case study research approaches

The Ultimate Guide to Qualitative Research - Part 1: The Basics

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

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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23 Case Study Research: In-Depth Understanding in Context

Helen Simons, School of Education, University of Southampton

• Published: 02 September 2020
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This chapter explores case study as a major approach to research and evaluation. After first noting various contexts in which case studies are commonly used, the chapter focuses on case study research directly. Strengths and potential problematic issues are outlined, followed by key phases of the process. The chapter emphasizes how important it is to design the case, to collect and interpret data in ways that highlight the qualitative, to have an ethical practice that values multiple perspectives and political interests, and to report creatively to facilitate use in policymaking and practice. Finally, the chapter explores how to generalize from the single case. Concluding issues center on the need to think more imaginatively about design and the range of methods and forms of reporting required to persuade audiences to value qualitative ways of knowing in case study research.

Introduction

This chapter explores case study as a major approach to research and evaluation using primarily qualitative methods, as well as documentary sources, contemporaneous or historical. However, this is not the only way in which case study can be conceived. No one has a monopoly on the term. While sharing a focus on the singular in a particular context, case study has a wide variety of uses, not all associated with research. A case study, in common parlance, documents a particular situation or event in detail in a specific sociopolitical context. The particular can be a person, a classroom, an institution, a program, or a policy. In the sections that follow, I identify different ways in which case study is used before focusing directly on qualitative case study research. However, first I wish to indicate how I came to advocate and practice this form of research. Origins, context, and opportunity often shape the research processes we endorse. It is helpful for the reader, I think, to know how I came to the perspective I hold.

The Beginnings

I first came to appreciate and enjoy the virtues of case study research when I entered the field of curriculum evaluation and research in the 1970s. The dominant research paradigm for educational research at that time was experimental or quasi-experimental, cost–benefit, or systems analysis, and the dominant curriculum model was aims and objectives (House, 1993 ). The field was dominated, in effect, by a psychometric view of research in which quantitative methods were preeminent. But the innovative projects we were asked to evaluate (predominantly, but not exclusively, in the humanities) were not amenable to such methodologies. The projects were challenging to the status quo of institutions, involved people interpreting the policy and programs, were implemented differently in different contexts and regions, and had many unexpected effects.

We had no choice but to seek other ways to evaluate these complex programs, and case study was the methodology we found ourselves exploring to understand how the projects were being implemented, why they had positive effects in some regions of the country and not others, and what the outcomes meant in different sociopolitical and cultural contexts. What better way to do this than to talk with people to see how they interpreted the “new” curriculum; to watch how teachers and students put it into practice; to document transactions, outcomes, and unexpected consequences; and to interpret all in the specific context of the case (Simons, 1971 , 1987 , ch. 3). From this point on and in further studies, case study in educational research and evaluation came to be a major methodology for understanding complex educational and social programs. It also extended to other practice professions, such as nursing, health, and social care (Greenhalgh & Worrall, 1997 ; Shaw & Gould, 2001 ; Zucker, 2001 ). (For further details of the evolution of the case study approach and qualitative methodologies in evaluation, see Greene, 2000 ; House, 1993 , pp. 2–3; Simons, 2009 , pp. 14–18).

This was not exactly the beginning of case study, of course. It has a long history in many disciplines (Gomm, Hammersley, & Foster, 2004 ; Platt, 2007 ; Ragin, 1992 ; Simons, 1980 ), many aspects of which form part of case study practice to this day. But its evolution in the context just described was a major move in the contemporary evolution of the logic of evaluative inquiry (House, 1980 ). It also coincided with movement toward the qualitative in other disciplines, such as sociology and psychology. This was all part of what Denzin & Lincoln ( 1994 ) termed “a quiet methodological revolution” (p. ix) in qualitative inquiry that had been evolving over the past two decades.

There is a further reason why I continue to advocate and practice case study research and evaluation to this day, and that is my personal predilection for trying to understand and represent complexity, for puzzling through the ambiguities that exist in many contexts and programs, and for presenting and negotiating different values and interests in fair and just ways.

Put more simply, I like interacting with people, listening to their stories, trials and tribulations—giving them a voice in understanding the contexts and projects with which they are involved and finding ways to share these with a range of audiences. In other words, the move toward case study methodology suited my preference for how I learn—through observation of people, events and social interaction in particular sociopolitical contexts.

Concepts and Purposes of Case Study

Before exploring case study as it has come to be established in educational research and evaluation since the mid-sixties I wish to acknowledge other uses of case study. More often than not, these relate to purpose, and appropriately so in their different contexts, but many do not have a research intention. For a study to count as research, it would need to be a systematic investigation generating evidence that leads to “new” knowledge that is made public and open to scrutiny. There are many ways to conduct research stemming from different traditions and disciplines, but they all, in different ways, involve these characteristics.

Everyday Usage: Stories We Tell

The most familiar of these uses of case study is the everyday reference to a person, an anecdote or story illustrative of a particular incident, event, or experience of that person. It is often a short, reported account seen commonly in journalism but also in books exploring a phenomenon, such as recovery from serious accidents or tragedies where the author chooses to illustrate the story or argument with a “lived” example. The story is sometimes written by the author and sometimes by the person whose tale it is. “Let me share with you a story” is a phrase frequently heard.

The spirit behind this everyday usage and its power to connect can be seen in a report by Tim Adams of the London Olympics opening ceremony’s dramatization by Danny Boyle.

It was the point when we suddenly collectively wised up to the idea that what we are about to receive over the next two weeks was not only about “legacy collateral” and “targeted deliverables,” not about G4S failings and traffic lanes and branding opportunities, but about the second-by-second possibilities of human endeavour and spirit and communality, enacted in multiple places and all at the same time. Stories in other words (Adams, 2012 ).

This was a collective story, of course, not an individual one, but it does convey some of the major characteristics of case study—that richness of detail, time, place, multiple happenings, and experiences—that are also manifest in case study research, although carefully evidenced in the latter instance. We can see from this common usage how people have come to associate case study with story. I return to this thread in the reporting section.

Individual Cases in the Professions

In professional settings, in health and social care, case studies, often called case histories , are used to accurately record a person’s health or social care history and his or her current symptoms, experience, and treatment. These case histories include facts, as well as judgments and observations about the person’s reaction to situations or medication. Usually they are confidential. Not dissimilar is the detailed documentation of a case in law, often termed a case precedent when referred to in a court case to support an argument being made. However, in law there is a difference in that such case precedents are publicly documented, whereas in health and social care, confidentiality of the client is the prime concern.

Case Studies in Teaching

Exemplars of practice.

In education, but also in health and social care training contexts, case studies have long been used as exemplars of practice. These are brief descriptions with some detail of a person or project’s experience in an area of practice. Though frequently reported accounts, they are based on a person’s experience and sometimes on previous research.

Case Scenarios

Management studies are a further context in which case studies are often used. Here the case is more like a scenario outlining a particular problem situation for the management student to resolve. These scenarios may be based on research, but frequently are hypothetical situations used to raise issues for discussion and resolution. What distinguishes these case scenarios and the case exemplars in education from case study research is the intention to use them for teaching purposes.

Country Case Studies

Then there are case studies of programs, projects, and even countries, as in international development, where a whole-country study might be termed a case study or, in the context of the Organization for Economic Co-operation and Development, which examines the state of the art of a subject, such as education or environmental science in one or several countries. This may be a contemporaneous study and/or what transpired in a program over a period of time. Such studies often do have a research base, but frequently are reported accounts that do not detail the design, methodology, and analysis of the case as a research case study would do. Nor do they report in ways that give readers a vicarious experience, through observations, incidents, and voices of participants, of what it is like to live in the particular context of the case. Such case studies tend to be more knowledge and information focused than experiential.

Case Study as History

Closer to a research context is case study as history—what transpired at a certain time in a certain place. This is likely to be supported by documentary evidence but not primary data, unless it is an oral history (see Leavy, 2011 , for the evolution and practice of oral history as a research method). In education, in the late 1970s, Stenhouse ( 1978 ) experimented with a case study archive. Using contemporaneous data gathering, primarily through interviewing, he envisaged this database, which he termed a case record , forming an archive from which different individuals, at some later date, could write a case study . This approach uses case study as a documentary source to begin to generate a history of education, as indicated in the subtitle of Stenhouse’s 1978 paper, “Towards a Contemporary History of Education.”

Case Study Research

From here on, my focus is on case study research per se, adopting for this purpose the following definition: “Case study is an in-depth exploration from multiple perspectives of the complexity and uniqueness of a particular project, policy, institution or system in a “real-life” context. It is research based, inclusive of different methods and is evidence-led” (Simons, 2009 , p. 21). For further related definitions of case study, see Stake ( 1995 ), Merriam ( 1988 ), and Chadderton and Torrance ( 2011 ). For definitions from a slightly different perspective, see Yin ( 2004 ) and Thomas ( 2016 , p. 23).

Not Defined by Method or Perspective

The inclusion of different methods in the definition quoted above signals that case study research is not defined by methodology or method. What defines case study is its singularity and the concept and boundary of the case. It is theoretically possible to conduct a case study using primarily quantitative data if this is the best way of providing evidence to inform the issues the case is exploring. This may not happen often, and only perhaps in some disciplines like medicine, although even in that context, there is increasing recognition, particularly in clinical settings, that client-centered and context studies are important for diagnosis and treatment (Greenhalgh & Worrall, 1997 ). It is equally possible to conduct case study that is mainly qualitative, to engage people with the experience of the case or to provide a rich portrayal of a person (MacDonald, 1977 ) or an event, project, or program. While the focus of the case is usually a project, program, or policy, within the case there can be portrayals of individuals who are key actors. These are what I term case profiles . In some instances, these profiles, or even shorter cameos of individuals, may be quite prominent. For it is through the perceptions, interpretations, and interactions of people that we learn how policies and programs are enacted (Kushner, 2000 , p. 12). The program is still the main focus of analysis in such cases, but, in exploring how individuals play out their different roles in the program, we get closer to the actual experience and meaning of the program in practice.

In the past three decades the literature and associated courses and conferences on mixed methods in educational and social research has proliferated (Greene, Caracelli, & Graham, 1989 ); (Greene & Caracelli, 1997 ; Tashakkori & Teddlie, 1998, 2003). This development, which first became evident in the eighties, evolved partly to overcome the partisan focus of either quantitative or qualitative research, but it also provides a perspective from different methodologies that may add to understanding of the case and increases the options for learning from different ways of knowing. Mixed methods methodology is sometimes preferred by stakeholders who believe it provides a firmer basis for informing policy. This is not necessarily the case, but is beyond the scope of this chapter to explore. Case study research has always been open to the inclusion of different methods because what is paramount in case research is understanding the complexity and uniqueness of the case, and a variety of methods offer different angles to comprehending this complexity and uniqueness. For further discussion of the complexities of mixing methods and the virtue of using qualitative methods and case study in a mixed methods design, see Greene ( 2007 ). The focus for the remainder of this chapter will be on the qualitative dimension of case study research.

Case study research may also be conducted from different standpoints—realist, interpretivist, or constructivist, for example. My perspective falls within a constructivist, interpretivist framework. What interests me is how I and those in the case perceive and interpret what we find and how we construct or co-construct understandings of the case. This suits not only my predilection for how I see the world, but also my preferred phenomenological approach to interviewing and curiosity about people and how they act in social and professional life.

Qualitative Case Study Research

Qualitative case study research shares many characteristics with other forms of qualitative research, such as narrative, oral history, life history, ethnography, in-depth interview and observational studies that utilize qualitative methods. However, its focus, purpose, and origins, in educational research and evaluation at least, are a little different. The focus is clearly the study of the singular. The purpose is to portray an in-depth view of the quality and complexity of social/educational programs or policies as they are implemented in specific sociopolitical contexts. What makes it qualitative is its emphasis on subjective ways of knowing, particularly the experiential, practical, and presentational rather than the propositional (Heron, 1992 , 1999 ) to comprehend and communicate what transpired in the case.

Characteristic Features and Advantages

Case study research is not method dependent, as noted earlier, nor is it constrained by resources or time. Although it can be conducted over several years, which provides an opportunity to explore the process of change and explain how and why things happened, it can equally be carried out contemporaneously in a few days, weeks, or months. This flexibility is extremely useful in many contexts, particularly when a change in policy or unforeseen issues in the field require modifying the design.

Flexibility extends to reporting. The case can be written up in different lengths and forms to meet different audience needs and to maximize use (see the section on reporting). Using the natural language of participants and familiar methods (like interview, observation and oral history) also enables participants to engage in the research process, thereby contributing significantly to the generation of knowledge of the case. As I have indicated elsewhere (Simons, 2009 ), “This is both a political and epistemological point. It signals a potential shift in the power base of who controls knowledge and recognizes the importance of co-constructing perceived reality through the relationships and joint understandings we create in the field” (p. 23).

Possible Disadvantages

If one is an advocate, identifying advantages of a research approach is easier than pointing out its disadvantages, something detractors are quite keen to do anyway! But no approach is perfect, and here are some of the issues that often trouble people about case study research. The sample of one is an obvious issue that worries those convinced that only large samples can constitute valid research, especially if it is to inform policy. Understanding complexity in depth may not be a sufficient counterargument, and I suspect there is little point in trying to persuade otherwise. For frequently this perception is one of epistemological and methodological, if not ideological, preference.

However, there are some genuine concerns that many case researchers face: the difficulty of processing a mass of data; of “telling the truth” in contexts where people may be identifiable; personal involvement, when the researcher is the main instrument of data gathering; and writing reports that are data based, yet readable in style and length. But one issue that concerns advocates and nonadvocates alike is how inferences are drawn from the single case.

Answers to some of these issues are covered in the sections that follow. Whether they convince may again be a question of preference. However, it is worth noting here that I do not think we should seek to justify these concerns in terms identified by other methodologies. Many are intrinsic to the nature and strength of qualitative case study research.

Subjectivity, for instance, both of participants and of the researcher, is inevitable, as it is in many other qualitative methodologies. This is often the basis on which we act. Rather than seeing this as bias or something to counter, it is an intelligence that is essential to understanding and interpreting the experience of participants and stakeholders. Such subjectivity needs to be disciplined, of course, through procedures that examine the validity of individuals’ representations of “their truth” and demonstrate how the researcher took a reflexive approach to monitoring how his or her own values and predilections may have unduly influenced the data.

Types of Case Study

There are numerous types of case study, too many to categorize, I think, as there are overlaps between them. However, attempts have been made to do so and, for those who value typologies, I refer them to Bassey ( 1999 ) and, for a more extended typology, to Thomas ( 2011 ). A slightly different approach is taken by Gomm et al. ( 2004 ): noting, in an annotated bibliography, the different emphases in major texts on case study. What I prefer to do here is to highlight a few familiar types to focus the discussion that follows on the practice of case study research.

Stake ( 1995 ) offered a threefold distinction that is helpful when it comes to practice, he says, because it influences the methods we choose to gather data (p. 4). He distinguishes between an intrinsic case study , one that is studied to learn about the particular case itself, and an instrumental case study , in which we choose a case to gain insight into a particular issue (i.e., the case is instrumental to understanding something else; p. 3). The collective case study is what its name suggests: an extension of the instrumental to several cases.

Theory-led or theory-generated case study is similarly self-explanatory, the first starting from a specific theory that is tested through the case and the second constructing a theory through interpretation of data generated in the case. In other words, one ends rather than begins with a theory. In qualitative case study research, this is the more familiar route. The theory of the case becomes the argument or story you will tell.

Evaluation case study has three essential elements. Its purpose is to determine the value of a particular project, program or policy, to include and balance different interests and perspectives and to report findings to a range of stakeholders in ways that they can use. It is a social, political and ethical practice. It needs to be responsive to issues or questions identified by stakeholders, including those who commission evaluations, who often have different perspectives of the program and different interests in the expected outcomes. The task of the evaluator in such situations becomes one of negotiating and representing all interests and values in the program fairly and justly. This is an inherently political process and requires an ethical practice that offers participants some protection over the personal data they give as part of the research and agreed audiences access to the findings presented in ways they can understand. The ethical protocols that have evolved to support this process are outlined in the section on ethics.

Designing Case Study Research

Design issues in case study sometimes take second place to those of data gathering, the more exciting task, perhaps, in beginning research. However, it is critical to consider the design at the outset, even if changes are required in practice due to the reality of what is encountered in the field. In this sense, the design of case study is emergent, rather than preordinate (predetermined in advance), shaped and reshaped as understanding of the significance of foreshadowed issues emerges and other, perhaps more pertinent issues are discovered.

