. One example relating to the effectiveness of interventions addressing inequalities in health comes from Blackman and colleagues [ 36 ], who explored configurations of conditions which did or did not lead to narrowing inequalities in teenage conception rates across a series of local areas as cases. This identified some surprising findings, including that ‘basic’ rather than good or exemplary standards of commissioning were associated with narrowing the equity gap, and that the proportion of minority ethnic people in the population was a key condition.

Not all case study research aims to contribute to causal inference, and neither should it [ 1 , 5 , 40 ]. However, it can. We have identified five ways in which case study evidence has contributed to causal explanations in relation to a particularly intractable challenge: inequalities in health. It is therefore time to stop claiming that case study designs have only a supporting role to play in evaluative health research. To develop a theoretical evidence base on ‘what works’, and how, in health services and public health, particularly around complex issues such as addressing unequal health outcomes, we need to draw on a greater range of evidential resources for informing decisions than is currently used. Best explanations are unlikely to be made from single studies based on one kind of causality, but instead will demand some kind of evidential pluralism [ 41 ]. That is, one single study, of any design, is unlikely to generate evidence for all links in complex causal chains between an intervention and health outcomes. We need a bricolage of evidence from a diverse range of designs [ 42 ] to make robust and credible cases for what will improve health and health equity. This will include evidence from case studies, both from single and small N studies, and from syntheses of findings from multiple cases.

Our focus on case studies that shed light on interventions for health inequalities identified the critical role that case studies can play in theorising, illuminating and making sense of: system actors’ own causal reasoning; whether there are causal links between intervention and outcome; what mechanism(s) might link them; when, where and for whom these causal relationships operate; and how unequal outcomes can be generated from the operation of complex systems. These examples draw on a range of different theoretical and methodological approaches, often from the wider political and social sciences. The approaches illustrated are rooted in very different, even incompatible, philosophical traditions: what researchers understand by ‘causality’ is diverse [ 43 ]. However, there are two commonalities across this diversity that suggest some conditions for producing good case studies that can generate evidence to support causal inferences. The first is the need for theoretically informed and comparative analysis. As Gerring [ 14 ] notes, causal inferences rely on comparisons – across units or time within a case, or between cases. It is comparison that drives the ability to make claims about the potential of interventions to produce change in outcomes of interest, and under what conditions. There are a range of approaches to qualitative data analysis, and choice of method has to be appropriate for the kinds of causal logics being explicated, and the availability of data on particular phenomena within the case. Typically, though, this will require analysis that goes beyond descriptive thematic analysis [ 31 ]. Approaches such as process tracing or analytic induction require both fine-grained and rigorous comparative analysis, and a sound theoretical underpinning that provides a framework for making credible inferences about the relationships between phenomena within the case and to the wider population from which the case is selected.

This leads to the second commonality: the need to clarify what the case is a case ‘of’, and how it relates to other candidate cases. What constitutes a ‘case’ is inevitably study specific. The examples we have drawn on include: PBF in a country [ 25 ], transport systems in a city [ 37 ], and a social prescribing intervention in primary care [ 32 ]. Clearly, in other contexts, each of these ‘cases’ could be sampling units within variable based studies (of financing systems, or countries; of infrastructures systems, or cities in a state; of particular kinds of service intervention, or primary care systems). Conversely, these cases could be populations within which lower level phenomena (districts, neighbourhoods, patients) are studied. What leads to appropriate generalisations about causal claims is a sound theorisation of the similarities and particularities of the case compared with other candidate cases: how Burkina Faso has commonalities with, or differences from, other settings in which PBF has failed to improve equity; or the contexts of gentrification and residential churn that make Philadelphia similar to other cities in the US; or the ways in which class-based dispositions and practices intersect with similar types of service provisions.

A critical question remains: How can well-conducted case study evidence be better integrated into the evidence base? Calls for greater recognition for case study designs within health research are hardly new: Flyvberg’s advocacy for a greater role for case studies in the social sciences [ 44 ] has now been cited around 20,000 times, and calls for methodological pluralism in health research go back decades [ 42 , 45 , 46 ]. Yet, case studies remain somewhat neglected, with ongoing misconceptions about their limited role, despite calls for evidence based medicine to incorporate evidence for mechanisms as complementary to evidence of correlation, rather than as inferior [ 47 ]. Even where the value of case studies for contributing to causal inference is recognised, searching for good evidence is not straightforward. Case studies are neither consistently defined nor necessarily well reported. Some of the examples in this paper do not use the term ‘case study’ in the title or abstract, although they meet our definition. Conversely, many small scale qualitative studies describe themselves as ‘case studies’, but focus on thick description rather than generalisability, and are not aiming to contribute to evaluative evidence. It is therefore challenging, currently, to undertake a more systematic review of empirical material. Forthcoming guidance on reporting case studies of context in complex systems aims to aid discoverability and transparency of reporting (Shaw S, et al: TRIPLE C Reporting Principles for Case study evaluations of the role of Context in Complex interventions, under review). This recommends including ‘case study’ in the title, clarifying how terms are used, and explicating the philosophical base of the study. To further advance the usefulness of case study evidence, we suggest that where an aim is to contribute to causal explanations, researchers should, in addition, specify their rationales for making causal inferences, and identify what broader class of phenomena their case is a case ‘of’.

Conclusions

Case study research can and does contribute to evidence for causal inferences. On challenging issues such as addressing health inequalities, we have shown how case studies provide more than detailed description of context or process. Contributions include: describing actors’ accounts of causal relationships; demonstrating theoretically plausible causal relationships; identifying mechanisms which link cause and effect; identifying the conditions under which causal relationships hold; and researching complex causation.

Acknowledgements

The research underpinning this paper was conducted as part of the Triple C study. We gratefully acknowledge the input of the wider study team, and that of the participants at a workshop held to discuss forthcoming guidance on reporting case study research.

Abbreviations

Authors’ contributions.

BH, JG and MP drafted the first version of the paper, which was revised with theoretical input from SS and SP. All authors contributed to the paper and have reviewed and approved the final manuscript.

The research was funded by the Medical Research Council (MR/S014632/1). JG is supported with funding from the Wellcome Trust (WT203109/Z/16/Z). Additional funding for SP and SS salaries over the course of the study was provided by the UK National Institute for Health Research Oxford Biomedical Research Centre (BRC-1215–20008), Wellcome Trust (WT104830MA; 221457/Z/20/Z) and the University of Oxford's Higher Education Innovation Fund.