Before entering the field, there are a myriad of planning issues to think about related to stakeholders, participants, and audiences. These include whose values matter, whether to engage these groups in data gathering and interpretation, the style of reporting appropriate for each, and the ethical guidelines that will underpin data collection and reporting. However, here I emphasize only three: the broad focus of the study, what the case is a case of, and framing questions/issues. These steps are often ignored in an enthusiasm to gather data, resulting in a case study that claims to be research but lacks the basic principles required for generation of valid, public knowledge.

Conceptualize the Topic

First, it is important that the topic of the research is conceptualized in a way that it can be researched (i.e., it is not too wide). This seems an obvious point to make, but failure to think through precisely what it is about your research topic you wish to investigate will have a knock-on effect on the framing of the case, data gathering and interpretation and may lead, in some instances, to not gathering or analyzing data that actually inform the topic. Further conceptualization or reconceptualization may be necessary as the study proceeds, but it is critical to have a clear focus at the outset.

What Constitutes the Case

Second, it is important to decide what would constitute the case (i.e., what it is a case of) and where the boundaries lie. This often proves more difficult than first appears. And sometimes, partly because of the semifluid nature of the way the case evolves, it is only possible to finally establish what the case is a case of at the end. Nevertheless, it is useful to identify what the case and its boundaries are at the outset to help focus data collection while maintaining an awareness that they may shift. This is emergent design in action.

In deciding the boundary of the case, there are several factors to bear in mind. Is it bounded by an institution or a unit within an institution, by people within an institution, by region, or by project, program, or policy? If we take a school as an example, the case could be composed of the principal, teachers and students, or the boundary could be extended to the cleaners, the caretaker, or the receptionist, people who often know a great deal about the subnorms and culture of the institution.

If the case is a policy or particular parameter of a policy, the considerations may be slightly different. People will still be paramount—those who generated the policy and those who implemented it—but there is likely also to be a political culture surrounding the policy that had an influence on the way the policy evolved. Would this be part of the case? In evaluation case study it invariably would, because it is difficult to fully comprehend how a policy is interpreted and implemented without an understanding of the values and intentions behind the setting up of the policy in the first place.

Whatever boundary is chosen, it may change in the course of conducting the study when issues arise that can only be understood by going to another level. What transpires in a classroom, for example, if a classroom is the case, is often partly dependent on the support of the school leadership and culture of the institution and this, in turn, to some extent is dependent on what resources are allocated from the local education administration. Much like a series of Russian dolls, one context inside the other.

Unit of analysis

Thinking about what would constitute the unit of analysis—a classroom, an institution, a program, a region—may help in setting the boundaries of the case, and it will certainly facilitate analysis. But this is a slightly different issue from deciding what the case is a case of. Taking a health example, the case may be palliative care support, but the unit of analysis the palliative care ward. The focus would be directly on how palliative care was managed in the context of a particular ward or wards and the understanding this generated for palliative care support in general. Here, as in the school example, you would need to consider which of the many people who populate the ward form part of the case—is it the nurses, interns, or doctors only, or does it extend to patients, cleaners, nurse aides, and medical students? If you took palliative care support as the unit of analysis, you would be less concerned about the specific details of the ward. Your focus would be more on the broader policy, key strategies, and units supporting palliative care, as well as the perspective of key actors in the process and how they delivered such care.

Framing Questions and Issues

The third most important consideration is how to frame the study, and you are likely to do this once you have selected the site or sites for study. There are at least four approaches: specific research or evaluative questions, foreshadowed issues (Smith & Pohland, 1974 ), theoretical framework, or a program logic. To some extent, your choice will be dictated by the type of case you have chosen, as well as by your personal preference for how to conduct it—in either a structured or an open way.

Initial questions give structure; foreshadowed issues give more freedom to explore. In qualitative case study, foreshadowed issues are more common, allowing scope for issues to change as the study evolves, guided by participants’ perspectives and events in the field. With this perspective, it is more likely that you will generate a theory of the case toward the end, through your interpretation and analysis, rather than start with a preexisting theoretical framework. See Thomas ( 2016 , ch. 11) for an exploration of different ways to generate theory in and of your case.

If you are conducting an instrumental case study , staying close to the questions or foreshadowed issues is necessary to be sure you gain data that will illuminate the central focus of the study. This is critical if you are exploring issues across several cases, although it is possible also to do a cross-case analysis from cases that have each followed a different route to discovering significant issues.

Opting to start with a theoretical framework provides a basis for formulating questions or identifying issues, but it can also constrain the study to only those questions/issues that fit the framework. The same is true with using program logic to frame the case. This approach is frequently adopted in evaluation case study, where the evaluator, individually or with stakeholders, examines how the aims and objectives of the program relate to the activities designed to promote it and the outcomes and impacts expected. It provides direction and is useful for engaging stakeholders in thinking through the assumptions underlying any theory of change they propose. However, it can lead to simply confirming what was anticipated, rather than documenting what transpired in the case (see Rogers, 2017 ; and Funnell & Rogers, 2011 , for helpful accounts of the potential and pitfalls of adopting a logic model as a framework).

Whichever approach you choose to frame the case, it is useful to think about the rationale or theory for each question or aspect of the framing and what methods would best enable you to gain an understanding of them. This will not only start a reflexive process of examining your choices—an important aspect of the process of data gathering and interpretation—but also aid analysis and interpretation further down the track.

Methodology and Methods

Qualitative case study research, as already noted, appeals to subjective ways of knowing and to a primarily qualitative methodology that captures experiential understanding (Stake, 2010 , pp. 56–70). It follows that the main methods of data gathering to access this way of knowing will be qualitative. Interviewing, observation, and document analysis are the primary three, often supported by critical incidents, focus groups, cameos, vignettes, diaries/journals, and photographs. Before gathering any primary data, however, it is useful to search relevant existing sources (written or visual) to learn about the antecedents and context of a project, program, or policy as a backdrop to the case. This can sharpen framing questions, avoid unnecessary data gathering, and shorten the time needed in the field.

Given that there are excellent texts on qualitative methods (see, for example, Denzin & Lincoln, 1994 ; Seale, 1999 ; Silverman, 2000 , 2004 ; Stake, 2010 ), I will not discuss all potential relevant methods here, but simply focus on the qualities of the primary methods that are particularly appropriate for case study research.

Primary Qualitative Data Gathering Methods

Interviewing.

The most effective style of interviewing in qualitative case study research is the unstructured interview, in which active listening and open questioning are paramount, whatever prequestions or foreshadowed issues have been identified. Specific advantages of this approach to gaining in-depth data are the opportunity to document multiple perspectives and experiences and establish which issues are most significant in the case—an important step in refining the emergent design. This form of interviewing can include photographs—a useful starting point with certain cultural groups and the less articulate, to encourage them to tell their story through connecting or identifying with something in the image. The flexibility of unstructured interviewing has three further advantages for understanding participants’ experiences. First, through questioning, probing, listening, and, above all, paying attention to the silences and what they mean, you can get closer to the meaning of participants’ experiences. It is not always what they say. For thoughtful observations of the meaning of silences in qualitative research, see Mazzei ( 2003 , 2007 ).Second, unstructured interviewing is useful for engaging participants in the process of research. Instead of starting with questions and issues, invite participants to tell their stories or reflect on specific issues, to conduct their own self-evaluative interview, in fact. Not only will they contribute their particular perspective to the case, they will also learn about themselves, thereby making the process of research educative for them as well as for audiences of the research. Third, the open-endedness of this style of interviewing has the potential for creating a dialogue between participants and the researcher and between the researcher and the public, if enough of the dialogue is retained in the publication (Bellah, Madsen, Sullivan, Swidler, & Tipton, 1985 ).

Observations

Observations in case study research are likely to be close-up descriptions of events, activities, and incidents that detail what happens in a particular context. These will record time, place, specific incidents, transactions, dialogue, and note characteristics of the setting and of people within it without preconceived categories or judgment. No description is devoid of some judgment in selection, but, on the whole, the intent is to describe the scene or event as it is, providing a rich, textured description to give readers a sense of what it was like to be there or provide a basis for later interpretation.

Take the following excerpt from a study of the West Bromwich Operatic Society. It is the first night of a new production, The Producers , by this amateur operatic society. This brief excerpt is from a much longer observation of the overture to the first evening’s performance, detailing exactly what the production is, where it is, and why there is such a tremendous sense of atmosphere and expectation surrounding the event. Space prevents including the whole observation, but I hope you can get a glimmer of the passion and excitement that precedes the performance:

Birmingham, late November, 2011, early evening.… Bars and restaurants spruce up for the evening’s trade. There is a chill in the air but the party season is just starting … A few hundred yards away, past streaming traffic on Suffolk Street, Queensway, an audience is gathering at the New Alexandra Theatre. The foyer windows shine in the orange sodium night. Above each one is the rubric: WORLD CLASS THEATRE. Inside the preparatory rituals are being observed; sweets chosen, interval drinks ordered and programmes bought. People swap news and titbits about the production … The bubble of anticipation grows as the 5-minute warning sounds. People make their way to the auditorium. There have been so many nights like this in the past 110 years since a man named William Coutts invested £10,000 to build this palace of dreams.… So many fantasies have been played under this arch: melodramas and pantomimes, musicals and variety.… So many audiences, settling down in their tip-up seats, wanting to be transported away from work, from ordinariness and private troubles … The dimming lights act like a mother’s hush. You could touch the silence. Boinnng! A spongy thump on a bass drum, and the horns pipe up that catchy, irrepressible, tasteless tune and already you’re singing under your breath, “Springtime for Hitler and Germany …” The orchestra is out of sight in the pit. There’s just the velvet curtain to watch as your fingers tap along. What’s waiting behind? Then it starts it to move. Opening night … It’s opening night! (Matarasso, 2012 , pp. 1–2)

For another and different example—a narrative observation of an everyday but unique incident that details date, time, place, and experience—see Simons ( 2009 , p. 60).

Such naturalistic observations are also useful in contexts where we cannot understand what is going on through interviewing alone or in cultures with which we are less familiar and where key actors may not share our language or have difficulty expressing what they mean. Careful description in these situations can help identify key issues, discover the norms and values that exist in the culture, and, if sufficiently detailed, allow others to cross-corroborate what significance we draw from these observations. This last point is very important to avoid the danger in observation of ascribing motivations to people and meanings to transactions.

Finally, naturalistic observations are very important in highly politicized environments, often the case in commissioned evaluation case study, where individuals in interview may try to elude the “truth” or press upon you that their view is the right view of the situation. In these contexts, naturalistic observations not only enable you to document interactions as you perceive them, but also provide a cross-check on the veracity of information obtained in interviews.

Document Analysis

Analysis of documents, as already intimated, is useful for establishing what historical antecedents might exist to provide a springboard for contemporaneous data gathering. In most cases, existing documents are also extremely pertinent for understanding the policy context.

In a national policy case study I conducted on a major curriculum change, the importance of preexisting documentation was brought home to me sharply when certain documentation initially proved elusive to obtain. It was difficult to believe that it did not exist, because the evolution of the innovation involved several parties who had not worked together before and they needed to develop a shared understanding of the ‘new’ curriculum. There was bound, I thought, to be minuted meetings sharing progress and documentation of the “new” curriculum. In the absence of some crucial documents, I began to piece together the story through interviewing different individuals who had a role to play in the evolution of the new curriculum. But there were gaps, and certain issues did not make sense.

It was only when I presented two versions of what I discerned had transpired in the development of this initiative in an interim report 18 months into the study that things started to change. Subsequent to the meeting at which the report was presented, the “missing” documents started to appear. Suddenly found! What lay behind the “missing” documents, something I suspected from what certain individuals did and did not say in interview, was a major difference of view about how the innovation evolved, who was key in the process, and whose voice was more important in the context: political differences, in other words, that some stakeholders were trying to keep from me. The emergence of the documents enabled me to finally produce an accurate and fair account.

This is an example of the importance of having access to all relevant documents relating to a program or policy to study it fairly. The other major way in which document analysis is useful in case study is for understanding the values, explicit and hidden, in policy and program documents and in the organization where the program or policy is implemented. Not to be ignored as documents are photographs; these, too, can form the basis of a cultural and value analysis of an organization (Prosser, 2000 ).

Creative Artistic Approaches

Increasingly, some case study researchers are employing creative approaches associated with the arts as a means of data gathering and analysis. Artistic approaches have often been used in representing findings, but less frequently in data gathering and interpretation (Simons & McCormack, 2007 ). A major exception is the work of Richardson ( 1994 ), who views the very process of writing as an interpretative act, and that of Cancienne and Snowber ( 2003 ), who argue for movement as method.

The most familiar of these creative and artistic forms are written—narratives and short stories (Clandinin & Connelly, 2000 ; Richardson, 1994 ; Sparkes, 2002 ), poems or poetic form (Butler-Kisber, 2010 ; Duke, 2007 ; Richardson, 1997; Sparkes & Douglas, 2007 ), and cameos of people, or vignettes of situations. These can be written by participants or by the researcher or developed in partnership. They can also be shared with participants to further the interpretation of the data.

Photographs also have a long history in qualitative research for presenting and constructing understanding (Butler-Kisber, 2010 ; Collier, 1967 ; Prosser, 2000 ; Rugang, 2006 ; Walker, 1993 ). The photo story in particular—a selection of photographs placed in sequence to show the interpretation of an event or circumstance—is a powerful way of telling. Less common are other visual forms of gathering data, such as “draw and write” (Sewell, 2011 ), artifacts, drawings, sketches, paintings, and collages, although these, too, are increasingly being adopted. For examples of the use of collage in data gathering, see Duke ( 2007 ) and Butler-Kisber ( 2010 ), and for charcoal drawing, see Elliott ( 2008 ). Collages have the potential not only for revealing inner states and feelings, but also for documenting conflicts and tensions in a case. Duke ( 2007 ) made effective use of collage in this respect to portray differences and tensions with doctors in a medical setting where she, in her role as a nurse consultant, was conducting research as well as performing her normal nurse duties. The collage served to channel the emotions she was experiencing in this hierarchical context without influencing the research or her professional role. More recently, Plakoyiannaki & Stavraki ( 2018 ) explored the various ways in which collages can be interpreted to reveal the meaning embedded in the juxtaposition of images and visual metaphors in a collage. They also offer a heuristic analytic approach to counter what they see as limitations in some of the other forms of analyzing collages. Though written primarily for an audience in management research, many aspects of their paper are pertinent for case study research.

Videos can be a useful means of documenting events and interactions between people, especially when individuals cannot be interviewed. See, for example, Flewitt ( 2005 ) for a discussion of the value of video for exploring communications between young children in the home and preschool contexts. In other contexts—videos of classroom events, for example—they can be extremely useful for engaging participants and stakeholders in the interpretation of such events. It is often suggested, furthermore, that videos are a useful means of reporting case study data. Not, I suggest, in raw form. Beyond the ethical issue of the potential identification of individuals is the difficulty of understanding what is going on if you were not present at the time and had a grasp of other data relevant to that understanding. In other words, videos have a temporary life. Without additional data, the distant viewer may not comprehend. This is a separate issue from preparing a video report, composed of different kinds of data to tell the story of the case in a visual, succinct way. Such videos have the power to engage different audiences and can facilitate immediate understanding of the critical issues in the case. An excellent example of this is the CD that Jenny Elliot ( 2008 ) prepared as part of her Ph.D. thesis, showing how it was possible through the research she conducted to get a unit of brain-damaged men to dance. The video was widely shown subsequently in many healthcare contexts.

In qualitative inquiry broadly, these creative approaches are now quite common. And in the context of arts and health (see, for example, Frank, 1997 ; Liamputtong & Rumbold, 2008 ; Spouse, 2000 ), they are frequently used to illuminate perspectives of individuals in therapeutic settings or enhance understanding of how spaces and environments in health and social care affect those who inhabit them (Fenner, 2011 , 2017 ). However, in case study research to date, narrative forms have tended to dominate, possibly because the contexts in which much case study research is conducted are policy or program focused where narrative forms of understanding are more the norm. This is not to say creative approaches may not be useful in these contexts. It may be a question of lack of familiarity with such approaches and acceptance of their usefulness in those environments.

Finally, for capturing the quality and essence of peoples’ experience, nothing could be more revealing than a recording of their voices. Video diaries—self-evaluative portrayals by individuals of their perspectives, feelings, or experience of an event or situation—are a most potent way both of gaining understanding and of communicating that to others. It is rather more difficult to gain access for observational videos because it is hard to effectively disguise individuals. Even if consent is granted, where individuals are visible it is not possible to foresee how portrayals of their life and experience will be viewed years after the research is completed. Research is context and time bound. So, video diaries may be most useful in a temporal sense to facilitate understanding of the case. See Simons ( 2007 ) for an exploration of the ethical dimension of the use of visual data.