The views and opinions expressed herein are those of the authors. Funding bodies had no input to the design of the study and collection, analysis, and interpretation of data or preparation of this paper.

Availability of data and materials

Declarations.

Not applicable.

The authors declare that they have no competing interests.

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• Published: 18 April 2024

A method for identifying different types of university research teams

• Zhe Cheng   ORCID: orcid.org/0009-0002-5120-6124 1 ,
• Yihuan Zou 1 &
• Yueyang Zheng   ORCID: orcid.org/0000-0001-7751-2619 2  

Humanities and Social Sciences Communications volume  11 , Article number:  523 ( 2024 ) Cite this article

Metrics details

Identifying research teams constitutes a fundamental step in team science research, and universities harbor diverse types of such teams. This study introduces a method and proposes algorithms for team identification, encompassing the project-based research team (Pbrt), the individual-based research team (Ibrt), the backbone-based research group (Bbrg), and the representative research group (Rrg), scrutinizing aspects such as project, contribution, collaboration, and similarity. Drawing on two top universities in Materials Science and Engineering as case studies, this research reveals that university research teams predominantly manifest as backbone-based research groups. The distribution of members within these groups adheres to Price’s Law, indicating a concentration of research funding among a minority of research groups. Furthermore, the representative research groups in universities exhibit interdisciplinary characteristics. Notably, significant differences exist in collaboration mode and member structures among high-level backbone-based research groups across diverse cultural backgrounds.

Introduction

Team science has emerged as a burgeoning field of inquiry, attracting the attention of numerous scholars (e.g., Stokols et al., 2008 ; Bozeman & Youtie, 2018 ; Coles et al., 2022 ; Deng et al., 2022 ; Forscher et al., 2023 ), who endeavor to explore and try to summarize strategies for fostering effective research teams. Conducting team science research would help improve team efficacy. The National Institutes of Health in the USA pointed out that team science is a new interdisciplinary field that empirically examines the processes by which scientific teams, research centers, and institutes, both large and small, are structured (National Research Council, 2015 ). In accordance with this conceptualization, research teams can be delineated into various types based on their size and organizational form. Existing research also takes diverse teams as focal points when probing issues such as team construction and team performance. For example, Wu et al. ( 2019 ) and Abramo et al. ( 2017 ) regard the co-authors of a single paper as a team, discussing issues of research team innovation and benefits. Meanwhile, Zhao et al. ( 2014 ) and Lungeanu et al. ( 2014 ) consider the project members as a research team, exploring issues such as internal interest distribution and team performance. Boardman and Ponomariov ( 2014 ), Lee et al. ( 2008 ), and Okamoto and Centers for Population Health and Health Disparities Evaluation Working Group ( 2015 ) view the university’s research center as a research group, investigating themes about member collaboration, management, and knowledge management portals.

Regarding the definition of research teams, some researchers believe that a research team is a collection of people who work together to achieve a common goal and discover new phenomena through research by sharing information, resources, and professional expertise (Liu et al., 2020 ). Conversely, others argue that groups operating across distinct temporal and spatial contexts, such as virtual teams, do not meet the criteria for teams, as they engage solely in collaborative activities between teams. According to this perspective, Research teams should be individuals collaborating over an extended period (typically exceeding six months) (Barjak & Robinson, 2008 ). Contemporary discourse on team science tends to embrace a broad conceptualization wherein research teams include both small-scale teams comprising 2–10 individuals and larger groups consisting of more than 10 members (National Research Council, 2015 ). These research teams are typically formed to conduct a project or finish research papers, while research groups are formed to solve complex problems, drawing members from diverse departments or geographical locations.

Obviously, different research inquiries are linked to different types of research teams. Micro-level investigations, such as those probing the impact of international collaboration on citations, often regard co-authors of research papers as research teams. Conversely, meso-level inquiries, including those exploring factors impacting team organization and management, often view center-based researchers as research groups. Although various approaches can be adopted to identify research teams, such as retrieving names from research centers’ websites or obtaining lists of project-funded members, when the study involves a large sample size and requires more data to measure the performance of research teams, it becomes necessary to use bibliometric methods for team identification.

Existing literature on team identification uses social network analysis (Zhang et al., 2019 ), cohesive subgroup (Dino et al., 2020 ), faction algorithm (Imran et al., 2018 ), FP algorithm (Liao, 2018 ), etc. However, these identification methods often target a singular type of research team or fail to categorize the identified research teams. Moreover, existing studies mostly explore the evolution of specific disciplines (Wang et al., 2017 ), with limited attention devoted to identifying university research teams and the influencing factors of team effectiveness. Therefore, this study tries to develop algorithms to identify diverse university research teams, drawing insights from two universities characterized by different cultural backgrounds. It aims to address two research questions:

How can we identify different types of university research teams?

What are the characteristics of research groups within universities?

Literature review

Why is it necessary to identify research teams? The research focuses on scientific research teams, mostly first identifying the members of research teams through their names on the list of funding projects or institutions’ websites and then conducting research through questionnaires or interviews. However, this methodology may compromise research validity for several reasons. Firstly, the mere inclusion of individuals on funding project lists does not guarantee genuine research team membership or substantive collaboration among members. Secondly, the institutional website generally announces important research team members, potentially overlooking auxiliary personnel or important members from external institutions. Thirdly, reliance solely on lists of research team members fails to capture nuanced information about the team, such as their research ability or communication intensity, thus hindering the exploration of team science-related issues.

Consequently, researchers have turned to co-authorship and citation to identify research teams using established software tools and customized algorithms. For example, Li and Tan ( 2012 ) applied UCINET and social network analysis to identify university research teams, while Hu et al. ( 2019 ) used Citespace to analyze research communities of four disciplines in China, the UK, and the US. Similarly, some researchers also identify the members and leaders of research teams by using and optimizing existing algorithms. For example, Liao ( 2018 ) applied the Fast-Unfolding algorithm to identify research teams in the field of solar cells, while Yu et al. ( 2020 ) and Li et al. ( 2017 ) employed the Louvain community discovery algorithm to identify research teams in artificial intelligence. Lv et al. ( 2016 ) applied the FP-GROWTH algorithm to identify core R&D teams. Yu et al. ( 2018 ) used the faction algorithm to identify research teams in intelligence. Dino et al. ( 2020 ) developed the CL-leader algorithm to confirm research teams and their leaders. Boyack and Klavans ( 2014 ) regard researchers engaged in the same research topic as research teams based on citation information. Notably, these community detection algorithms complement each other, offering versatile tools for identifying research teams.

Despite the utility of these identification methods, they are not without limitations. For example, fixed software algorithms are constrained by predefined rules, posing challenges for researchers seeking to customize identification criteria. Moreover, for developed algorithms, although algorithms based on computer programming languages have high accuracy, they overemphasize the connection relationship between members and do not consider the definition of research teams. In addition, research based on co-authorship networks and community identification algorithms faces inherent problems: (1) Ensuring temporal consistency in co-authorship networks is challenging due to variations in publication timelines, potentially undermining the temporal alignment of team member collaborations; (2) The lack of stability in team identification result means that different identification standards would produce different outcomes; (3) Team members only belong to one research team, but in the actual process, researchers often participate in multiple research teams with different identities, or the same members conduct research in different team combinations.

In summary, research teams in a specific field can be identified using co-authorship information, designing or introducing identification algorithms. However, achieving more accurate identification necessitates consideration of the nuanced definition of research teams. Therefore, this study focuses on university research teams, addressing temporal and spatial collaboration issues among team members by incorporating project information and first-author information. Furthermore, it tackles the issue of classifying research team members by introducing Price’s Law and Everett’s Rule. Additionally, it tackles the issue of team members’ multiple affiliations through the Jaccard Similarity Coefficient and the Louvain Algorithm. Ultimately, this study aims to achieve the classification recognition of university research teams.

Team identification method

An effective team identification method requires both consideration of the definition of research teams and the ability to transform this definition into operable programming languages. University research teams, by definition, comprise researchers collaborating towards a shared objective. As a typical form of the output of a research team, the co-authorship of a scientific research paper implies information exchange and interaction among team members. Thus, this study uses co-authorship relationships within papers to reflect the collaborative relationships among research team members. In this section, novel algorithms for identifying research teams are proposed to address deficiencies observed in prior research.

Classification of research team members

A researcher might be part of multiple research teams, with varying roles within each. Members of the research team can be categorized according to how the research team is defined.

The original idea of team member classification

The prevailing notion of teams underscores the collaborative efforts between individual team members and their contributions toward achieving research objectives. This study similarly classifies team members based on these dual dimensions.

In terms of overall contributions, members who make substantial contributions are typically seen as pivotal figures within the research team, providing the primary impetus for the team’s productivity. Conversely, those with lesser input only contribute to specific facets of the team’s goals and engage in limited research activities, thus being regarded as standard team members.

In terms of collaboration, it is essential to recognize that high levels of contribution do not inherently denote a core position within a team. The collaboration among team members serves as an important indicator of their identity characteristics within the research team. Based on the collaboration between members, this study believes that researchers who have high contributions and collaborate with many high-contribution team members assume the core members of the research team. Conversely, members who have high contributions but only collaborate with a limited number of high-contribution team members are identified as backbone members. Similarly, members displaying low levels of contributions but collaborating widely with high contributors are categorized as ordinary members. Conversely, those with low contributions and limited collaboration with high-contributing team members are regarded as marginal members of the research team.

Establishment of team member classification criteria

This study introduces Price’s Law and Everett’s Rule to realize the idea of team member classification.

In terms of overall contribution, the well-known bibliometrics Price, drawing from Lotka’s Law, deduced that the number of papers published by prolific scientists is 0.749 times the square root of the number of papers published by the most prolific scientist in a group. Existing research also used this law when analyzing prolific authors of an organization. This study believes that prolific authors who conform to Price’s Law are important members who contribute more to the research team.

In terms of collaboration, existing research mostly employs the concept of factions. Factions refer to a relationship where members reciprocate and cannot readily join new groups without altering the reciprocal nature of their factional ties. However, in real-world settings, relationships with overtly reciprocal characteristics are uncommon. Therefore, to ensure the applicability and stability of the faction, Seidman and Foster ( 1978 ) proposed the concept of K-plex, pointing out that in a group of size n, when the number of direct connections of any point in the group is not less than n-k, this group is called k-plex. For k-plex, as the number k increases, the stability of the entire faction will decrease. Addressing this concern, renowned sociologist Martin Everett ( 2002 ), based on the empirical rule of research, proposed specific values for k and corresponding minimum group sizes, stipulating that the overall team size should not fall below 2k-1 (Scott, 2017 ). The expression is:

In other words, for a K-plex, the most acceptable definition to qualify as a faction is when each member of the team is directly connected to at least ( n  − 1)/2 members of the team. Applied to research teams, this empirical guideline necessitates that team members maintain collaborative ties with at least half or more of the team.

Based on Price’s Law and Everett’s Empirical Rule, this study gives the criteria for distinguishing prolific authors, core members, backbone members, ordinary members, and marginal members of research teams. The specifics are shown in the following Table 1 .

Classification of research teams

Within universities, a diverse array of research teams exists, categorized by their scale, the characteristics of funded projects, and the platforms they rely upon. This study proposes the identification algorithms for project-based teams, individual-based teams, backbone-based groups, and representative groups.

Project-based research teams: identification based on research projects

Traditional methods for identifying research teams attribute co-authorship to collaboration among multiple authors without considering the time scope. However, in practice, collaborations vary in content and duration. Therefore, in the identification process, it is necessary to introduce appropriate standards to distinguish varying degrees of collaboration and content among scholars.

Research projects serve as evidence of researchers engaging in the same research topic, thereby indicating that the paper’s authors belong to the same research team. Upon formal acceptance of a research paper, authors typically append funding information to the paper. Therefore, papers sharing the same funding information can be aggregated into paper clusters to identify the research team members who completed the fund project. The specific steps proposed for identifying a single research project fund are as follows.

Firstly, extract the funding number and regard all papers attached with the same funding number as a paper cluster. Secondly, construct a co-authorship network based on the paper cluster. Thirdly, identify the research team using the team member classification criteria.

Individual-based research teams: team identification based on the first author

For research papers lacking project numbers, clustering can be performed based on the contribution and research experience of the authors. Each co-author of the research paper contributes differently to the paper’s content. In 2014, the Consortia Advancing Standards in Research Administration Information (CASRAI) proposed classification standards for paper contributions, including 14 types such as conceptualization, data processing, formal analysis, funding acquisition, investigation, methods, project management, resources, software, supervision, validation, visualization, paper writing, review, and editing.