It will be evident from the foregoing discussion of qualitative methods that close-up portrayals of individuals and contexts requires sensitive ethical protocols. Negotiating what information becomes public can be quite difficult in singular settings where people are identifiable and intricate or problematic transactions have been documented. The consequences that ensue from making knowledge public that hitherto was private may be considerable for those in the case. It may also be difficult to portray some of the contextual detail that would enhance understanding for readers because it would raise the risk of identifiability of individuals, as would visual data, as already noted.

The ethical stance that underpins the case study research and evaluation I conduct stems from a theory of ethics that emphasizes the centrality of relationships in the specific context (see Kirkhart, 2013 , for the concept of relational validity that supports this focus) and the consequences for individuals, while remaining aware of the research imperative to publicly report. It is essentially an independent democratic process based on the concepts of fairness and justice, in which confidentiality, negotiation, and accessibility are key principles (MacDonald, 1976 ; Simons, 2009 , ch. 6; and Simons, 2010 ). The principles are translated into specific procedures to guide the collection, validation, and dissemination of data in the field. These include:

engaging participants and stakeholders in identifying issues to explore and sometimes also in interpreting the data;

documenting how different people interpret and value the program;

negotiating what data become public, respecting both the individual’s “right to privacy” and the public’s “right to know”;

offering participants opportunities to check how their data are used in the context of reporting;

reporting in language and forms accessible to a wide range of audiences; and

disseminating to audiences within and beyond the case.

For further discussion of the ethics of democratic case study evaluation and examples of their use in practice, see Simons ( 2000 , 2006 , 2009 , ch. 6, 2010 ).

Getting It All Together

Case study is so often associated with story or with a report of some event or program that it is easy to forget that much analysis and interpretation has gone on before we reach this point. In many case study reports, this process is hidden, leaving the reader with little evidence on which to assess the validity of the findings and having to trust the one who wrote the tale.

This section briefly outlines possibilities, first, for analyzing and interpreting data, and second, for how to communicate the findings to others. However, it is useful to think of them together and indeed, at the start, because decisions about how you report may influence how you choose to make sense of the data. Your choice may also vary according to the context of the study—what is expected or acceptable—and your personal predilections, whether you prefer a more rational than intuitive mode of analysis, for example, or a formal or informal style of writing up that includes images, metaphor, narratives, or poetic forms.

Analyzing and Interpreting Data

When it comes to making sense of data, I make a distinction between analysis—a formal inductive process that seeks to explain—and interpretation, a more intuitive process that gains understanding and insight from an holistic grasp of data, although they may interact and overlap at different stages.

The process, whichever emphasis you choose, is one of reducing or transforming a large amount of data to themes that can encapsulate the overarching meaning in the data. This involves sorting, refining, and refocusing data until they make sense. It starts at the beginning with preliminary hunches, sometimes called interpretative asides or working hypotheses , later moving to themes, analytic propositions, or a theory of the case.

There are many ways to conduct this process. Two strategies often employed are concept mapping —a means of representing data visually to explore links between related concepts—and progressive focusing (Parlett & Hamilton, 1976 ), the gradual reframing of initially identified issues into themes that are then further interpreted to generate findings. Each of these strategies tends to have three stages: initial sense making, identification of themes, and examination of patterns and relationships between them.

If taking a formal analytic approach to the task, the data would likely be broken down into segments or data sets (coded and categorized) and then reordered and explored for themes, patterns, and possible propositions. If adopting a more intuitive process, you might focus on identifying insights through metaphors and images, lateral thinking, or puzzling over paradoxes and ambiguities in the data, after first immersing yourself in the total data set and reading and rereading interview scripts, observations, and field notes to get a sense of the whole. Trying different forms of making sense through poetry, vignettes, cameos, narratives, collages, and drawing are further creative ways to interpret data, as are photographs taken in the case arranged to explain or tell the story of the case.

Reporting Case Study Research

Narrative structure and story.

As indicated in the introduction, telling a story is often associated with case study and some think this is what a case study is. In one sense it is, and, given that story is the natural way in which we learn (Okri, 1997 ), it is a useful framework both for gathering data and for communicating case study findings. Not any story will do, however. To count as research, it must be authentic, grounded in data, interpreted and analyzed to convey the meaning of the case.

There are several senses in which story is appropriate in qualitative case study: in capturing stories participants tell, in generating a narrative structure that makes sense of the case (i.e., the story you will tell), and in deciding how you communicate this narrative (i.e., in story form). If you choose a written story form, Harrington ( 2003 ) and Caulley ( 2008 ) are useful authors to consult to ensure the story is clearly structured, well written, and contains only the detail that is necessary to give readers the vicarious experience of what it was like in the case. Harrington ( 2003 ) reminds us, among other things, that it is not only in the technical sense that good writing is required—using plain, precise, direct language and grammar—but also how we convey meaning—“‘selecting telling details’ … ‘balancing the particular and the universal’ … ‘structuring stories so insight emerges’” (p. 97). If the story is to be communicated in other ways, through, for example, audio or videotape or computer or personal interaction, the same applies, substituting visual and interpersonal skill for written. In addition to these authors, I often get inspiration for constructing a story or a portrayal of a person from novelists who write well.

Matching Forms of Reporting to Audience

The art of reporting is strongly connected to usability, so forms of reporting need to connect to the audiences we hope to inform: how they learn, what kind of evidence they value, and what kind of reporting maximizes the chances they will use the findings to promote policies and programs in the interests of beneficiaries. As Okri ( 1997 ) further reminds us “The writer only does half the work, the reader does the other” (p. 41).

There may be other considerations as well: How open are commissioners to receiving stories of difficulties as well as success stories? What might they need to hear beyond what is sought in the technical brief? And through what style of reporting would you try to persuade them? If you are conducting noncommissioned case study research, the scope for different forms of reporting is wider. In academia, for instance, many institutions these days accept creative and artistic forms of reporting when supported by supervisors and appreciated by examiners.

Styles of Reporting

The most obvious form of reporting is linear , often starting with a short executive summary and a brief description of focus and context, followed by methodology, the case study itself in its totality, or demonstrated in the thematic analysis, findings, and conclusions or implications. Conclusion-led reporting is similar in terms of its formality, but simply starts the other way around. From the conclusions drawn from the analyzed data, it works backward to tell the story through narrative, verbatim, and observational data of how these conclusions were reached. Both have a strong storyline. The intent is analytic and explanatory.

Quite a different approach is to engage the reader in the experience and veracity of the case. Rather like constructing a portrait or editing a documentary film, this involves the sifting, constructing, and reordering of frames, events, and episodes to tell a coherent story primarily through interview excerpts, observations, vignettes, and critical incidents that depict what transpired in the case. Interpretation is indirect through the weaving of the data. The story can start at any point, provided the underlying narrative structure is maintained to establish coherence (House, 1980 , p. 116).

Different again, and from the other end of a continuum, is a highly interpretative account that may use similar ways of presenting data but weaves a story from the outset that is highly interpretative. Engaging metaphor, images, short stories, contradictions, paradoxes, and puzzles, it is invariably interesting to read and can be most persuasive. However, the evidence is less visible and therefore less open to alternative interpretations.

Even more persuasive is a case study that uses artistic forms to communicate the story of the case. Paintings, poetic form, drawings, photography, collage, and movement can all be adopted to report findings, whether the data were acquired using these forms or by other means. The arts-based inquiry movement (Mullen & Finley, 2003 ) has contributed hugely to the validation and legitimation of artistic and creative ways of representing qualitative research findings. The journal Qualitative Inquiry contains many good examples, but see also Liamputtong & Rumbold ( 2008 ). Such artistic forms of representation may not be for everyone or appropriate in some contexts, but they do have the power to engage an audience and the potential to facilitate use.

Before leaving reporting, it is important to mention that in recent years, not surprisingly given the rapid growth and ever-changing technology at our disposal, there has been an increase in the use of data visualization techniques, both to present data and to report findings. See, for example, some of the excellent ideas offered by Stephanie Evergreen ( 2013 , 2016 ) using graphics and charts of different kinds to summarize data effectively. Telling the story of the case, then, can be visual as well as literary. Using these techniques, linked often with quotations from interviews and pictorial evidence of context, it is possible to communicate the findings of a case in a few pages, or even just one page. This can be of immense benefit to policy makers who may have little time to read long case reports or those who value visual learning as much as written. Such techniques are unlikely to replace the narrative form. Given the importance of people and context in case study, the need to represent participants’ voices and the sociopolitical context will invariably demand a longer and integrated story. Data visualization is an added strength and an option for those who are persuaded by visual means or who have little time.

Generalization in Case Study Research

One of the potential limitations of case study often proposed is that it is impossible to generalize. This is not so. However, the way in which one generalizes from a case is different from that adopted in traditional forms of social science research that utilize large samples (randomly selected) and statistical procedures and that assume regularities in the social world that allow cause and effect to be determined. In this form of research, inferences from data are stated as formal propositions that apply to all in the target population. See Donmoyer ( 1990 ) for an argument on the restricted nature of this form of generalization when considering single-case studies.

Making inferences from cases with a qualitative data set arises more from a process of interpretation in context, appealing to tacit and situated understanding for acceptance of their validity. Such inferences are possible where the context and experience of the case is richly described so the reader can recognize and connect with the events and experiences portrayed. There are two ways to examine how to reach these generalized understandings. One is to generalize from the case to other cases of a similar or dissimilar nature. The other is to see what we learn in depth from the uniqueness of the single case itself.

Generalizing from the Single Case

A common approach to generalization and one most akin to a propositional form is cross-case generalization. In a collective or multisite case study, each case is explored to see if issues that arise in one case also exist in other cases and what interconnecting themes exist between them. This kind of generalization has a degree of abstraction and potential for theorizing and is often welcomed by commissioners of research concerned that findings from the single case do not provide an adequate or “safe” basis for policy determination.

However, there are four additional ways to generalize from the single case, all of which draw more on tacit knowledge and recognition of context, although in different ways. In naturalistic generalization , first proposed by Stake ( 1978 ), generalization is reached on the basis of recognition of similarities and differences to cases with which we are familiar. To enable such recognition, the case should feature rich description; people’s voices; and enough detail of time, place, and context to provide a vicarious experience to help readers discern what is similar and dissimilar to their own context (Stake, 1978 ).

Situated generalization (Simons, Kushner, Jones, & James, 2003 ) is close to the concept of naturalistic generalization in relying for its generality on retaining a connectedness with the context in which it first evolved. However, it has an extra dimension in a practice context. This notion of generalization was identified in an evaluation of a research project that engaged teachers in and with research. Here, in addition to the usual validity criteria to establish the methodological warrant for the findings, the generalization was seen as dependable if trust existed between those who conducted the research (teachers, in this example) and those thinking about using it (other teachers). In other words, beyond the technical validity of the research, teachers considered using the findings in their own practice because they had confidence in those who generated them. This is a useful way to think about generalization if we wish research findings to improve professional practice.

The next two concepts of generalization— concept and process generalization —relate more to what you discover in making sense of the case. As you interpret and analyze, you begin to generate a theory of the case that makes sense of the whole. Concepts may be identified that make sense in the one case but have equal significance in other cases of a similar kind, even if the contexts are different. It is the concept that generalizes, not the specific content or context. This may be similar to the process Donmoyer ( 2008 ) identifies of “intellectual generalization” (as cited in Butler-Kisber, 2010 , p. 15) to indicate the cognitive understanding one can gain from qualitative accounts even if settings are quite different.

The same is true for generalization of a process. It is possible to identify a significant process in one case (or several cases) that is transferable to other contexts, irrespective of the precise content and contexts of those other cases. An example here is the collaborative model for sustainable school self-evaluation I identified in researching school self-evaluation in a number of schools and countries (Simons, 2002 ). Schools that successfully sustained school self-evaluation had an infrastructure that was collaborative at all stages of the evaluation process from design to conduct of the study, to analyzing and interpreting the results, and to reporting the findings. This ensured that the whole school was involved and that results were discussed and built into the ongoing development of school policies and practice. In other cases, different processes may be discovered that have applicability in a range of contexts. As with concept generalization, it is the process that generalizes not the substantive content or specific context.

Particularization

The forms of generalization discussed above are useful when we have to justify case study in a research or policy context. But the overarching justification for how we learn from case study is particularization —a rich portrayal of insights and understandings interpreted in the particular context. Several authors have made this point (Flyvberg, 2006 ; Simons, 2009 ; Stake, 1995 , 2006 ). Stake (2005) puts it most sharply when he observes that “the real business of case study is particularization, not generalization” (p. 8), referring here to the main reason for studying the singular, which is to understand the uniqueness of the case itself.

My perspective (explored further in Simons, 1996 , 2009 , p. 239; Simons & McCormack, 2007 ) is similar in that I believe the “real” strength of case study lies in the insights we gain from in-depth study of the particular. But I also argue for the universality of such insights—if we get it “right,” by which I mean that if we are able to capture and report the uniqueness, the essence of the case, in all its particularity and present it in a way we can all recognize, we will discover something of universal significance. This is something of a paradox. The more you learn in depth about the particularity of one person, situation, or context, the more likely you are to discover something universal. This process of reaching understanding has support both from the way in which many discoveries are made in science and in how we learn from artists, poets, and novelists, who reach us by communicating a recognizable truth about individuals, human relationships, and/or social contexts.

This concept of particularization is far from new, as the quotation below from a preface to a book written in 1908 attests. Stephen Reynolds, the author of A Poor Man’s House (Reynolds, 1908 ) noted in the preface that the substance of the book was first recorded in a journal, kept for purposes of fiction and in letters to one of his friends, but fiction proved an inappropriate medium. He felt that the life and the people were so much better than anything he could invent. The book therefore consists of the journal and letters drawn together to present a picture of a typical poor man’s house and life, much as we might draw together a range of data to present a case study. It is not the substance of the book that concerns us here, but the methodological relevance to case study research. Reynolds pointed out that the conclusions in his book were tentative and possibly went beyond this man’s life, so he thought some explanation of the way he arrived at them was needed:

Educated people usually deal with the poor man’s life deductively; they reason from the general to the particular; and, starting with a theory, religious, philanthropic, political, or what not, they seek, and too easily find, among the millions of poor, specimens—very frequently abnormal—to illustrate their theories. With anything but human beings, that is an excellent method. Human beings, unfortunately, have individualities. They do what, theoretically, they ought not to do, and leave undone those things they ought to do. They are even said to possess souls—untrustworthy things beyond the reach of sociologists. The inductive method—reasoning from the particular to the general … should at least help to counterbalance the psychological superficiality of the deductive method. (Reynolds, 1908 , preface) 1

Slightly overstated, perhaps, but the point is well made. In our search for general laws, we not only lose sight of the uniqueness and humanity of individuals, but also reduce them in the process, failing to present their experience in any “real” sense. What is astonishing about the quotation is that it was written over a century ago, and yet many still argue that you cannot generalize from the particular.

Going even further back to 1798, Blake proclaimed that ‘To generalise is to be an idiot; to particularise is the alone distinction of merit&quot; (Blake,1798, cited by Keynes (1957). In research, we may not wish to make such a strong distinction; these processes both have their uses in different kinds of research. But there is a major point here for the study of the particular that Wilson ( 2008 ) notes in commenting on Blake’s perception when he says, “Favouring the abstract over the concrete, one ‘sees all things only thro’ the narrow chinks of his cavern’ ” (referring here to Blake’s The Marriage of Heaven and Hell [1793], as cited in Wilson, 2008 , p. 62). The danger Wilson is pointing to here is that abstraction relies heavily on what we know from our past understanding of things, and this may prevent us experiencing a concrete event directly or “apprehend[ing] a particular moment” (Wilson, 2008 , p. 63).

Blake had a different mission, of course, from case researchers, and he was not himself free from abstractions, as Wilson points out, although he [Blake] fought hard “to break through mental barriers to something unique and living” (Wilson, 2008 , p. 65). It is this search for the “unique and living” and experiencing the “isness” of the particular that we should take from the Blake example to remind us of the possibility of discovering something “new,” beyond our current understanding of the way things are.