In this study, the primary author of a paper lacking project funding is considered the initiator, while other authors are seen as contributors who advance and finalize the research. For papers not affiliated with any project, the first author and all their published papers form a paper group for team identification purposes. The procedure entails the following steps: Initially, gather the first author and all papers authored by them within the identification period to constitute a paper group. Subsequently, a co-authorship network will be constructed using the papers within the group. Lastly, the research team will be identified based on the criteria for classifying team members.

Backbone-based research group: merging based on project-based and individual-based research teams

Research teams can be identified either by a single project number or by individual researchers. Upon identification, it becomes evident that many research teams share similar members. This is because a research team may engage in multiple projects, and some members collaborate without funding support. While identification algorithms are suitable for evaluating the quality of a research article or funding, they may not suffice when assessing the research group, or they may not suffice when assessing the key factors affecting their performance. To address this, it is necessary to merge highly similar individual-based or project-based research teams according to specific criteria. The merged one should be termed a group, as it encompasses multiple project-based and individual-based research teams.

In the pursuit of building world-class universities, governments worldwide often emphasize the necessity of fostering research teams led by discipline backbones. In this vein, this study further develops a backbone-based research group identification algorithm, which considers project-based and individual-based research teams.

Identification of university discipline backbone members

Previous studies have summarized the characteristics of the university discipline backbones, revealing that these individuals often excel in indicators such as degree centrality, eigenvector centrality, and betweenness centrality. Each centrality indicator demonstrates a strong positive correlation with the author’s output volume, indicating that high-productive researchers with more collaborators are more inclined to be university discipline backbones. Based on these characteristics, Price’s law is applied, defining discipline backbone members as researchers whose publications count exceeds 0.749 times the square root of the highest publication count within the discipline.

Team identification with discipline backbone members as the Core

Following the identification of discipline backbones, this study consolidates paper groups wherein the discipline backbone serves as the core member of either individual-based or project-based research teams. Subsequently, backbone-based research groups are formed.

Merging based on similarity perspective

It should be noted that different discipline backbones may simultaneously participate as core members in the same individual-based or project-based research teams. Consequently, distinct backbone-based research groups may encompass duplicate project-based and individual-based research teams, necessitating the merging of backbone-based research groups.

To address this redundancy issue, this study introduces the concept of similarity in community identification. In the community identification process, existing algorithms often assess whether to incorporate members into the community based on their level of similarity. Among various algorithms for calculating similarity, the Jaccard coefficient is deemed to possess superior validity and robustness in merging nodes within network communities (Wang et al., 2020 ). Its calculation formula is as follows.

N i denotes the nodes within subset i , while N j represents the nodes within subset j ; N i  ∩ N j signifies the nodes present in both subsets, whereas N i ∪ N j encompasses all nodes in subsets i and j . Existing research shows that when the Jaccard coefficient equals or exceeds 0.5 (Guo et al., 2022 ), the community identification algorithm achieves optimal precision.

In the context of this study, N i represents the core and backbone members of research group i , while N j denotes the core and backbone members of research group j . If these two groups exhibit significant overlap in core and backbone members, the papers from both research groups are merged into a new set of papers to identify the research team.

Given the efficacy of the Jaccard similarity measure in identifying community networks and merging, this study employs this principle to merge backbone-based research groups. Specifically, groups are merged if the Jaccard similarity coefficient between their core and backbone members equals or exceeds 0.5. Subsequently, new research groups are formed based on the merged set of papers.

It’s important to note that during the merging process, certain research teams within a backbone-based group may be utilized multiple times. Initially, the merging occurs based on the core and backbone members of the backbone-based research group, adhering to the Jaccard coefficient criterion. However, since project or individual-based research teams within a backbone-based research group may be reused, resulting in the similarity of research papers across different groups, the study further tested the team duplication of the merged papers of various groups. During the research process, it was found that the research papers within groups often exhibit similarity due to their association with multiple funding projects. Therefore, a principle of “if connected, then merged” was adopted among groups with highly similar research papers to ensure the heterogeneity of papers within the final merged research groups.

The generation process of the backbone-based research groups is illustrated in Fig. 1 below. Initially, university discipline backbones α, β, γ, θ, δ, and ε are each designated as core members within project-based or individual-based research teams A, B, C, D, E, and F, among which αβγ, γθ, θδ, δε ‘s core and backbone members’ Jaccard coefficient meet the merging standard and generate lines. After the first merging, the Jaccard coefficient of the papers of the αβγ, γθ, θδ, δε are calculated, and the lines are generated because of a high duplicated papers between γθ, θδ, and θδ, δε. Finally, αβγ and γθδε are retained based on the rule.

The α, β, γ, θ, δ, and ε are core members within project-based or individual-based research teams. The A, B, C, D, E, and F are project-based or individual-based research teams. From step 1 to step 2, research groups are merged according to the Jaccard coefficient between research team members. From step 2 to step 3, research groups are merged according to the Jaccard coefficient between research group papers.

In summary, the process of identifying a backbone-based research group involves the following steps: (1) Identify prolific authors within the university’s discipline by analyzing all papers published in the field, considering them as the discipline’s backbones members; (2) Merge the project-based and individual-based research teams wherein university discipline backbones are core member, thereby forming backbone-based research groups; (3) Merge the backbone-based research group identified in step (2) based on the Jaccard coefficient between their core and backbone members; (4) Calculate the Jaccard coefficient of the papers of the merged groups in step (3), merge the groups with significant paper overlap, and generate new backbone-based research groups.

The research groups identified through the above steps offer two advantages: Firstly, they integrate similar project-based and individual-based research teams, avoiding redundancy in team identification outcomes. Secondly, the same member may participate in different research teams, assuming distinct roles within each, thus better reflecting the complexity of scientific research practices.

Representative team: consolidation via backbone-based research group

When universities introduce their research groups to external parties, they typically highlight the most significant research members within the institution. Although the backbone-based research group has condensed the project-based and individual-based research teams, there may still be some overlap among members from different backbone-based research groups.

In order to create condensed and representative research groups that accurately reflect the development of the university’s discipline, this study extracts the core and backbone members identified in the backbone-based research group. It then identifies the representative group using the widely utilized Louvain algorithm (Blondel et al., 2008 ) commonly employed in research group identification. This algorithm facilitates the integration of important members from different backbone-based research groups while ensuring there is no redundancy among group members. The merging process is shown in Fig. 2 .

Each pass is made of two phases: one where modularity is optimized by allowing only local changes of communities, and one where the communities found are aggregated in order to build a new network of communities. The passes are repeated iteratively until no increase in modularity is possible.

Research team identification process and its pros and cons

Overall, the method of identifying university research teams proposed in this research encompasses four stages: Initially, research teams are categorized into project-based research teams and individual-based research teams based on information provided with research papers, distinguishing between those supported by funding projects and those not. Subsequently, the prolific authors of universities are identified to combine individual-based and project-based research teams, and backbone-based research groups are generated. Finally, representative research groups are established utilizing the Louvain algorithm and the interrelations among members within the backbone-based research groups. The entire process is depicted in Fig. 3 below.

Different university research teams are identified at different stage.

Each type of research team or group has its advantages and disadvantages, as shown in Table 2 below.

Validation of identification results

In order to verify the accuracy of the identification results, the method proposed by Boyack and Klavans ( 2014 ), which relies on citation analysis, is utilized. This method calculates the level of consistency regarding the main research areas of the core and backbone members, thereby verifying the validity of the identification method.

In the SCIVAL database, all research papers are clustered into relevant topic groups, providing insights into the research area of individual authors. By examining the research topic clusters of team papers in the SCIVAL database, the predominant research areas of prolific authors can be determined. Authors sharing common research areas within a university are regarded as constituting a research team. Given that authors often conduct research in various research areas, this study focuses solely on the top three research areas for each author.

As demonstrated in Table 3 below, for the prolific authors A, B, C, D, and E of the research team, their top three research areas collectively span five distinct fields. By calculating the highest value of the consistency among these research areas, it can be judged whether these researchers can be classified as members of the same research group. As depicted in Table 3 , the main research areas of all prolific authors include Research Area 3, indicating that this field is one of the three most important research areas for all prolific authors. This consistency validates that the main research areas of the five authors align, affirming their classification within the same research team.

Data collection and preprocessing

In order to present the distinct characteristics of various types of scientific research teams as intuitively as possible, this study focuses on the field of material science, with Tsinghua University and Nanyang Technological University selected for analysis. The selection of these two institutions is driven by several considerations: (1) both universities boast exceptional performance in the field of material science on a global scale, consistently ranking within the top 10 worldwide for numerous years; (2) The scientific research systems in the respective countries where these universities are situated differ significantly. China’s scientific research system operates under a government-led funding model, whereas Singapore’s system involves a multi-party funding approach with contributions from the government, enterprises, and societies. By examining universities from these distinct scientific research cultures, this study aims to validate the proposed methods and highlight disparities in the characteristics of their scientific research teams. (3) Material science is inherently interdisciplinary, with contributions from researchers across various domains. Although the selected papers focus on material science, they may also intersect with other disciplines. Therefore, investigating research teams in material science could somewhat represent the interdisciplinary research teams.

The data utilized in this study is sourced from the Clarivate Analytics database, which categorizes scientific research papers based on the subject classification catalogs. In order to ensure the consistency and reliability of scientific research paper identification, this study focuses on the papers published in the field of material science by the two selected universities between 2017 and 2021. Additionally, considering the duration of funded projects, papers associated with projects that have appeared in 2017–2021 within ten years (2011–2022) are also included for analysis to enhance the precision of identification. In order to ensure the affiliation of a research team with the respective universities, this study exclusively considers papers authored by the first author or the corresponding author affiliated with the university as the subject of analysis.

Throughout this process, it should be noted that the name problem in identifying scientific research. Abbreviations, orders, and other name-related information are cleaned and verified. Given that this study exports data utilizing the Author’s Full name and restricts it to specific universities and disciplines, the cleaning process targets the rectification of identification discrepancies arising from a minority of abbreviations and similar names. The specific cleaning procedures entail the following steps.

First, all occurrences of “-” are replaced with null values, and names are standardized by capitalization. Second, the Python dedupe module is employed to mitigate ambiguity in author names, facilitating the differentiation or unification of authors sharing the same surname, name, and initials. List and output all personnel names of each university in this discipline and observe in ascending order. Third, a comparison of names and abbreviations is conducted in reverse order, alongside their respective affiliations and replacements in the identification data. For example, names such as “LONG, W.H” “LONG, WEN, HUI” and “LONG, WENHUI” are uniformly replaced with “LONG, WENHUI.” Fourth, identify and compare similar names in both abbreviations and full forms and confirm whether they are consistent by scrutinizing their affiliations and collaborators. Names exhibiting consistency are replaced accordingly, while those lacking uniformity remain unchanged. For example, “LI, W.D” and “LI, WEIDE” lacking common affiliations and collaborators, are not considered the same person and thus remain distinct.

The publication of the two universities in the field of Materials Science and Engineering across two distinct time periods is shown in Table 4 below.

Based on the publication count of papers authored by the first author or corresponding author from both universities, Tsinghua University demonstrates a significantly higher publication output than Nanyang Technological University, indicating a substantial disparity between the two institutions.

Subsequent to data preprocessing, this study uses the Python tool to develop algorithms in accordance with the proposed principles, thereby facilitating the identification of research teams and groups.

This study has identified several research teams through the sorting and analysis of original data. In order to provide a comprehensive overview of the identification results, this study begins by outlining the characteristics of the identification results and then analyzes the research teams affiliated with both universities, focusing on three aspects: scale, structure, and output.

Identification results of university research teams

The results reveal that both Tsinghua University and Nanyang Technological University boast a considerable number of Pbrts, indicating that most of the researchers from both universities have received funding support. Additionally, a small number of teams have not received funding support, although their overall proportion is relatively low. The Bbrgs predominantly encompass the majority of the Ibrts and Pbrts, underscoring the significant influence of the discipline backbone members within both universities. Notably, the total count of Rrg across the two universities stands at 39, reflecting that many research groups are supporting the construction of material disciplines in the two universities (Table 5 ).

In order to validate the accuracy of the developed method, this study verifies the effectiveness of the identification algorithm. Given that the method emphasizes the main research area of its members, it is appropriate to apply it to the verification of the Bbrgs, which encompass the majority of the individual-based and project-based teams.

The analysis reveals that the consistency level of the most concentrated research area within the identified Bbrgs is 0.93. This signifies that within a Bbrg comprising 10 core or backbone members, a minimum of 9.3 individuals share the same main research area. Moreover, across Bbrgs of varying sizes, the average consistency level of the most concentrated research area also reached 0.90, indicating that the algorithm proposed in this study is valid (Table 6 ).

Analysis of the characteristics of Bbrg in universities

The findings of the analysis show that the Bbrgs encompass the vast majority of Pbrts and Ibrts within universities. Consequently, this study further analyzes the scale, structure, and output of the Bbrgs to present the characteristics of university research teams.