Focusing on particularization does not diminish the usefulness of case study research for policy makers or practitioners. Grounded in recognizable experience, the potential is there to reach a range of audiences and to facilitate use of the findings. It may be more difficult for those who seek formal generalizations that seem to offer a safe basis for policy making to accept case study reports. However, particular stories often hold the key to why policies have or have not worked well in the past. It is not necessary to present long cases—a criticism frequently leveled—to demonstrate the story of the case. Such case stories can be most insightful for policy makers who, like many of us in everyday life, often draw inferences from a single instance or case, whatever the formal evidence presented “I am reminded of the story of …” Stake ( 2006 , pp. 197–198) also reminds us that we are constantly making small generalizations from particular situations as we go about our professional work and life. These may not survive systematic research scrutiny, but the point Stake is making here is that it is our natural tendency to generalize from the particular in making sense of our worlds. In case study research that aspires to represent “lived experience,” this seems a natural way to proceed.

The case for studying the particular to inform practice in professional contexts needs less persuasion because practitioners can recognize the content and context quite readily and make the inference to their own particular context (Simons et al., 2003 ). In both sets of circumstances—policy and practice—it is more a question of whether the readers of our case research accept the validity of findings determined in this way, how they choose to learn, and our skill in telling the case study story.

Conclusion and Future Directions

In this chapter, I have presented an argument for case study research, making the case, in particular, for using qualitative methods to highlight what qualitative case study research can bring to the study of social and educational programs. I outlined the various ways in which case study is commonly used before focusing directly on case study as a major mode of research inquiry, noting characteristics it shares with other qualitative methodologies, as well as its difference and the difficulties it is often perceived to have. The chapter emphasizes the importance of thinking through what the case is to be sure that the issues explored and the data generated do illuminate this case and not any other.

But there is still more to be done. In particular, I think we need to be more adventurous in how we craft and report the case, and I have made several suggestions in the text as to how this could be done. I suspect also that we may have been too cautious in the past in how we justified case study research, borrowing concepts from other disciplines and forms of educational research. Fifty years on, it is time to take a greater risk—in demonstrating the intrinsic nature of case study research and what it can offer our understanding of human and social situations.

I have already drawn attention to the need to design the case, although this could be developed further to accentuate the uniqueness of the particular case. One way to do this is to feature individuals more in the design itself, not only to explore programs and policies through perspectives of key actors or groups and transactions between them, which to some extent happens already, but also to get them to characterize what makes the context unique. This is the reversal of many a design framework that starts with the logic of a program and takes forward the argument for personalizing evaluation (Kushner, 2000 ) on the grounds that it is through individuals that programs and policies are enacted. Apart from this attention to design, there are three other issues I think we need to explore further: the warrant for creative methods in case study, more imaginative reporting, and how we learn from a study of the singular.

Warrant for More Creative Methods in Case Study Research

The promise that creative methods have for eliciting in-depth understanding and capturing the unusual, the idiosyncratic, the uniqueness of the case, was mentioned in the methods section. Yet, in case study research, particularly in program and policy contexts, we have few good examples of the use of artistic approaches for eliciting and interpreting data, although there are more, indicated below for presenting it. This may be because case study research is often conducted in academic or policy environments, where propositional ways of knowing are more valued.

Using creative and artistic forms in generating and interpreting case study data offers a form of evidence that acknowledges experiential understanding in illuminating the uniqueness of the case. The question is how to establish the warrant for this way of knowing and persuade others of its virtue. The answer is simple: by demonstrating the use of these methods in action, by arguing for a different form of validity that matches the intrinsic nature of the method, and, above all, by good examples. I earlier noted the impact that Elliott’s CD of men with brain damage had on audiences beyond the case. Rugang ( 2006 ) also used the CD form, two in this instance, presenting contrasting photographs of a “new” culture and an old culture in one province in China. These told the story well, as did a narrative poem by Duke ( 2007 ) of her leadership illustrating how she performed her role as a nurse consultant with responsibility to help other nurses research concurrent with her usual job as a senior nurse.

Re-presenting Findings to Engage Audiences in Learning

In evaluative and research policy contexts, where case study is often the main mode of inquiry or part of a broader study, case study reports often take a formal structure or, sometimes, where the context is receptive, a portrayal or interpretative form. But, too often, the qualitative is an add-on to a story told by other means or reduced to issues in which the people who gave rise to the data are no longer seen. However, there are many ways to put them center stage.

Tell good stories and tell them well. Or, let key actors tell their own stories in narrative or on video. Explore the different ways technology can help. Make video clips that demonstrate events in context, illustrate interactions between people, give voice to participants—show the reality of the program, in other words. Use graphics to summarize key issues and interactive cartoon technology, as seen on some TED presentations, to summarize and visually show the complexity of the case. Explore the data visualization techniques now becoming widely available. Video diaries were mentioned in the methods section: seeing individuals tell their tales directly is a powerful way of communicating, unhindered by “our” sense making. Tell photo stories. Let the photos convey the narrative, but make sure the structure of the narrative is evident to ensure coherence. These are just the beginnings. Those skilled in information technology could no doubt stretch our imagination further.

One problem and a further question concerns our audiences. In your thesis you may well have scope to experiment with some of these alternative forms of presentation. In other contexts—I am thinking here of policy makers and commissioners—it may be more challenging, and you may wonder if they will accept these alternative modes of communication. Maybe not, in some cases. However, there are three points I wish to leave you with. First, if people are fully present in the story and the complexity is not diminished, those reading, watching, or hearing about the case will get the message. If you are worried about how commissioners might respond, remember that they are no different from any other stakeholder or participant when it comes to how they learn from human experience. Witness the reference to Okri ( 1997 ) earlier about how we learn and Stake’s ( 2010 ) reminder of how we generalize from the particular in everyday life.

Second, when you detect that the context requires a more formal presentation of findings, respond according to expectation, but also include elements of other forms of presentation. Nudge a little in the direction of creativity. Third, simply take a chance. Challenge the status quo. Find situations and contexts where you can fully represent the qualitative nature of the experience in the cases you study with creative forms of interpretation and representation. And let the audience decide.

Learning from a Study of the Singular

Finally, to return to the issue of “generalization” in case study that worries some audiences. I pointed out in the generalization section several ways in which it is possible to generalize from case study research, not in a formal propositional sense or from a case to a population, but by retaining a connection with the context in which the generalization first arose—that is, to realize in-depth understanding in context in different circumstances and situations. However, I also emphasized that, in many instances, it is particularization from which we learn. That is the point of the singular case study, and it is an art to perceive and craft the case in ways that we can.

Acknowledgments

Parts of this chapter build on ideas first explored in Simons ( 2009 ).

I am grateful to Bob Williams for pointing out the relevance of this quotation from Reynolds to remind us that “there is nothing new under the sun” and that we sometimes continue to engage endlessly in debates that have been well rehearsed before.

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

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

How to Approach Writing a Case Study Research Paper

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

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

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

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

Structure and Writing Style

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

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

I.  Introduction

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

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

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

II.  Literature Review

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

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

III.  Method

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

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

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

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

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

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

IV.  Discussion

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

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

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

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

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

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

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

V.  Conclusion

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

At Least Five Misconceptions about Case Study Research

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

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

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

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

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Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Case Study Research Method in Psychology

Saul Mcleod, PhD

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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Associate Editor for Simply Psychology

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On This Page:

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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Short and sweet: multiple mini case studies as a form of rigorous case study research

• Original Article
• Open access
• Published: 15 May 2024

Cite this article

You have full access to this open access article

• Sebastian Käss   ORCID: orcid.org/0000-0002-0640-3500 1 ,
• Christoph Brosig   ORCID: orcid.org/0000-0001-7809-0796 1 ,
• Markus Westner   ORCID: orcid.org/0000-0002-6623-880X 2 &
• Susanne Strahringer   ORCID: orcid.org/0000-0002-9465-9679 1  

Case study research is one of the most widely used research methods in Information Systems (IS). In recent years, an increasing number of publications have used case studies with few sources of evidence, such as single interviews per case. While there is much methodological guidance on rigorously conducting multiple case studies, it remains unclear how researchers can achieve an acceptable level of rigour for this emerging type of multiple case study with few sources of evidence, i.e., multiple mini case studies. In this context, we synthesise methodological guidance for multiple case study research from a cross-disciplinary perspective to develop an analytical framework. Furthermore, we calibrate this analytical framework to multiple mini case studies by reviewing previous IS publications that use multiple mini case studies to provide guidelines to conduct multiple mini case studies rigorously. We also offer a conceptual definition of multiple mini case studies, distinguish them from other research approaches, and position multiple mini case studies as a pragmatic and rigorous approach to research emerging and innovative phenomena in IS.

Avoid common mistakes on your manuscript.

1 Introduction

Case study research has become a widely used research method in Information Systems (IS) research (Palvia et al. 2015 ) that allows for a comprehensive analysis of a contemporary phenomenon in its real-world context (Dubé and Paré, 2003 ). This research method is particularly useful due to its flexibility in covering complex phenomena with multiple contextual variables, different types of evidence, and a wide range of analytical options (Voss et al. 2002 ; Yin 2018 ). Although case study research is particularly useful for studying contemporary phenomena, some researchers feel that it lacks rigour, particularly in terms of the validity of findings (Lee and Hubona 2009 ). In response to these criticisms, Yin ( 2018 ) provides comprehensive methodological steps to conduct case studies rigorously. In addition, many other publications with a partly discipline-specific view on case study research, offer guidelines for achieving rigour in case study research, e.g., Benbasat et al. ( 1987 ), Dubé and Paré ( 2003 ), Pan and Tan ( 2011 ), or Voss et al. ( 2002 ). Most publications on case study methodology converge on four criteria for ensuring rigour in case study research: (1) construct validity, (2) internal validity, (3) external validity, and (4) reliability (Gibbert et al. 2008 ; Voss et al. 2002 ; Yin 2018 ).

A key element of rigour in case study research is to look at the unit of analysis of a case from multiple perspectives in order to draw informed conclusions (Dubois and Gadde 2002 ). Case study researchers refer to this as triangulation, for example, by using multiple sources of evidence per case to support findings (Benbasat et al. 1987 ; Yin 2018 ). However, in our own research experience, we have come across numerous IS publications with a limited number of sources of evidence per case, such as a single interview per case. Some researchers refer to these studies as mini case studies (e.g., McBride 2009 ; Weill and Olson 1989 ), while others refer to them as multiple mini cases (e.g., Eisenhardt 1989 ). We were unable to find a definition or conceptualisation of this type of case study. Therefore, we will refer to this type of case study as a multiple mini case study (MMCS). Interestingly, many researchers use these MMCSs to study emerging and innovative phenomena.

From a methodological perspective, multiple case study publications with limited sources of evidence, also known as MMCSs, may face criticism for their lack of rigour (Dubé and Paré 2003 ). Alternatively, they may be referred to as “marginal case studies” (Piekkari et al. 2009 , p. 575) if they fail to establish a connection between theory and empirical evidence, provide only limited context, or merely offer illustrative aspects (Piekkari et al. 2009 ). IS scholars advocate conducting case study research in a mindful manner by balancing methodological blueprints and justified design choices (Keutel et al. 2014 ). Consequently, we propose MMCSs as a mindful approach with the potential for rigour, distinguishing them from marginal case studies. The following research question guides our study:

RQ: How can researchers rigorously conduct MMCSs in the IS discipline?

As shown in Fig.  1 , we develop an analytical framework by synthesising methodological guidance on how to rigorously conduct multiple case study research. We then address three aspects of our research question: For aspect (1), we analyse published MMCSs in the IS discipline to derive a "Research in Practice" definition of MMCSs and research situations for MMCSs. For aspect (2), we use the analytical framework to analyse how researchers in the IS discipline ensure that existing MMCSs follow a rigorous methodology. For aspect (3), we discuss the methodological findings about rigorous MMCSs in order to derive methodological guidelines for MMCSs that researchers in the IS discipline can follow.

Overview of the research approach

We approach these aspects by introducing the conceptual foundation for case study research in Sect.  2 . We define commonly accepted criteria for ensuring validity in case study research, introduce the concept of MMCSs, and distinguish them from other types of case studies. Furthermore, as a basis for analysis, we present an analytical framework of methodological steps and options for the rigorous conduct of multiple case study research. Section  3 presents our methodological approach to identifying published MMCSs in the IS discipline. In Sect.  4 , we first define MMCSs from a research in practice perspective (Sect.  4.1 ). Second, we present an overview of methodological options for rigorous MMCSs based on our analytical framework (Sect.  4.2 ). In Sect.  5 , we differentiate MMCSs from other research approaches, identify research situations of MMCSs (i.e., to study emerging and innovative phenomena), and provide guidance on how to ensure rigour in MMCSs. In our conclusion, we clarify the limitations of our study and provide an outlook for future research with MMCSs.

2 Conceptual foundation

2.1 case study research.

Case study research is about understanding phenomena by studying one or multiple cases in their context. Creswell and Poth ( 2016 ) define it as an “approach in which the investigator explores a bounded system (a case) or multiple bounded systems (cases) over time, through detailed, in-depth data collection” (p. 73). Therefore, it is suitable for complex topics with little available knowledge, needing an in-depth investigation, or where the research subject is inseparable from its context (Paré 2004 ). Additionally, Yin ( 2018 ) states that case study research is useful if the research focuses on contemporary events where no control of behavioural events is required. Typically, this type of research is most suitable for how and why research questions (Yin 2018 ). Eventually, the inferences from case study research are based on analytic or logical generalisation (Yin 2018 ). Instead of drawing conclusions from a representative statistical sample towards the population, case study research builds on analytical findings from the observed cases (Dubois and Gadde 2002 ; Eisenhardt and Graebner 2007 ). Case studies can be descriptive, exploratory, or explanatory (Dubé and Paré 2003 ).

The contribution of research to theory can be divided into the steps of theory building , development and testing , which is a continuum (Ridder 2017 ; Welch et al. 2011 ), and case studies are useful at all stages (Ridder 2017 ). In theory building, there is no theory to explain a phenomenon, and the researcher identifies new concepts, constructs, and relationships based on the data (Ridder 2017 ). In theory development, a tentative theory already exists that is extended or refined (e.g., by adding new antecedents, moderators, mediators, and outcomes) (Ridder 2017 ). In theory testing, an existing theory is challenged through empirical investigation (Ridder 2017 ).

In case study research, there are different paradigms for obtaining research results, either positivist or interpretivist (Dubé and Paré 2003 ; Orlikowski and Baroudi 1991 ). The positivist paradigm assumes that a set of variables and relationships can be objectively identified by the researcher (Orlikowski and Baroudi 1991 ). In contrast, the interpretivist paradigm assumes that the results are inherently rooted in the researcher’s worldview (Orlikowski and Baroudi 1991 ). Nowadays, researchers find that there are similar numbers of positivist and interpretivist case studies in the IS discipline compared to almost 20 years ago when positivist research was perceived as dominant (Keutel et al. 2014 ; Klein and Myers 1999 ). As we aim to understand how to conduct MMCSs rigorously, we focus on methodological guidance for positivist case study research.

The literature proposes a four-phased approach to conducting a case study: (1) the definition of the research design, (2) the data collection, (3) the data analysis, and (4) the composition (Yin 2018 ). Table 1 provides an overview and explanation of the four phases.

Case studies can be classified based on their depth and breadth, as shown in Fig.  2 . We can distinguish five types of case studies: in-depth single case studies , marginal case studies , multiple case studies , MMCSs , and extensive in-depth multiple case studies . Each type has distinct characteristics, yet the boundaries between the different types of case studies is blurred. Except for the marginal case studies, the italic references in Fig.  2 are well-established publications that define the respective type and provide methodological guidance. The shading is to visualise the different types of case studies. The italic references in Fig.  2 for marginal case studies refer to publications that conceptualise them.

Simplistic conceptualisation of MMCS

In-depth single case studies focus on a single bounded system as a case (Creswell and Poth 2016 ; Paré 2004 ; Yin 2018 ). According to the literature, a single case study should only be used if a case meets one or more of the following five characteristics: it is a critical, unusual, common, revelatory, or longitudinal case (Benbasat et al. 1987 ; Yin 2018 ). Single case studies are more often used for descriptive research (Dubé and Paré 2003 ).

A second type of case studies are marginal case studies , which generally have low depth (Keutel et al. 2014 ; Piekkari et al. 2009 ). Marginal case studies lack a clear link between theory and empirical evidence, a clear contextualisation of the case, and are often used for illustration purposes (Keutel et al. 2014 ; Piekkari et al. 2009 ). Therefore, marginal case studies provide only marginal insights with a lack of generalisability.

In contrast, multiple case studies employ multiple cases to obtain a broader picture of the researched phenomenon from different perspectives (Creswell and Poth 2016 ; Paré 2004 ; Yin 2018 ). These multiple case studies are often considered to provide more robust results due to the multiplicity of their insights (Eisenhardt and Graebner 2007 ). However, often discussed criticisms of multiple case studies are high costs, difficult access to multiple sources of evidence for each case, and long duration (Dubé and Paré 2003 ; Meredith 1998 ; Voss et al. 2002 ). Eisenhardt ( 1989 ) considers four to ten in-depth cases as a suitable number of cases for multiple case study research. With fewer than four cases, the empirical grounding is less convincing, and with more than ten cases, researchers quickly get overwhelmed by the complexity and volume of data (Eisenhardt 1989 ). Therefore, methodological literature views extensive in-depth multiple case studies as almost infeasible due to their high complexity and resource demands, which can easily overwhelm the research team and the readers (Stake 2013 ). Hence, we could not find a methodological publication outlining the approach for this case study type.

To solve the complexity and resource issues for multiple case studies, a new phenomenon has emerged: MMCS . An MMCS is a special type of multiple case study that focuses on an investigation's breadth by using a relatively high number of cases while having a somewhat limited depth per case. We characterise breadth not only by the number of cases but also by the variety of the cases. Even though there is no formal conceptualisation of the term, we understand MMCSs as a type of multiple case study research with few sources of evidence per case. Due to the limited depth per case, one can overcome the resource and complexity issues of classical multiple case studies. However, having only some sources of evidence per case may be considered a threat to rigour. Therefore, in this publication, we provide suggestions on how to address these threats.

2.2 Rigour in case study research

Rigour is essential for case study research (Dubé and Paré 2003 ; Yin 2018 ) and, in the early 2000s, researchers criticised case study research for inadequate rigour (e.g., Dubé and Paré 2003 ; Gibbert et al. 2008 ). Based on this, various methodological publications provide guidance for rigorous case study research (e.g., Dubé and Paré 2003 ; Gibbert et al. 2008 ).

Methodological literature proposes four criteria to ensure rigour in case study research: Construct validity , internal validity , external validity , and reliability (Dubé and Paré 2003 ; Gibbert et al. 2008 ; Yin 2018 ). Table 2 outlines these criteria and states in which research phase they should be addressed (Yin 2018 ). Methodological literature agrees that all four criteria must be met for rigorous case study research (Dubé and Paré 2003 ).

The methodological literature discusses multiple options for achieving rigour in case study research (e.g., Benbasat et al. 1987 ; Dubé and Paré 2003 ; Eisenhardt 1989 ; Yin 2018 ). We aggregated guidance from multiple sources by conducting a cross-disciplinary literature review to build our analytical foundation (cf. Fig. 1 ). This literature review aims to identify the most relevant multiple case study methodology publications from a cross-disciplinary and IS-specific perspective. We focus on the most cited methodology publications, while being aware that this may over-represent disciplines with a higher number of case study publications. However, this approach helps to capture an implicit consensus among case study researchers on how to conduct multiple case studies rigorously. The literature review produced an analytical framework of methodological steps and options for conducting multiple case studies rigorously. Appendix A Footnote 1 provides a detailed documentation of the literature review process. The analytical framework derived from the set of methodological publications is presented in Table  3 . We identified required and optional steps for each research stage. The analytical framework is the basis for the further analysis of MMCS and an explanation of all methodological steps is provided in Appendix B. Footnote 2

3 Research methodology

For our research, we analysed published MMCSs in the IS discipline with the goal of understanding how these publications ensured rigour. This section outlines the methodology of how we identified our MMCS publications.

First, we searched bibliographic databases and citation indexing services (Vom Brocke et al. 2009 ; Vom Brocke et al. 2015 ) to retrieve IS-specific MMCSs (Hanelt et al. 2015 ). As shown in Fig.  3 , we used two sets of keywords, the first set focusing on multiple case studies and the second set explicitly on mini case studies. We decided to follow this approach as many MMCSs are positioned as multiple case studies, avoiding the connotation “mini” or “short”. We restricted our search to completed research publications written in English from litbaskets.io size “S”, a set of 29 highly ranked IS journals (Boell and Wang 2019 ) Footnote 3 and leading IS conference proceedings from AMCIS, ECIS, HICSS, ICIS, and PACIS (published until end of June 2023). We focused on these outlets, as they can be taken as a representative sample of high quality IS research (Gogan et al. 2014 ; Sørensen and Landau 2015 ).

The search process for published MMCSs in the IS discipline

Second, we screened the obtained set of IS publications to identify MMCSs. We only included publications with positivist multiple cases where the majority of cases was captured with only one primary source of evidence. Further, we excluded all publications which were interview studies rather than case studies (i.e., they do not have a clearly defined case). In some cases, it was unclear from the full text whether a publication fulfils this requirement. Therefore, we contacted the authors and clarified the research methodology with them. Eventually, our final set contained 50 publications using MMCSs.

For qualitative data analysis, we employed axial coding (Recker 2012 ) based on the pre-defined analytical framework shown in Table  3 . For the coding, we followed the explanations of the authors in the manuscripts. The coding was conducted and reviewed by two of the authors. We coded the first five publications of the set of IS MMCS publications together and discussed our decisions. After the initial coding was completed, we checked the reliability and validity by re-coding a sample of the other author’s set. In this sample, we achieved inter-coder reliability of 91% as a percent agreement in the decisions made (Nili et al. 2020 ). Hence, we consider our coding as highly consistent.

In the results section, we illustrate the chosen methodological steps for each MMCS type (descriptive, exploratory, and explanatory). For this purpose, we selected three publications based on two criteria: only journal publications, as they have more details about their methodological steps and publications which applied most of the analytical framework’s methodology steps. This led to three exemplary IS MMCS publications: (1) McBride ( 2009 ) for descriptive MMCSs, (2) Baker and Niederman ( 2014 ) for exploratory MMCSs, and (3) van de Weerd et al. ( 2016 ) for explanatory MMCSs.

4.1 MMCS from a “Research in Practice" perspective

In this section, we explain MMCSs from a "Research in Practice" perspective and identify different types based on our sample of 50 MMCS publications. As outlined in Sect.  2.1 , an MMCS is a special type of a multiple case study, which focuses on an investigation’s breadth by using a relatively high number of cases while having a limited depth per case. In the most extreme scenario, an MMCS only has one source of evidence per case. Moreover, breadth is not only characterised by the number of cases, but also by the variety of the cases. MMCSs have been used widely but hardly labelled as such, i.e., only 10 of our analysed 50 MMCS publications explicitly use the terms mini or short case in the manuscript . Multiple case study research distinguishes between descriptive, exploratory, and explanatory case studies (Dubé and Paré 2003 ). The MMCSs in our sample follow the same classification with three descriptive, 40 exploratory, and seven explanatory MMCSs. Descriptive and exploratory MMCSs are used in the early stages of research , and exploratory and explanatory MMCSs are used to corroborate findings .

Descriptive MMCSs provide little information on the methodological steps for the design, data collection, analysis, and presentation of results. They are used to illustrate novel phenomena and create research questions, not solutions, and can be useful for developing research agendas (e.g., McBride 2009 ; Weill and Olson 1989 ). The descriptive MMCS publications analysed contained between four to six cases, with an average of 4.6 cases per publication. Of the descriptive MMCSs analysed, one did not state research questions, one answered a how question and the third answered how and what questions. Descriptive MMCSs are illustrative and have a low depth per case, resulting in the highest risk of being considered a marginal case study.

Exploratory MMCSs are used to explore new phenomena quickly, generate first research results, and corroborate findings. Most of the analysed exploratory MMCSs answer what and how questions or combinations. However, six publications do not explicitly state a research question, and some MMCSs use why, which, or whether research questions. The analysed exploratory MMCSs have three to 27 cases, with an average of 10.2 cases per publication. An example of an exploratory MMCS is the study by Baker and Niederman ( 2014 ), who explore the impacts of strategic alignment during merger and acquisition (M&A) processes. They argue that previous research with multiple case studies (mostly with  three cases) shows some commonalities, but much remains unclear due to the low number of cases. Moreover, they justify the limited depth of their research with the “proprietary and sensitive nature of the questions” (Baker and Niederman 2014 , p. 123).

Explanatory MMCSs use an a priori framework with a relatively high number of cases to find groups of cases that share similar characteristics. Most explanatory MMCSs answer how questions, yet some publications answer what, why, or combinations of the three questions. The analysed explanatory MMCSs have three to 18 cases, with an average of 7.2 cases per publication. An example of an explanatory MMCS publication is van de Weerd et al. ( 2016 ), who researched the influence of organisational factors on the adoption of Software as a Service (SaaS) in Indonesia.

4.2 Applied MMCS methodology in IS publications

4.2.1 overarching.

In the following sections, we present the results of our analysis. For this purpose, we mapped our 50 IS MMCS publications to the methodological options (Table  3 ) and present one example per MMCS type. We extended some methodological steps with options from methodology-in-use. A full coding table can be found in Appendix D Footnote 4 . Tables 4 , 5 , 6 and 7 summarise the absolute and percentual occurrences of each methodological option in descriptive, exploratory, and explanatory IS MMCS publications. All tables are structured in the same way and show the number of absolute and, in parentheses, the percentual occurrences of each methodological option. The percentages may not add up to 100% due to rounding. The bold numbers show the most common methodological option for each MMCS type and step. Most publications were classified in previously identified options. Some IS MMCS publications lacked detail on methodological steps, so we classified them as "step not evident". Only 16% (8 out of 50) explained how they addressed validity and reliability threats.

4.2.2 Research design phase

There are six methodological steps in the research design phase, as shown in Table  4 . Descriptive MMCSs usually define the research question (2 out of 3, 67%), clarify the unit of analysis (2 out of 3, 67%), bound the case (2 out of 3, 67%), or specify an a priori theoretical framework (2 out of 3, 67%). The case replication logic is mostly not evident (2 out of 3, 67%). Descriptive MMCS use a criterion-based selection (1 out of 3, 33%), a maximum variation selection (1 out of 3, 33%), or do not specify the selection logic (1 out of 3, 33%). Descriptive MMCSs have a high risk of becoming a marginal case study due to their illustrative nature–our chosen example is not different. McBride ( 2009 ) does not define the research question, does not have a priori theoretical framework, nor does he justify the case replication and the case selection logic. However, he clarifies the unit of analysis and extensively bounds each case with significant context about the case organisation and its setup.

The majority of exploratory MMCSs define the research question (34 out of 40, 85%) clarify the unit of analysis (35 out of 40, 88%), and specify an a priori theoretical framework (33 out of 40, 83%). However, only a minority (6 out of 40, 15%) follow the instructions of bounding the case or justify the case replication logic (13 out of 40, 33%). The most used case selection logic is the criterion-based selection (23 out of 40, 58%), followed by step not evident (5 out of 40, 13%), other selection approaches (3 of 40, 13%), maximum variation selection (3 out of 40, 13%), a combination of approaches (2 out of 40, 5%), snowball selection (2 out of 40, 5%), typical case selection (1 out of 40, 3%), and convenience-based selection (1 out of 40, 3%). Baker and Niederman ( 2014 ) build their exploratory MMCS on previous multiple case studies with three cases that showed ambiguous results. Hence, Baker and Niederman ( 2014 ) formulate three research objectives instead of defining a research question. They clearly define the unit of analysis (i.e., the integration of the IS function after M&A) but lack the bounding of the case. The authors use a rather complex a priori framework, leading to a high number of required cases. This a priori framework is also used for the “theoretical replication logic [to choose] conforming and disconfirming cases” (Baker and Niederman 2014 , p. 116). A combination of maximum variation and snowball selection is used to select the cases (Baker and Niederman 2014 ). The maximum variation is chosen to get evidence for all elements of their rather complex a priori framework (i.e., the breadth), and the snowball sampling is chosen to get more details for each framework element.

All explanatory MMCS s define the research question, clarify the unit of analysis, and specify an a priori theoretical framework. However, only one (14%) bounds the case. The case replication logic is mostly a mixture of theoretical and literal replication (3 out of 7, 43%) and one (14%) MMCS does a literal replication. For 43% (3 out of 7) of the publications, the step is not evident. Most explanatory MMCSs use criterion-based selection (4 out of 7, 57%), followed by maximum variation selection (2 out of 7, 29%) and snowball selection (1 out of 7, 14%). In their publication, van de Weerd et al. ( 2016 ) define the research question and clarify the unit of analysis (i.e., the influence of organisational factors on SaaS adoption in Indonesian SMEs). Further, they specify an a priori framework (i.e., based on organisational size, organisational readiness, and top management support) to target the research (van de Weerd et al. 2016 ). A combination of theoretical (between the groups of cases) and literal (within the groups of cases) replication was used. To strengthen the findings, van de Weerd et al. ( 2016 ) find at least one other literally replicated case for each theoretically replicated case.

To summarize this phase, we see that in all three types of MMCSs, the majority of publications define the research question, clarify the unit of analysis, and specify an a priori theoretical framework. Moreover, descriptive MMCSs are more likely to bound the case than exploratory and explanatory MMCSs. However, only a minority across all MMCSs justify the case replication logic, whereas the majority does not. Most MMCSs justify the case selection logic, with criterion-based case selection being the most often applied methodological option.

4.2.3 Data collection phase

In the data collection phase, there are four methodological steps, as summarised in Table  5 .

One descriptive MMCS applies triangulation via multiple sources, whereas for the majority (2 out of 3, 67%), the step is not evident. One (33%) of the analysed descriptive MMCSs creates a full chain of evidence, none creates a case study database, and one (33%) uses a case study protocol. McBride ( 2009 ) applies triangulation via multiple sources, as he followed “up practitioner talks delivered at several UK annual conferences” (McBride 2009 , p. 237). Therefore, we view the follow-up interviews as the primary source of evidence per case, as dedicated questions to the unit of analysis can be asked per case. Triangulation via multiple sources was then conducted by combining practitioner talks and documents with follow-up interviews. McBride ( 2009 ) does not create a full chain of evidence, a case study database, nor a case study protocol. This design decision might be rooted in the objective of a descriptive MMCS to illustrate and open up new questions rather than find clear solutions (McBride 2009 ).

Most exploratory MMCSs triangulate via multiple sources (20 out of 40, 50%) or via multiple investigators (4 out of 40, 10%). Eight (20%) exploratory MMCSs apply multiple triangulation types and for eight (20%), no triangulation is evident. At first glance, a triangulation via multiple sources may seem contradictory to the definition of MMCSs–yet it is not. MMCSs that triangulate via multiple sources have one source per case as the primary, detailed evidence (e.g., an interview), which is combined with easily available supplementary sources of evidence (e.g., public reports and documents (Baker and Niederman 2014 ), press articles (Hahn et al. 2015 ), or online data (Kunduru and Bandi 2019 )). As this leads to multiple sources of evidence, we understand this as a triangulation via multiple sources; however, on a different level than triangulating via multiple in-depth interviews per case. Only a minority of exploratory MMCSs create a full chain of evidence (14 out of 40, 35%), and a majority (23 out of 40, 58%) use a case study database or a case study protocol (20 out of 40, 50%). Baker and Niederman ( 2014 ) triangulate with multiple sources (i.e., financial reports as supplementary sources) to increase the validity of their research. Further, the authors create a full chain of evidence from their research question through an identical interview protocol to the case study’s results. For every case, an individual case report is created and stored in the case study database (Baker and Niederman 2014 ).

All explanatory MMCSs triangulate during the data collection phase, either via multiple sources (2 out of 7, 29%) or a combination of multiple investigators and sources (5 out of 7, 71%). Interestingly, only three explanatory MMCSs (43%) create a full chain of evidence. All create a case study database (7 out of 7, 100%) and the majority creates a case study protocol (6 out of 7, 86%). In their explanatory MMCS, van de Weerd et al. ( 2016 ) use semi-structured interviews as the primary data collection method. The interview data is complemented “with field notes and (online) documentation” (van de Weerd et al. 2016 , p. 919), e.g., data from corporate websites or annual reports. Moreover, a case study protocol and a case study database in NVivo are created to increase reliability.

To summarise the data collection phase, we see that most (40 out of 50, 80%) of MMCSs apply some type of triangulation. However, only 36% (18 out of 50) of the analysed MMCSs create a full chain of evidence. Moreover, descriptive MMCSs are less likely to create a case study database (0 out of 3, 0%) or a case study protocol (1 out of 3, 33%). In contrast, most exploratory and explanatory MMCS publications create a case study database and case study protocol.

4.2.4 Data analysis phase

There are three methodological steps (cf. Table 6 ) for the data analysis phase, each with multiple methodological options.