Group scale

Upon scrutinizing the distribution of Bbrgs across the two universities, it is observed that the number of core members is similar. Bbrg with a core member scale of 6–10 individuals are the most prevalent, followed by those with a scale of 0–5 members. Additionally, there are Bbrgs comprising 11–15 members, with relatively fewer Bbrgs consisting of 15 members or more. On average, the number of core members in Bbrgs stands at 7.08. Tsinghua University has more Bbrgs than Nanyang Technological University, while the average number of core members is relatively less. Notably, the proportion of core and backbone members amounts to nearly 12%, ranging from 11.22% to 13.88% (Table 7 ).

Group structure

The structural attributes of the research groups could be assessed through network density among core members, core and backbone members, and all team members. Additionally, departmental distribution can be depicted based on the identification of core members and their organizational affiliations. The formula for network density calculation is as follows:

Note : R is the number of relationships, and N is the number of members.

Overall, the network density characteristics exhibit consistency across both universities. Specifically, the network density among research group members tends to decrease as the group size expands. The network density among core members is the highest, while that among all members records the lowest. Comparatively, the average amount of various types of network density at Tsinghua University is relatively lower than that at Nanyang Technological University, indicating a lesser degree of connectivity among members within Tsinghua University’s research group. However, the network density levels among core members and core and backbone members of research teams in both institutions remain relatively high. Notably, the network density of backbone-based research groups exceeds 0.5, indicating a close collaboration among the core and backbone members of these university research groups (Table 8 ).

The T-test analysis reveals no significant difference in the network density among core members between Tsinghua University and Nanyang Technological University. This suggests that core members of research groups from universities with high-level discipline often maintain close communication. However, concerning the network density among core and backbone members and all members, the average amount of Tsinghua University’s research groups is significantly lower than those of Nanyang Technological University. This implies less direct collaboration among prolific authors at Tsinghua University, with backbone members relying more on different core members of the group to carry out research.

To present the cooperative relationship among the core and backbone members of the Bbrgs, the prolific authors associated with the backbone-based research groups are extracted. Subsequently, the representative research groups affiliated with Nanyang Technological University and Tsinghua University are identified using the fast-unfolding algorithm. The resultant collaboration network diagram among prolific authors is depicted in Fig. 4 , wherein each node color corresponds to different representative research groups of the respective universities.

Nodes (author) and links (relation between different authors) with the same color could be seen as the same representative research group.

The network connection diagram of Nanyang Technological University illustrates the presence of 39 Rrgs, including Rrgs from the School of Materials Science and Engineering and the Singapore Centre for 3D Printing. Owing to the inherently interdisciplinary characteristics of the materials discipline, its research groups are not only distributed in the School of Materials Science and Engineering; other academic units also have research groups engaged in materials science research.

Further insights into the distribution of research groups can be gleaned by examining the departments to which the primary members belong. Counting the departmental affiliations of the members with the highest centrality in each representative team reveals that, among the 39 Rrgs, the School of Materials Science and Engineering and the College of Engineering boast the highest number of affiliations, with nine core members of the research groups coming from these two departments, Following closely is the School of Physical and Mathematical Sciences. Notably, entities external to the university, such as the National Institute of Education and the Singapore Institute of Manufacturing Technology, also host important representative groups, underscoring the interdisciplinarity nature of material science. The distribution of Rrgs affiliations is delineated in Table 9 .

Similar to Nanyang Technological University, Tsinghua University also exhibits tightly woven connections within its backbone-based research group in Materials Science and Engineering, comprising a total of 39 Rrgs. Compared with Nanyang Technological University, Tsinghua University boasts a larger cohort of core and backbone members. The collaboration network diagram of representative groups is shown below (Fig. 5 ).

Similar to Nanyang Technological University, representative research groups at Tsinghua University are distributed in different schools within the institution, with the School of Materials being the directly related department. In addition, the School of Medicine and the Center for Brain-like Computing also conduct research related to materials science (Table 10 ).

By summarizing the departmental affiliations of the research groups, it becomes evident that the Rrgs in Materials Science and Engineering at these universities span various academic departments, reflecting the interdisciplinary characteristics of the field. The network density of the research groups is also calculated, with Nanyang Technological University exhibiting a higher density (0.028) compared to Tsinghua University (0.022), indicating tighter connections within the representative research groups at Nanyang Technological University.

Group output

In order to control the impact of scale, this study compares several metrics, including publication, publication per capita of core and backbone members, capita of the most prolific author within the groups, field-weighted citation impact, and citations per publication of Bbrgs at these two top universities.

Regarding publications, the average number and the T-test results show that Tsinghua University significantly outperforms Nanyang Technological University, suggesting that the Bbrgs and prolific authors affiliated with Tsinghua University are more productive in terms of research output.

However, in terms of field-weighted citation impact and citations per publication of the Bbrgs, the average number and the T-test results show that Tsinghua University is significantly lower than that of Nanyang Technological University, which indicates the research papers originating from the Bbrgs at Nanyang Technological University have a greater academic influence (see Table 11 ).

Typical cases

To intuitively present the research groups identified, this study has selected the two Bbrgs with the highest number of published papers at Tsinghua University and Nanyang Technological University for analysis, aiming to offer insights for constructing research teams.

Basic Information of the Bbrgs

Examining the basic information of the Bbrgs reveals that although Kang Feiyu’s group at Tsinghua University comprises fewer researchers than Liu Zheng’s group at Nanyang Technological University, Kang Feiyu’s group has a higher total number of published papers. In order to measure the performance of the research results of these two Bbrgs, the field-weighted citation impact of their research papers was queried using SCIVAL. The results showed that the field-weighted citation impact of Kang Feiyu’s group at Tsinghua University was higher, indicating a greater influence in the field of Materials Science and Engineering. Furthermore, the identity information of the two group leaders was compared. It was found that Kang Feiyu, in addition to being a professor at Tsinghua University, holds administrative positions as the dean of the Shenzhen Graduate School of Tsinghua University. Meanwhile, LIU, Zheng, mainly serves as the chairman of the Singapore Materials Society alongside his role as a professor (see Table 12 ).

Characteristics of team member network structure

In order to reflect the collaboration characteristics of research groups, this study calculates the network density of the two groups and utilizes VOSviewer to present the collaboration network diagrams of their members.

In terms of network density, both groups exhibit a density of 1 among core members, indicating that the collaboration between core members is tight. However, regarding the network density of core and backbone members, as well as all members, Liu Zheng’s group at Nanyang Technological University demonstrates a higher density. This indicates a stronger interconnectedness between the backbone and other members within the group (refer to Table 13 ).

For the co-authorship network diagram of group members, distinctive characteristics are observed between the two Bbrgs. In Kang Feiyu’s team, the core members exhibit prominence, with sub-team structures under evident each team member (Fig. 6 ). Conversely, while Liu Zheng’s team also features different core members, the centrality within each member is not obvious (Fig. 7 ).

Nodes (author) and links (relation between different authors) with the same color could be seen as the same sub-team.

Discussion and conclusion

Distinguishing different research teams constitutes the foundational stage in conducting team science research. In this study, we employ Price’s Law, Everett’s Rule, Jaccard Similarity Coefficient, and Louvain Algorithm to identify different research teams and groups in two world-leading universities specializing in Materials Science and Engineering. Through this exploration, we aim to explore the characteristics of research teams. The main findings are discussed as follows.

First, based on the co-authorship and project data from scholarly articles, this study develops a methodology for identifying research teams that distinguishes between different types of research teams or groups. In contrast to the prior identification method, our algorithms could identify different types of research teams and realize the member classification within research teams. This affords greater clarity regarding collaboration time and content among team members. The validation of identification results, conducted using the methodology proposed by Boyack and Klavans ( 2014 ), demonstrates the consistency of the main research areas among identified research group members. This validation shows the accuracy and efficacy of the research team identification methodology proposed in this study.

Second, universities have different types of research teams or groups, encompassing both project-based research teams and individual-based research teams lacking project support. Among these, most research teams rely on projects to conduct research (Bloch & Sørensen, 2015 ). Concurrently, this research finds that university research groups predominantly coalesce around eminent scholars, with backbone-based research groups comprising the majority of both project-based and individual-based research teams. This phenomenon shows the concentration of research resources within a select few research groups and institutions, a concept previously highlighted by Mongeon et al. ( 2016 ), who pointed out that research funding tends to be concentrated among a minority of researchers. In this research, we not only corroborate this assertion but also observe that researchers with abundant funding collaborate to form research groups, thereby mutually supporting each other. In addition, based on the structures of research groups at Nanyang Technological University and Tsinghua University, one could posit that these institutions resemble what might be termed a “rich club” (Ma et al., 2015 ). However, despite the heightened productivity of relatively concentrated research groups at Tsinghua University in terms of research output, their academic influence pales compared to that of Nanyang Technological University. To enhance research influence, it seems that the funding agency should curtail funding allocations to these “rich” research groups and instead allocate resources to support more financially challenged research teams. This approach would serve to alleviate the trend of concentration in research project funding, as suggested by Aagaard et al. ( 2020 ).

Thirdly, research groups in Material Science and Engineering exhibit obvious interdisciplinary characteristics. Despite all research papers being classified under the Material Science and Engineering discipline, the distribution of research groups across various academic departments suggests a pervasive interdisciplinary nature. This phenomenon underscores the interconnectedness of Materials Science and Engineering with other disciplines and serves as evidence that members from diverse departments within high-caliber universities actively engage in collaborative efforts. Previous research conducted in the United Kingdom has revealed that interdisciplinary researchers from arts and humanities, biology, economics, engineering and physics, medicine, environmental sciences, and astronomy occupy a pivotal position in academic collaboration and can obtain more funding (Sun et al., 2021 ). In this research, similar conclusions are also found in Material Science and Engineering.

Fourth, the personnel structure distribution in university research groups adheres to Price’s Law, wherein prolific authors are a small part of the group members, with approximately 20% of individuals contributing to 80% of the work. Backbone-based research groups, comprising predominantly project-based and individual-based research teams in universities, typically exhibit a core and backbone members ratio of approximately 10%–15%, aligning with Price’s Law. Peterson ( 2018 ) also pointed out that Price’s Law is almost universally present in all creative work. Scientific research relies more on innovative thinking and collaboration among researchers, and the phenomenon was first confirmed within university research groups. Besides, systematic research activities require many researchers to participate, but few people make important intellectual support and contributions. In practical research endeavors, principal researchers, such as professors and associate professors, often exhibit higher levels of innovation and stability, while graduate students and external support staff tend to be more transient, engaging in foundational research tasks.

Fifth, regarding the research group with the highest publication count of the two universities, Tsinghua University has more core members, highlighting the research model centered around a single scholar, while Nanyang Technological University exhibits a more dispersed distribution of researchers. This discrepancy may be attributed to differences in the university’s system. In China, valuable scientific research often unfolds under the leadership of authoritative scholars, typically holding multiple administrative roles, thus exhibiting hierarchical centralization within the group. This hierarchical structure aligns with Merton’s Sociology of Science ( 1973 ), positing that the higher the position of scientists, the higher their status in the hierarchy, facilitating increased funding acquisition and research impact. Conversely, Singapore’s research system is more like that of developed countries such as the UK and the US, fostering a more democratic culture where communication among members is more open. This relatively flat team culture is conducive to generating high-level research outcomes (Xu et al., 2022 ). However, concerning the field-weighted citation impact of research group papers, the Chinese backbone-based research group outperforms in both publication volume and academic influence, suggesting that this organizational characteristic is more suitable for China and is more conducive to doing research with stronger academic influence.

The research teams and groups in these top two universities offer insights for constructing science teams: Firstly, the university should prioritize individual-based research teams to enhance the academic influence of their research. Secondly, intra-university research teams should foster collaboration across different departments to promote interdisciplinary research, contributing to the advancement of the discipline. Thirdly, emphasis should be placed on supporting core and backbone members who often generate innovative ideas and contribute more to the academic community. Fourth, the research team should cultivate a suitable research atmosphere according to their cultural background, whether centralized or democratic, to harness researchers’ strengths effectively.

This research proposes a method for identifying university research teams and analyzing the characteristics of such teams at the top two universities. In the future, further exploration into the role of different team members and the development of more effective research team construction strategies are warranted.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. The data about the information of research papers authored by the two universities and the identification results of the members of university research teams are shared.

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School of Education, Central China Normal University, Wuhan, PR China

Zhe Cheng & Yihuan Zou

Faculty of Education, The Chinese University of Hong Kong, Hong Kong SAR, PR China

Yueyang Zheng

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Zhe Cheng contributed to the study conception, research design, data collection, and data analysis. Zhe Cheng wrote the first draft of the manuscript. Yihuan Zou made the last revisions. Yihuan Zou and Yueyang Zheng supervised, proofread, and commented on previous versions of this manuscript. All authors read and approved the final manuscript.