One descriptive MMCS (33%) corroborates findings through triangulation, and two do not (67%). Further, one (33%) uses a rich description of findings as other corroboration approaches, whereas for the majority (2 out of 3, 67%), the corroboration with other approaches is not evident. Descriptive MMCSs mostly do not define their within-case analysis strategy (2 out of 3, 67%). However, pre-defined patterns are used to conduct a cross-case analysis (2 out of 3, 67%). In the data analysis, McBride ( 2009 ) triangulates via multiple sources of evidence (i.e., talks at practitioner conferences and resulting follow-up interviews), but does not apply other corroboration approaches or provides methodological explanations for the within or cross-case analysis. This design decision might be rooted in the illustrative nature of his descriptive MMCS and the focus on analysing each case standalone.

Exploratory MMCSs mostly corroborate findings through a combination of triangulation via multiple investigators and sources (15 out of 40, 38%) or triangulation via multiple sources (9 out of 40, 23%). However, for ten (25%) exploratory MMCSs, this step is not evident. For the other corroboration approaches, a combination of approaches is mostly used (15 out of 40, 38%), followed by rich description of findings (11 out of 40, 28%), peer review (6 out of 40, 15%), and prolonged field visits (1 out of 40, 3%). For five (13%) publications, other corroboration approaches are not evident. Pattern matching (17 out of 40, 43%) and explanation building (5 out of 40, 13%) are the most used methodological options for the within-case analysis. To conduct a cross-case analysis, 11 (28%) MMCSs use a comparison of pairs or groups of cases, nine (23%) pre-defined patterns, and six (15%) structure their data along themes. Interestingly, for 14 (35%) exploratory MMCSs, no methodological step to conduct the cross-case analysis is evident. Baker and Niederman ( 2014 ) use a combination of triangulation via multiple investigators (“The interviews were coded by both researchers independently […], with a subsequent discussion to reach complete agreement” (Baker and Niederman 2014 , p. 117)) and sources to increase internal validity. Moreover, the authors use a rich description of the findings. An explanation-building strategy is used for the within-case analysis, and the cross-case analysis is done based on pre-defined patterns (Baker and Niederman 2014 ). This decision for the cross-case analysis is justified by a citation of Dubé and Paré ( 2003 , p. 619), who see it as “a form of pattern-matching in which the analysis of the case study is carried out by building a textual explanation of the case.”

Explanatory MMCSs corroborate findings through a triangulation via multiple sources (4 out of 7, 57%) or a combination of multiple investigators and sources (3 out of 7, 43%). For the other corroboration approaches, a rich description of findings (3 out of 7, 43%), a combination of approaches (3 out of 7, 43%), or peer review (1 out of 7, 14%) are used. To conduct a within-case analysis, pattern matching (5 out of 7, 71%) or explanation building (1 out of 7, 14%) are used. For the cross-case analysis, pre-defined patterns (3 out of 7, 43%) and a comparison of pairs or groups of cases (2 out of 7, 29%) are used; yet, for two (29%) explanatory MMCSs a cross-case analysis step is not evident. van de Weerd et al. ( 2016 ) corroborate their findings through a triangulation via multiple sources, a combination of rich description of findings and solicitation of participants’ views (“summarizing the interview results of each case company for feedback and approval” (van de Weerd et al. 2016 , p. 920)) as other corroboration approaches. Moreover, for the within-case analysis, the authors “followed an explanation-building procedure to strengthen […] [the] internal validity” (van de Weerd et al. 2016 , p. 920). For the cross-case, the researchers compare groups of cases. They refer to this approach as an informal qualitative comparative analysis.

To summarize the results of the data analysis phase, we see that some type of triangulation is used by most of the MMCSs, with source triangulation (alone or in combination with another approach) being the most often used methodological option. For the within-case analysis, pattern matching (22 of 50, 44%) is the most often used methodological option. For the cross-case analysis, pre-defined patterns are most often used (14 out of 50, 28%). However, depending on the type of MMCS, there are differences in the options used and some methodological options are never used (e.g., time-series analysis and solicitation of participants’ views).

4.2.5 Composition phase

We can find two methodological steps for the composition phase, as summarized in Table  7 .

Descriptive MMCSs do not apply triangulation in the composition phase (3 out of 3, 100%), nor do they use the methodological step to let key informants review the draft of the case study report (3 of 3, 100%). Also, the descriptive MMCS by McBride ( 2009 ) does not apply any of the methodological steps.

Exploratory MMCSs mostly use triangulation via multiple sources (25 out of 40, 63%), a combination of multiple sources and theories (2 out of 40, 5%), triangulation via multiple investigators (1 out of 40, 3%), and a combination of multiple sources and methods (1 out of 40, 3%). However, for 11 (28%) exploratory MMCS publications, no triangulation step is evident. Moreover, the majority (24 out of 40, 85%) do not let key informants review a draft of the case study report. Baker and Niederman ( 2014 ) do not use triangulation in the composition phase nor let key informants review the draft of the case study report. An example of an exploratory publication that applies both methodological steps is the publication by Kurnia et al. ( 2015 ). The authors triangulate via multiple sources and let key informants review their interview transcripts and the case study report to increase construct validity.

Explanatory MMCSs mostly use triangulation via multiple sources (5 out of 7, 71%) and for two (29%), the step is not evident. Furthermore, only two MMCS (29%) publications let key informants review the draft of the case study report, whereas the majority (5 out of 7, 71%) do not. In their publication , van de Weerd et al. ( 2016 ) use both methodological steps of the composition phase. The authors triangulate via multiple sources by presenting interview snippets from different cases for each result in the case study manuscript. Moreover, each case and the final case study report were shared with key informants for review and approval to reduce the risk of misinterpretations and increase construct validity.

To summarize, most exploratory and explanatory MMCSs use triangulation in the composition phase, whereas descriptive MMCSs do not. Moreover, only a fraction of all MMCSs let key informants review a draft of the case study report (8 out of 50, 16%).

5 Discussion

5.1 mmcs from a “research in practice" perspective, 5.1.1 delineating mmcs from other research approaches.

In this section, we delineate MMCSs from related research approaches. In the subsequent sections, we outline research situations for which MMCSs can be used and the benefits MMCSs provide.

Closely related research approaches from which we delineate MMCSs are multiple case studies , interviews, and vignettes . As shown in Fig.  2 , MMCSs differ from multiple case studies in that they focus on breadth by using a high number of cases with limited depth per case. In the most extreme situation, an MMCS only has one primary source of evidence per case. Moreover, MMCSs can also consider a greater variety of cases. In contrast, multiple case studies have a high depth per case and multiple sources of evidence per case to allow for a source triangulation (Benbasat et al. 1987 ; Yin 2018 ). Moreover, multiple case studies mainly focus on how and why research questions (Yin 2018 ), whereas MMCSs can additionally answer what, whether, and which research questions. The rationale why MMCSs are used for more types of research questions is their breadth, allowing them to also answer rather explorative research questions.

Distinguishing MMCSs from interviews is more difficult . Yet, we see two differences. First, interview studies do not have a clear unit of analysis. Interview studies may choose interviewees based on expertise (expert interviews), whereas case study researchers select informants based on the ability to inform about the case (key informants) (Yin 2018 ). Most of the 50 analysed MMCS (88%) specify their unit of analysis. Second, MMCSs can use multiple data collection methods (e.g., observations, interviews, documents), while interviews only use one (the interview) (Lamnek and Krell 2010 ). An example showing these delineation difficulties between MMCSs and interviews is the publication of Demlehner and Laumer ( 2020 ). The authors claim to take “a multiple case study approach including 39 expert interviews” (Demlehner and Laumer 2020 , p. 1). However, our criteria classify this as an interview study. Demlehner and Laumer ( 2020 ) contend that the interviewees were chosen using a “purposeful sampling strategy” (p. 5). However, case study research selects cases based on replication logic, not sampling (Yin 2018 ). Moreover, the results are not presented on a per-case basis (as usual for case studies); instead, the findings are presented on an aggregated level, similar to expert interviews. Therefore, we would not classify this publication as an MMCS but find that it is a very good example to discuss this delineation.

MMCSs differ from vignettes, which are used for (1) data collection , (2) data analysis , and (3) research communication (Klotz et al. 2022 ; Urquhart 2001 ). Researchers use vignettes for data collection as stimuli to which participants react (Klotz et al. 2022 ), i.e., a carefully constructed description of a person, object, or situation (Atzmüller and Steiner 2010 ; Hughes and Huby 2002 ). We can delineate MMCS from vignettes for data collection based on this definition. First, MMCSs are not used as a stimulus to which participants can react, as in MMCSs, data is collected without the stimulus requirement. Furthermore, vignettes for data collection are carefully constructed, which contradicts the characteristics of MMCS, that are all based on collected empirical data and not constructed descriptions.

A data analysis vignette is used as a retrospective tool (Klotz et al. 2022 ) and is very short, which makes it difficult to analyse deeper relationships between constructs. MMCSs differ from vignettes for data analysis in two ways. First, MMCSs are a complete research methodology with four steps, whereas vignettes for data analysis cover only one step (the data analysis) (e.g., Zamani and Pouloudi 2020 ). Second, vignettes are too short to conduct a thorough analysis of relationships, whereas MMCSs foster a more comprehensive analysis, allowing for a deeper analysis of relationships.

Finally, a vignette used for research communication “(1) is bounded to a short time span, a location, a special situation, or one or a few key actors, (2) provides vivid, authentic, and evocative accounts of the events with a narrative flow, (3) is rather short, and (4) is rooted in empirical data, sometimes inspired by data or constructed.” (Klotz et al. 2022 , p. 347). Based on the four elements for the vignettes’ definition, we can delineate MMCS from vignettes used for research communication. First, MMCSs are not necessarily bounded to a short time span, location, special situation, or key actors; instead, with MMCSs, a clearly defined case bounded in its context is researched. Second, the focus of MMCSs is not on the narrative flow; instead, the focus is on describing (c.f., McBride ( 2009 )), exploring (c.f., Baker and Niederman ( 2014 )), or explaining (c.f., van de Weerd et al. ( 2016 )) a phenomenon. Third, while MMCSs do not have the depth of multiple case studies, they are much more comprehensive than vignettes (e.g., the majority of analysed publications (42 of 50, 84%) specify an a priori theoretical framework). Fourth, every MMCS must be based on empirical data, i.e., all of our 50 MMCSs collect data for their study and base their results on this data. This is a key difference from vignettes, which can be completely fictitious (Klotz et al. 2022 ).

5.1.2 MMCS research situations

The decision to use an MMCS as a research method depends on the research context. MMCSs can be used in the early stages of research (descriptive and exploratory MMCS) and to corroborate findings (exploratory and explanatory MMCS). Academic literature has yet to agree on a uniform categorisation of research questions. For instance, Marshall and Rossman ( 2016 ) distinguish between descriptive, exploratory, explanatory, and emancipatory research questions. In contrast, Yin ( 2018 ) distinguishes between who , what , where , how , and why questions, where he argues that the latter two are especially suitable for explanatory case study research. MMCSs can answer more types of research questions than Yin ( 2018 ) proposed. The reason for this is rooted in the higher breadth of MMCSs, which allows MMCSs to also answer rather exploratory what , whether , or which questions, besides the how and why questions that are suggested by Yin ( 2018 ).

For descriptive MMCSs , the main goal of the how and what questions is to describe the phenomenon. However, in our sample of analysed MMCSs, the analysis stops after the description of the phenomenon. The main goal of the five types of exploratory MMCS research questions is to investigate little-known aspects of a particular phenomenon. The how and why questions analyse operational links between different constructs (e.g., “How do different types of IS assets account for synergies between business units to create business value?” (Mandrella et al. 2016 , p. 2)). Exploratory what questions can be answered by case study research and other research methods (e.g., surveys or archival analysis) (Yin 2018 ). Nevertheless, all whether and which MMCS research questions can also be re-formulated as exploratory what questions. The reason why many MMCSs answer what , whether , or which research questions lies in the breadth (i.e., higher number and variety of cases) of MMCS, that allow them to answer these rather exploratory research questions to a satisfactory level. Finally, the research questions of the explanatory MMCSs aim to analyse operational links (i.e., how or why something is happening). This is also in line with the findings of Yin ( 2018 ) for multiple case study research. However, for MMCSs, this view must be extended, as explanatory MMCSs are also able to answer what questions. We explain this with the higher breadth of MMCS.

To discuss an MMCS’s contribution to theory, we use the idea of the theory continuum proposed by Ridder ( 2017 ) (cf. Section  2.1 ). Despite being used in the early phase of research (descriptive and exploratory), we do not recommend using MMCSs to build theory . We argue that for theory building, data with “as much depth as […] feasible” (Eisenhardt 1989 , p. 539) is required on a per-case basis. However, a key characteristic of MMCSs is the limited depth per case, which conflicts with the in-depth requirements of theory building. Moreover, a criterion for theory building is that there is no theory available which explains the phenomenon (Ridder 2017 ). Nevertheless, in our analysed MMCSs, 84% (42 out of 50) have an a priori theoretical framework. Furthermore, for theory building, the recommendation is to use between four to ten cases; with more, “it quickly becomes difficult to cope with the complexity and volume of the data” (Eisenhardt 1989 , p. 545). However, a characteristic of MMCSs is to have a relatively high number of cases, i.e., the analysed MMCSs often have more than 20 cases, which is significantly above the recommendation for theory building.

The next phase in the theory continuum is theory development , where a tentative theory is extended or refined (Ridder 2017 ). MMCSs should and are used for theory development, i.e., 84% (42 out of 50) of analysed MMCS publications have an a priori theoretical framework extended and refined using the MMCS. An MMCS example for theory development is the research of Karunagaran et al. ( 2016 ), who use a combination of the diffusion of innovation theory and technology organisation environment framework as tentative theories to research the adoption of cloud computing. As Ridder ( 2017 ) outlined, for theory development, literal replication and pattern matching should be used. Both methodological steps are used by Karunagaran et al. ( 2016 ) to identify the mechanisms of cloud adoption more precisely.

The next step in the theory continuum is theory testing , where existing theory is challenged by finding anomalies that existing theory cannot explain (Ridder 2017 ). The boundaries between theory development and testing are often blurred (Ridder 2017 ). In theory testing, the phenomenon is understood, and the research strategy focuses on testing if the theory also holds under different circumstances, i.e., hypotheses can be formed and tested based on existing theory (Ridder 2017 ). In multiple case study research, theory testing uses theoretical replication with pattern matching or addressing rival explanations (Ridder 2017 ). In our MMCS publications, no publication addresses rival explanations, and only a few apply theoretical replication and pattern matching–yet not for theory testing. A few publications claim to test propositions derived from an a priori theoretical framework (e.g., Schäfferling et al. 2011 ; Spiegel and Lazic 2010 ; Wagner and Ettrich-Schmitt 2009 ). However, these publications either do not state their replication logic (e.g., Spiegel and Lazic 2010 ; Wagner and Ettrich-Schmitt 2009 ) or use a literal replication (e.g., Schäfferling et al. 2011 ), both of which weaken the value of their theory testing.

5.1.3 MMCS research benefits

MMCSs are beneficial in multiple research situations and can be an avenue to address the frequent criticism of multiple case study research of being time-consuming and costly (Voss et al. 2002 ; Yin 2018 ).

Firstly, MMCSs can be used for time-critical topics where it is beneficial to publish results quicker and discuss them instead of conducting in-depth multiple case studies (e.g., COVID-19 (e.g., dos Santos Tavares et al. 2021 ) or emergent technology adoption (e.g., Bremser 2017 )). Especially with COVID-19, research publishing saw a significantly higher speed due to special issues of journals and faster review processes. Further, due to the fast technological advancements, there is a higher risk that the results are obsolete and of less practical use when researched with time-consuming multiple in-depth case studies.

Secondly, MMCSs can be used in research situations when it is challenging to gather in-depth data from multiple sources of evidence for each case due to the limited availability of sources of evidence or limited accessibility of sources of evidence. When researching novel phenomena (e.g., the adoption of new technologies in organisations), managers and decision-makers are usually interviewed as sources of evidence. However, in most organisations, only one (or very few) decision-makers have the ability to inform and should be interviewed, limiting the potential sources of evidence per case. These decision-makers often have limited availability for multiple in-depth interviews. Furthermore, the sources of evidence are often difficult to access, as professional organisations have regulations that prevent sharing documents with researchers.

Thirdly, MMCSs can be beneficial when the research framework is complex and requires many cases for validation (e.g., Baker and Niederman ( 2014 ) validate their rather complex a priori framework with 22 cases) or when previous research has led to contradictory results . Therefore, in both situations, a higher breadth of cases is required to also research combinatorial effects (e.g., van de Weerd et al. 2016 ). However, conducting an in-depth multiple case study would take time and effort. Therefore, MMCSs can be a mindful way to collect many cases, but in the same vein, being time and cost-efficient.

5.2 MMCS research rigour

Table 8 outlines two types of methodological steps for MMCSs. The first are methodological steps, where MMCSs should follow multiple case study methodological guidance (e.g., clarify the unit of analysis ), while the second is unique to MMCSs due to its characteristics. This section focuses on the latter, exploring MMCS characteristics, problems, validity threats, and proposed solutions.

The characteristics of MMCSs of having only one primary source of evidence per case prevents MMCSs from using source triangulation, which is often used in multiple case study research (Stake 2013 ; Voss et al. 2002 ; Yin 2018 ). By only having one source of evidence, researchers can fail to develop a sufficient set of operational measures and instead rely on subjective judgements, which threatens construct validity (Yin 2018 ). The threats to construct validity must be addressed throughout the MMCS research process. To do so, we propose to use easily accessible supplementary data or other triangulation approaches to increase construct validity in a MMCS. For the other triangulation approaches, we see that the majority of publications use supplementary data (e.g., publicly available documents) as further sources of evidence, multiple investigators, multiple methods (e.g., quantitative and qualitative), multiple theories, or combinations of these (cf. Tables 5 , 6 and 7 ). Having one or, in the best case, all of them reduces the risk of reporting spurious relationships and subjective judgements of the researchers, as a phenomenon is analysed from multiple perspectives. Besides the above-mentioned types of triangulation, we propose to apply a new type of triangulation, which is specific to MMCSs and triangulates findings across similar cases combined to groups instead of multiple sources per case. We propose that all reported findings have to be found in more than one case in a group of cases. This is also in line with previous methodological guidelines, which suggest that findings should only be reported if they have at least three confirmations (Stake 2013 ). To triangulate across multiple cases in one group, researchers have to identify multiple similar cases by applying a literal case replication logic to reinforce similar results. One should also apply a theoretical replication to compare different groups of literally replicated cases (i.e., searching for contrary results). Therefore, researchers have to justify their case replication logic . However, in our sample of MMCS, the majority (32 of 50, 64%) does not justify their replication logic, whereas the remaining publications use either literal replication (8 of 50, 16%), theoretical replication (6 of 50, 12%), or a combination (4 of 50, 8%). We encourage researchers to use a combination of literal and theoretical replication because it allows triangulation across different groups of cases. An exemplary MMCS that uses this approach is the publication of van de Weerd et al. ( 2016 ), who use theoretical replication to find cases with different outcomes (e.g., adoption and non-adoption) and use literal replication to find cases with similar characteristics and form groups of them.

Two further methodological steps, which are not exclusive to MMCS but recommended for increasing the construct validity, are creating a chain of evidence and letting key informants review a draft of the case study report . Only 36% (18 out of 50) of the analysed MMCS publications establish a chain of evidence. One reason for this lower usage may be that the majority (35 out of 50, 70%) of the publications analysed are conference proceedings. While we understand that these publications face space limitations, we note that no publication offers a supplementary appendix with in-depth insights. However, we encourage researchers to create a full chain of evidence with as much transparency as possible. Therefore, online directories for supplementary appendices could be a valuable addition. As opposed to a few years ago, these repositories today are widely available and using them for such purposes could become a good research practice for qualitative research. Interestingly, only 16% (8 of 50) analysed MMCS publications let key informants review the draft of the case study report . As MMCSs only have one source of evidence per case, misinterpretations and subjective judgement by the researcher have a significantly higher impact on the results compared to multiple case study research. Therefore, MMCS researchers should let key informants review the case study report before publishing.

MMCSs only have few (one) sources of evidence per case, so the risk of focusing on spurious relationships is higher, threatening internal validity (Dubé and Paré 2003 ). This threat to internal validity must be addressed in the data analysis phase. In the context of MMCSs, researchers may aggregate fewer data points to obtain a within-case overview. Therefore, having a clear perspective of the existing data points and rigorously applying the within-case analysis methodological steps (e.g., pattern matching) is even more critical. However, due to the limited depth of data at MMCSs, the within-case analysis must be combined with an analysis across groups of cases (to allow triangulation via multiple groups of cases). For MMCSs, we propose not doing the cross-case analysis on a per-case basis. Instead, we propose to build groups of similar cases across which researchers could conduct an analysis across groups of cases. This solidifies internal validity in case study research (Eisenhardt 1989 ) by viewing and synthesising insights from multiple perspectives (Paré 2004 ; Yin 2018 ).

Another risk of MMCSs is the relatively high number of cases (i.e., we found up to 27 for exploratory MMCSs) that is higher than Eisenhardt’s ( 1989 ) recommendation of maximal ten cases in multiple case study research. With more than ten in-depth cases, researchers struggled to manage the complexity and data volume, resulting in models with low generalisability and reduced external validity (Eisenhardt 1989 ). We propose to use two methodological steps to address the threat to external validity.

First, like Yin’s ( 2018 ) recommendation to use theory for single case studies, we suggest an a priori theoretical framework for MMCSs. 84% (42 out of 50) of the analysed MMCS publications use such a framework. An a priori theoretical framework has two advantages: it simplifies research by pre-defining constructs and relationships, and it enables analytical techniques like pattern matching. Second, instead of doing the within and then cross-case analysis on a per-case basis, for MMCSs, we propose first doing the within-case analysis and then forming groups of similar cases. Then, the cross-case analysis is performed on the formed groups of cases. To form case groups, replication logic (literal and theoretical) must be chosen carefully. Cross-group analysis (with at least two cases per group) can increase the generalisability of results.

To increase MMCS reliability, a case study database and protocol should be created, similar to multiple case studies. To ensure higher reliability, researchers should document MMCS design decisions in more detail. As outlined in the results section, the documentation on why design decisions were taken is often relatively short and should be more detailed. This call for better documentation is not exclusive to MMCSs, as Benbasat et al. ( 1987 ) and Dubé and Paré ( 2003 ) also criticised this for multiple case study research.To ensure rigour in MMCS, we suggest following the steps for multiple case study research. However, MMCSs have unique characteristics, such as an inability to source triangulate on a per-case level, a higher risk of marginal cases, and difficulty in managing a high number of cases. Therefore, for some methodological steps (cf. Table 8 ), we propose MMCS-specific methodological options. First, MMCS should include supplementary data per case (to increase construct validity). Second, instead of doing a cross-case analysis, we propose to form groups of similar cases and focus on the cross-group analysis (i.e., in each group, there must be at least two cases). Third, researchers should justify their case replication logic , i.e., a combination of theoretical replication (to form different groups) and literal replication (to find the same cases within groups) should be conducted to allow for this cross-group analysis.

6 Conclusion

Our publication contributes to case study research in the IS discipline and beyond by making four methodological contributions. First, we provide a conceptual definition of MMCSs and distinguish them from other research approaches. Second, we provide a contemporary collection of exemplary MMCS publications and their methodological choices. Third, we outline methodological guidelines for rigorous MMCS research and provide examples of good practice. Fourth, we identify research situations for which MMCSs can be used as a pragmatic and rigorous approach.

Our findings have three implications for research practice: First, we found that MMCSs can be descriptive, exploratory, or explanatory and can be considered as a type of multiple case study. Our set of IS MMCS publications shows that this pragmatic approach is advantageous in three situations. First, for time-sensitive topics, where rapid discussion of results, especially in the early stages of research, is beneficial. Second, when it is difficult to collect comprehensive data from multiple sources for each case, either because of limited availability or limited accessibility to the data source. Third, in situations where the research setting is complex, many cases are needed to validate effects (e.g., combinatorial effects) or previous research has produced conflicting results. It is important, however, that the pragmatism of the MMCS should not be misunderstood as a lack of methodological rigour.

Second, we have provided guidelines that researchers can follow to conduct MMCSs rigorously. As we observe an increasing number of MMCSs being published, we encourage their authors to clarify their methodological approach by referring to our analytical MMCS framework. Our analytical framework helps researchers to justify their approach and to distinguish it from approaches that lack methodological rigour.

Third, throughout our collection of MMCS publications, we contacted several authors to clarify their case study research methodology. In many cases, these publications lacked critical details that would be important to classify them as MMCS or marginal cases. Many researchers responded that some details were not mentioned due to space limitations. While we understand these constraints, we suggest that researchers still present these details, for example, by considering online appendices in research repositories.

Our paper has five limitations that could be addressed by future research. First, we focus exclusively on methodological guidelines for positivist multiple case study research. Therefore, we have not explicitly covered methodological approaches from other research paradigms.

Second, we aggregated methodological guidance on multiple case study research from the most relevant publications by citation count only. As a result, we did not capture evidence from publications with far fewer citations or that are relevant in specific niches. However, our design choice is still justified as the aim was to identify established and widely accepted methodological strategies to ensure rigour in case study research.

Third, the literature reviews were keyword-based. Therefore, concepts that fall within our understanding of MMCS but do not include the keywords used for the literature search could not be identified. However, due to the different search terms and versatile search approaches, our search should have captured the most relevant contributions.

Fourth, we selected publications from highly ranked IS MMCS publications and proceedings of leading IS conferences to analyse how rigour is ensured in MMCSs in the IS discipline. We therefore excluded all other research outlets. As with the limitations arising from the keyword-based search, we may have omitted IS MMCS publications that refer to short or mini case studies. However, the limitation of our search is justified as it helps us to ensure that all selected publications have undergone a substantial peer review process and qualify as a reference base in IS.

Fifth, we coded our variables based on the characteristics explicitly stated in the manuscript (i.e., if authors position their MMCS as exploratory, we coded it as exploratory). However, for some variables, researchers do not have a consistent understanding (e.g., the discussion of what constitutes exploratory research by cf., Sarker et al. ( 2018 )). Therefore, we took the risk that MMCS may have different understandings of the coded variables.

For the future, our manuscript on positivist MMCSs provides researchers with guidance for an emerging type of case study research. Based on our study, we can identify promising areas for future research. By limiting ourselves to the most established strategies for ensuring rigour, we also invite authors to enrich our methodological guidelines with other, less commonly used steps. In addition, future research could compare the use of MMCSs in IS with other disciplines in order to solidify our findings.

Data availability

Provided at https://doi.org/10.6084/m9.figshare.24916458

The information can be found in the online Appendix: https://doi.org/10.6084/m9.figshare.24916458 .

litbaskets.io is a web interface that allows searching for literature across the top 847 IS journals. It offers ranging from 2XS (Basket of Eight) to 3XL (847) essential IS journals and a full list of 29 journals which are the basis for this study can be found in Appendix C ( https://doi.org/10.6084/m9.figshare.24916458 ).

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Bridging the gap between research evidence and its implementation in public health practice: case studies of embedded research model

• Abisope Akintola 1 , 3 ,
• Dorothy Newbury-Birch 2 &
• Stephanie Kilinc 2  

BMC Public Health volume  24 , Article number:  1299 ( 2024 ) Cite this article

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Metrics details

To investigate the potential of embedded research in bridging the gap between research evidence and its implementation in public health practice.

Using a case study methodology, semi-structured interviews were conducted with 4 embedded researchers, 9 public health practitioners, and 4 other stakeholders (2 teachers and 2 students) across four case study sites. Sites and individuals were purposively selected. Sites included two local authorities, one secondary school, and one sports organisation. Thematic data analysis was adopted to analyse the qualitative data.

Four themes were identified: (1) building and maintaining relationships, (2) working with stakeholders, (3) informing practice, and (4) critical reflection.

Conclusions

Embedded researchers build and maintain relationships with practitioners and other stakeholders to produce research. Evidence from the co-produced research informs future practice and research to improve service and delivery rendered to the public. Thus, embedded researchers use their role to bridge the research evidence - implementation gap in public health practice.

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Implementation science is widely recognised as a study of methods to adopt and utilise evidence-based interventions in specific locations or settings to improve the health of the population [ 1 ]. However, the gap between research evidence and its implementation in public health practice is still globally recognised [ 2 ]. According to scholars, some of the factors associated with the problem of inadequate implementation of research evidence in practice could either originate from the researchers or the practitioners [ 3 , 4 , 5 ]. This implies that both researchers and practitioners could be responsible for the creation of the gap between research evidence and its implementation in public health practice.

Evidence suggests that lack of access to research evidence is one of the barriers to the implementation of research evidence in practice [ 6 , 7 , 8 ]. One report suggests that increased connectivity between researchers and practitioners would enhance the practitioners’ accessibility to research evidence [ 9 ]. The report explained further that creating some forums where practitioners and researchers could interact would not only bring about easy access to relevant research evidence, but also would serve as a means to share learning, and link researchers and practitioners who have a common interest. Similarly, other scholars report that increasing the interaction between researchers and practitioners among other factors could facilitate the use of research-based evidence in practice [ 10 , 11 ]. To that end, there is a need to increase the opportunities for practitioners and researchers to interact in order to facilitate the utilisation of research evidence in public health practice.

As there are many identified barriers to the use of research evidence in practice, the disparity between the context and the language by which researchers and practitioners operate has also been identified as one of the barriers. The incompatibility in the language spoken by the researchers with respect to the scientific methods and the evidence generated could be ambiguous for practitioners [ 12 ]. Therefore, to overcome this challenge, scholars advise that practitioners and researchers should work collaboratively from the onset of the research while putting into consideration each other’s differences [ 13 , 14 ]. Furthermore, it has been recommended that researchers need to present their research findings and explain the relevance to solving practical problems to the practitioners in a simple language without ambiguity [ 15 ]. This suggests a need for an approach that would involve practitioners and researchers undertaking the research agenda together, and also a need for effectively communicating research findings and their relevance in a simple language to the practitioners.

The context in which the researchers operate could also serve as a challenge to the utilisation of research evidence in practice [ 9 ]. As such, competing pressures such as teaching commitments and publishing academic papers [ 16 ] could pose a challenge to the researchers’ involvement in practical problems that could inform their research questions. Hence, there is a need for an approach for researchers to be more involved in practical problems to facilitate the conduction of research that is relevant and applicable to problem solving. It was noted that not all researchers have the relevant skills to conduct co-produced research [ 17 ]. There is a need to create opportunities for researchers who have relevant skills to co-produce research, to conduct research with suitable practitioners.

On the other hand, organisational factors such as time constraints are contributing factors to the gap between research evidence and practice as most practitioners do not have the skills nor the time needed to implement research outcomes in practice [ 18 ]. To tackle these challenges, some studies recommend continuous training and commitment to quality health delivery on the part of practitioners. They also recommended advancements in technological decision support systems as instruments to combat barriers between research evidence and practice [ 19 , 20 ]. There is an argument that achieving these may be difficult as a result of inadequate funds in health services [ 21 ]. Hence, there is a need for the adoption of a method that will bring about building the capacity of the practitioners towards conducting research that is achievable based on the available budget.

Furthermore, the disparity of influence and power between academics and practitioners could be responsible for the wide gap between research and practice [ 22 ]. This means the relationship between academic researchers and practitioners plays a vital role in the use of research evidence. Therefore, there is a need for a method that would enhance or build mutually beneficial relationships between academic researchers and practitioners to bridge the ‘research evidence-implementation’ gap.

The separation of the development of research evidence from the places it is to be used contributes to the challenges of using research evidence in practice [ 23 ]. This implies that the creation of research knowledge where it is to be utilised could bridge the ‘research evidence-implementation’ gap. As such, co-production has been recommended by scholars to bridge the ‘research evidence-implementation’ gap as co-production involves the collaborative working between the researchers and the practitioners [ 24 ]. Hence, the adoption of co-production to produce public health knowledge by researchers, practitioners, and other stakeholders in non-clinical settings [ 13 , 25 ]. This is essential in tackling the challenges of inadequate implementation of research evidence in public health settings.

Being involved in co-production could result in reputational risk for the researcher involved as the researcher could be used by politicians to enhance authenticity to their political stand [ 26 ]. Thus, being viewed to approve such a political stand can limit the researcher’s ability to work only with a certain political group – this can also impact the researcher’s personal safety [ 27 ]. Also, this can impact negatively on the credibility of the co-production findings as it might be viewed as biased and not a true representation but a narrative to back up a political viewpoint, thus generating “policy-based evidence” [ 28 ] rather than “research-based evidence”. On the other hand, policy-makers might be at risk of sharing sensitive information while participating in co-production work [ 29 ] such as disclosing political errors.