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Cheng, Z., Zou, Y. & Zheng, Y. A method for identifying different types of university research teams. Humanit Soc Sci Commun 11 , 523 (2024). https://doi.org/10.1057/s41599-024-03014-4

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Received : 03 August 2023

Accepted : 28 March 2024

Published : 18 April 2024

DOI : https://doi.org/10.1057/s41599-024-03014-4

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• Bad news: how the media reported on an observational study about cardiovascular outcomes of COVID-19
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• http://orcid.org/0000-0002-4166-5450 Camilla Alderighi 1 , 2 ,
• Raffaele Rasoini 1 , 2 ,
• Rebecca De Fiore 2 , 3 ,
• Fabio Ambrosino 2 , 3 ,
• Steven Woloshin 1 , 4
• 1 Lisa Schwartz Foundation for Truth in Medicine , Norwich , Vermont , USA
• 2 Alessandro Liberati Association - Cochrane Affiliate Centre , Potenza , Italy
• 3 Pensiero Scientifico Editore s.r.l , Roma , Italy
• 4 Center for Medicine and the Media, The Dartmouth Institute for Health Policy and Clinical Practice , Dartmouth University , Lebanon , New Hampshire , USA
• Correspondence to Dr Camilla Alderighi, Lisa Schwartz Foundation for Truth in Medicine, Norwich, Vermont, USA; camilla.alderighi{at}gmail.com

https://doi.org/10.1136/bmjebm-2023-112814

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• Cardiovascular Diseases
• PUBLIC HEALTH
• Cardiovascular Abnormalities

Medical research gets plenty of media attention. Unfortunately, the attention is often problematic, frequently failing to provide readers with information needed to understand findings or decide whether to believe them. 1 Unless journalists highlight study cautions and limitations, avoid spin 2 and overinterpretation of findings, the public may draw erroneous conclusions about the reliability and actionability of the research. Coverage of observational research may be especially challenging given inherent difficulty in inferring causation, a limitation that is rarely mentioned in medical journals articles or corresponding news. 3 We used news coverage of a retrospective cohort study, published in Nature Medicine in 2022, 4 as a case study to assess news reporting quality. The index study used national data from US Department of Veteran Affairs to characterise the post-acute cardiovascular manifestations of COVID-19. We chose this study because of its potential public health impact (ie, reporting increased cardiovascular diseases after even mild COVID-19 infection) and its enormous media attention: one of the highest Altmetric scores ever (>20 k, coverage in over 600 news outlets and 40 000 tweets). Our study supplements a previous analysis limited to Italian news. 5

Supplemental material

Using Altmetric news page, we collected the news stories released in the first month after index study publication. We excluded duplicate articles, articles where the index study was not the main topic, articles<150 words or with unreachable link, paywalled articles and articles aimed at healthcare professionals. We translated articles not in English or Italian into Italian using Google Translate. Four raters (two physicians and two scientific journalists) independently analysed the included news articles using the coding scheme in online supplemental appendix 1 . Outcome was the proportion of news articles failing to meet each of the quality measures. Inter-rater agreement across all items was substantial (Fleiss’ kappa=0.78). Coder disagreements were resolved through discussion.

Almost all news stories (95 of 96, 99%) failed to mention the causal inference limitation or used causal language (eg, “Covid causes substantial long-term cardiovascular risks.”). 69 of 96 (72%) made unsupported recommendations (eg, “Based on the results of this study, I recommend that everyone who has been infected with Covid-19 […] get a cardiovascular workup within 12 months.”). 62 of 88 (70%) employed spin, for example, by reporting only relative risks (eg, “Overall, for all cardiovascular diseases combined, the risk after Covid-19 infection increased by 55%.”). 84 of 96 (87%) employed fear mongering (eg, “The results of the paper have shocked other researchers.”). 75 of 96 (78%) failed to undertake a basic critical evaluation of the study (eg, mention population characteristics and study context). More quality measure details and examples from the news are given in table 1 .

• View inline

Quality measures investigated in the analysis and examples from the news

This case study highlights how uncritical reporting of observational research in the news can result in dissemination of poor-quality information to the public. In this case, a high-impact study described an increased incidence of cardiovascular diseases after COVID-19, including coronary disease, myocarditis, pericarditis, heart failure, dysrhythmias, cerebrovascular disease and thromboembolic disease. Because they were based on observational analyses of US Veterans cohorts, these findings should be interpreted cautiously. Nevertheless, many of the subsequent news reports used inappropriate causal language and made recommendations unsupported by the research.

In this analysis, we focused on issues about reporting, that is what people eventually read. However, upstream sources are part of the problem 8 : for instance, the quality of reporting in the case study press release 9 reflects what we have observed in the news (eg, from an investigator quoted in the press release: “Because of the chronic nature of these conditions, they will likely have long-lasting consequences for patients and health systems and also have broad implications on economic productivity and life expectancy”).

The Nature Medicine paper was timely and of great interest to a public concerned about the sequelae of COVID-19. Not surprisingly, it received extraordinary coverage in the media. Careful, balanced news coverage could have helped the public understand that there might be long-term harms of COVID-19. Unfortunately, instead, as documented in our analysis, most media tended to overstate the certainty of results, likely generating substantial public anxiety about an inevitable epidemic of post-COVID-19 cardiovascular disease, and that is bad news.

Our analysis has limitations, such as, being restricted to a single study, unpaywalled articles and using a subjective selection of quality measures—albeit consistent with minimum quality standards used to judge reporting on observational research. 6 7

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• Pérez Gaxiola G , et al
• Boutron I ,
• Bolland MJ ,
• Bowe B , et al
• Rasoini R ,
• Ambrosino F ,
• De Fiore R , et al
• von Elm E ,
• Altman DG ,
• Egger M , et al
• Schwitzer G
• Schwartz LM ,
• Woloshin S ,
• Andrews A , et al
• Nordemberg T

Supplementary materials

Supplementary data.

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• Data supplement 1
• Data supplement 2

X @camialderighi

Contributors All authors contributed to conception, planning, design and conduct; acquisition, analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; and administrative, technical or material support and had full access to all the data in the study. CA, FA, RDF and RR: contributed to statistical analysis and take responsibility for the integrity of the data and the accuracy of the data analysis. CA and RR contributed equally to the creation of this manuscript; the order of their authorship is entirely arbitrary. CA, RR and SW: contributed to supervision.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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