Also, co-production can be costly as it usually involves the stakeholders travelling to the co-production site. This could be viewed as challenging for those that are involved in the co-production project, as their presence at meetings for the co-production work is seen as crucial. Also, funding and sustainability of co-production can pose a great risk to the adoption of co-production [ 48 ]. However, the challenges associated with co-production can be overcome if stakeholders are involved and are carried along at every stage of co-production, from design to implementation [ 30 ]. The success of co-production depends on but is not limited to the following: the individuals involved; how clear the aims and objectives of the project are to all those involved, and how duties are allocated [ 31 ]. This also suggests a need to critically analyse the role of stakeholders involved in co-production to overcome the challenges associated with co-production, to achieve success.

Embedded research, also known as ‘researcher-in-residence’, is becoming popular as a type of co-production research [ 3 ]. Different authors used different terminologies for embedded researchers such as insider researcher [ 32 ], knowledge broker [ 33 , 34 ], or scholar-practitioner [ 35 ]. Within an embedded research model, one of the distinguishing features is that the researcher is located in the host organisation as a member of staff to carry out a research agenda with the host organisation’s staff, and at the same time maintaining affiliation with an academic institution [ 36 , 37 , 38 , 39 ]. In this paper we investigate how an embedded research model can help bridge the gap between research evidence and its implementation in public health practice.

We conducted qualitative case studies and drew data from semi-structured interviews with four embedded researchers, nine public health practitioners, and four other stakeholders (two teachers and two students) across four case study sites including two local authorities (Sites one and two), one secondary school (Site three), and one sports organisation (Site four) in the Northeast of England.

One of the advantages of qualitative research is the ability to generate rich in-depth data or knowledge that can serve as a basis for health and social practices being effective and relevant to the contexts they are applied to [ 40 ]. We adopted a qualitative multi-site case study to understand the context by providing in-depth description and analysis within sites and as well by comparing data between sites in order to identify the similarities and differences between the sites explored [ 41 ]. Thus, this will assist to maximise the applicability of the findings on how an embedded research model can help bridge the gap between research evidence and its implementation in other similar settings.

In site one, the embedded research project aimed to understand and make recommendations regarding population changes, and service needs, including health, education, housing, and social care, in the local communities. In site two, an embedded researcher works at the local authority to provide research support to the local authority’s public health team to secure their targets which include commissioning evidence-based services and interventions, and promotion of healthy lifestyles. Site three conducted an embedded research project to explore the academic and health impact of the recent changes to the General Certificate of Secondary Education (GCSE) system on both staff and students. Site four was established to encourage more people to engage in physical activities to improve their health and well-being. In order to improve the service rendered to the public, an embedded researcher was employed in site four to co-produce research with the sports organisation members of staff. All the embedded researchers across the four case study sites were PhD holders. The amount of time spent in their respective host organisations varied from one hour per fortnight to two and a half days a week to suit the embedded researchers and the host organisations. The embedded researchers’ positions were funded either by the University they are affiliated with, or their host organisation.

Purposive snowball sampling was used in this study. Requests for participants and sites who could volunteer to be part of the study were sent out via relevant professional contacts and networks. The participants and sites that volunteered to take part in this research were asked to assist in the search for participants and/or sites by circulating the study’s details to those who might meet the study’s criteria and would be willing to take part in the study. The inclusion criteria were: (1) being a public health embedded researcher, and (2) being a public health practitioner or stakeholder who is working or has worked with a public health embedded researcher. Potential participants were assessed for eligibility before being interviewed. A total of 17 participants were recruited for the interviews across the four case study sites. The sample size would have been larger than 17 but for the Covid-19 pandemic. Ethical approval was obtained from the Teesside University School of Health and Life Sciences Research Governance and Ethics Committee in November 2019. Data was collected between November 2019 and April 2020.

To facilitate participation, participants were offered alternative modes of interview for their convenience: face-to-face, telephone, and Skype-based interviews. The Covid-19 pandemic occurred during the interview period, but most interviews conducted before COVID-19 were face-to-face. All interviews conducted during the pandemic (March 2020 and onwards) were either Skype or telephone-based, as advised by the Ethics department at Teesside University and as per the requirements of the interviewees’ workplaces. Before each interview, oral and written informed consent was obtained from each participant. Each participant was asked to complete two copies of the consent form, one for their own records and one for the researcher.

Following each interview, a reflective note was taken to identify what went well and what could be done differently in the next interview. Since there were three categories of interview participants – embedded researchers (ERs), public health practitioners (PHPs), and other stakeholders (students (STs) and teachers (TRs)–three sets of interviews were prepared. Although the interview questions were nearly the same for each category of participants, some of the interview questions differed in the way they were structured. Here is an example of how a question was worded differently depending on the participant: (ERs) Can you cite an example where you have built practitioners and other stakeholders’ confidence to conduct their own research? (PHPs, TRs, and STs) Can you cite an example where an embedded researcher has built your confidence to conduct your own research? A full outline of the interview guide is in Appendix .

A summary of each interview was noted in a research diary for reference. Details noted included where each interview took place, the date of the interview, the length of the interview and how the interviewee responded to questions. Each interview lasted between 40 and 90 min. The interviews were recorded, and data was transcribed. We analysed data using inductive thematic analysis [ 42 ] to allow new themes besides the preconceived ones to emerge from the coding of the interviews. Trustworthiness of the analysis was assessed by triangulating between data sources.

Four themes emerged from the analysis of the interview data on the potential of embedded research in bridging the gap between research evidence and its implementation in public health practice: (1) building and maintaining relationships (2) working with stakeholders, (3) informing practice, and (4) critical reflection.

Building and maintaining relationships

All participants across the four case study sites, irrespective of their age, years of experience, or education, recounted the significance of this theme to the embedded research projects in their respective sites. They articulated the benefits of the role of the embedded researchers in building and maintaining relationships with the public health practitioners and other stakeholders to facilitate the co-production of research evidence. They all agreed that building and maintaining relationships played a vital role in the utilisation of the co-produced research evidence and in the closing of the gap between research evidence and its implementation. Overall, the strategies adopted by the embedded researchers to achieve this theme were identified as: (1) building internal/external relationships and sharing skills, and 2) maintaining regular contact with practitioners and other stakeholders.

Building internal/external relationships and sharing skills

Participants agreed that the embedded researchers’ role entails having diverse connections built on good relationships. These relationships assist the embedded researchers in connecting their partners to other relevant organisations such as academic institutions and third sector agencies.

“I think some of that is around having this kind of good grounding so sort of beginning the role with already having made, a lot of kind of contacts, a lot of sort of good relationships been built. [..] I have a line manager in the council, who was the project manager for the first phase so we’ve got that continuity there [..] I also have an academic supervisor who is also my kind of my line manager from the academic side” [ERsite1] .

“I can say that’s [having connections] actually key because they are straddling both worlds. [..] not somebody who sat in the academic institution who didn’t understand the wider context. I think these roles are really key in bridging the institutions” [PHP2site1] .

It was clear that building relationships and connecting the ‘two worlds’ is not only advantageous to both institutions but also assisted the embedded researchers to seek support from both their academic supervisor at the University they were employed and the local authority (LA) they are working with. Therefore, this enables the embedded researchers to be supported fully to carry out their role successfully. It was also recognised that while embedded researchers play their role in building relationships and connecting relevant organisations, the role assisted them to understand the context in which research evidence is to be utilised. Thus, the relevance of research evidence to the host organisation facilitates its use.

This relationship-building was seen as crucial to the success of the role, and it was felt that these relationships could determine the success of any work carried out.

“[..] I would go as far to say I think it’s the relationship that’s built with the individuals who developed that project was important. [..] are the most important elements of co-production” [ERsite2] .

This implies that lack of relationship-building between researchers and public health practitioners can serve as a barrier to embedded research project. Furthermore, it was evident that the relationship built with the stakeholders who were involved in the embedded research was crucial to the projects. For instance, an embedded researcher from site two used her skills to build relationships with the volunteers that participated in the project.

“She [embedded researcher] has been there longer, excellent relationships with the volunteers, that helped to build and shape this project, so she has a very useful experience in terms of relationship-building” [PHP6site2] .

Thus, this assisted in structuring the work which had a positive impact on the project. This two-way relationship with other organisations, including the local universities and research participants, was seen as a benefit of embedded research.

Findings showed that embedded researchers used their contacts and good relationships to facilitate the sharing of skills useful in carrying out embedded research projects and also enable working with other academics at the University.

“[..] even for me just working as an individual in that organisation, I don’t know everything about the research, but because you are linked with the University, that gives an avenue to ask questions and link up with people with expertise to then support an evaluation” [ERsite2] .

These connections and relationships, therefore, enable the sharing of skills useful to co-produce relevant high-quality research evidence useful to host organisations and policy makers.

Within this current study, it was clear that if the embedded researchers were not located or had spent time in the sites, they felt it would be difficult for them to build relationships, and understand the context in which the co-produced research is to be utilised.

“So, having the researcher embedded within in what we do, the researcher has the understanding of the project, and initially she has been with it from the start to finish, so she understands the journey that’s been on, and she understands why it’s been done, how it’s been done [..] So, I think, so the embedded researcher role in what we do is infallible resource really” [ PHP1site4 ] .

The ‘embeddedness’ gave the researchers an understanding of the projects they were involved in. As such, the embedded researchers were seen as ‘insiders’ and their ‘embeddedness’ was seen as key to the success of the work.

It is worth noting that the amount of time spent by the embedded researchers in their respective host organisation varied and was negotiated at the sites to suit the embedded researchers and the host organisations.

“[..] I was familiar with quite a lot of people but obviously kind of being there regularly I have got to know them much better basically. [..] I mean it really varies; I would say probably kind of at least a couple of days in a week” [ERsite1] .

“Being embedded within their team I spend half of the week working within the organisation. It’s been a real pleasure to work alongside them” [ERsite2] .

“ So, we tend to have meetings where I will go in for a few hours at a time. I would probably say, maybe an hour in a fortnight ” [ERsite3] .

“[..] I spend two and a half days working within the organisation. [..] you want to be seen as part of that team and not somebody who just pops up every now and again” [ERsite4] .

However, building relationships and sharing skills was not seen as without its challenges with some tension between roles and expectations.

“[..] it has become trickier splitting myself now between the organisations as they all have their roles and expectations on how they want things to be done” [ ERsite2] .

“The structure can be quite challenging as well, but probably [..] just having that balance in the relationships with the organisation you are working for and the organisation you are evaluating for. And I think yeah you have got to have that one, but that is a challenge of working in large organisation” [PHP6site2] .

The embedded researchers from sites one and two found there was some tension in working in both ‘worlds’ as a result of the responsibilities associated with it, such as building relationships, and balancing diverse responsibilities. This is due to their dual affiliation as such, they are expected to manage a large workload, managing both successfully. A practitioner from site two added that the structure of the organisations the embedded researcher works could also be a challenge, therefore, it is important for an embedded researcher to be able to discuss this with both sides in order that they balance the relationships between the host organisation and the academic institution.

Another notable challenge is having to manage diverse expectations including the ability to balance competing interests of the different organisations.

“There is sort of difference in expectations because I think from the academic point of view, [..] we want publications, we want things that give us an academic output, whereas someone who works in the school is not going to be bothered about that sort of things. They have to see where it positively affects their school, [..] so I think having that difference in agendas on what you want to achieve from this school research can be quite hard to manage. [..] you want different things from this piece of research is quite hard, and make sure that both sides are happy at the end of the day, and I think we did that quite well” [ERsite3] .

For instance, an embedded researcher from the school stated that the expectations from the embedded research project did differ. That is, while part of the aim of the academic input was to publish the outcome of the project to improve or boost their academic output, the school aimed for a practical positive impact of the project on the school, such as improvement in students’ engagement in academic activities. Hence, it was essential to balance the competing interests of the school and the academic side of the embedded research project.

Maintaining regular contact with practitioners and other stakeholders

Based on the participants’ experiences, the embedded researchers built relationships with the practitioners and other stakeholders by maintaining regular contact.

“I think what we did was to help build that relationship. It was not just a telephone conversation just to discuss. We actually worked side by side so there was time to actually do that embedded research. We spent time in the office, we spent like one or two days a week” [PHP1site2] .

“Yeah, but then we did send them emails and stuff, in between [..] yeah we did have time outside of the face to face sessions and sending stuff to the teachers to encourage them, ‘can you remind the students that we have got to do this week’, we have got to get this done by then, so I would say obviously we had the face to face sessions but then we had email correspondence as well” [ERsite3] .

The practitioners from site two reported that the embedded researcher maintained regular contact by face to face, or by telephone. They further explained that they worked side by side with the embedded researcher to build relationships. This implies that if the practitioners and the embedded researcher were not chanced to work together, which assisted in maintaining regular contact, it would have been difficult to build relationships. Thus, this widens the gap between academia and practice. The embedded researchers had similar experiences. For instance, an embedded researcher from site three (school) confirmed that she maintained regular contact to build relationships with the students and the teachers by email and face to face. This shows that it is important to develop project strategies in order to maintain regular contact with the practitioners and other stakeholders to build relationships.

According to the embedded researchers, building mutually beneficial relationships was achieved by maintaining regular contact not only with the stakeholders but also with their academic supervisors which enabled the embedded researchers to have the necessary support to achieve their role.

“I mean knowing that I do have kind of the support at the University to draw on and also have a kind of a good working relationship with my line manager in the council as well really. I don’t feel that I am lacking in any kind of support, which is a good kind of place to be in yeah. So I have monthly meetings in the University and that’s very much really useful in times of keeping track of some of the other parts of my roles so around kind of trying to ensure that we can get some like academic publications and things like that so yeah” [ERsite1] .

Another strategy that was mentioned regarding how the embedded researchers maintained regular contact to build relationships with the practitioners and other stakeholders was ‘attending formal meetings’.

“Interestingly, the researcher has always been on the co-production committee and she attends the meetings, so she is excellent, much better than me because she has been there longer, [..] that helped to build and shape this project [..]” [PHP6site2] .

“So, I have to go to all their team meetings that’s gonna help you form a lot of relationships. Meetings are where the real connection starts to happen. So, you have to invest that time ” [ERsite4] .

As well as making use of formal meeting, the embedded researchers adopted ‘informal conversations’ to maintain regular contact to build relationships with the public health practitioners and other stakeholders.

“For me, I am quite like a chatty person and I think that’s like the characteristics of an embedded researcher. You need somebody who is easy to get on with lots of different people. You need to have that ability to do that. Otherwise, you gonna struggle to form a relationship especially if you aren’t there as often as what you would be if it’s a full-time job” [ ERsite4] .

A practitioner from the sports organisation added that engaging in informal conversations also helped in building a trustworthy relationship with the embedded researcher.

“[..] We have that relationship and some other things you can visit, particularly when things get tough, it’s easy enough to fall back on different conversations on sport [..] These conversations increase our relationship and trust, we trust each other” [PHP1site4] .

The practitioner further explained that he has a good relationship with the embedded researcher and so they engage in informal conversations at difficult times thereby developing a relationship that is based on trust.

Working with stakeholders

Results showed that the embedded researchers build and maintain relationships with the practitioners, and with other stakeholders in order to effectively work together to produce research. This, therefore, facilitated the production and the use of the co-produced research evidence at the embedded sites and helped close the gap between research evidence and its implementation as results were shared quickly with all those that were involved. All participants across the four case study sites unanimously agreed that this theme is one of the primary roles of an embedded researcher, and the strategies identified include: (1) co-producing research, and (2) building research capacity.

Co-producing research

The participants confirmed that they worked together to identify, plan, and conduct research intended to help the host organisations improve their services and meet the needs of the communities with which they work.

“We liaise with the researcher to develop the initial kind of overview of that population [..] the researcher supports us in developing the initial questions, the questionnaire, and the initial research” [PHP1site4] .

“[..] embedding research into the public health team. [..] then helping us to explore the questionnaires. The embedded researcher helps us with the development of that work including the formulae and evaluation for the intervention. We design and develop and embed and undertake the research together. She is very much a part of the team and a core within the team” [PHP4site2] .

“[..] So, really it’s about giving us the exposure to that sort of research. Well, honestly, I have learnt how to conduct research” [ST1site3] .

The participants acknowledged that working together to co-produce research with the embedded researchers encouraged adjustments to and engagement with research-related activities. Furthermore, embedded research was considered a cost-effective research approach.

“ I have been out in a couple of beneficiary interviews with the researcher. Certainly, I would not normally get involved with going out to see clients, but I have gone out a couple of times with the researcher, so that was interesting” [PHP5site2] .

“[..] the embedded researcher worked alongside the public health practitioners [..] how to shape some of the evaluations, including how to be really clear about the methodology, the approach [..] And how to write protocol [..] So, I think that was the aim of it, it was to ensure that we have much more effective and cost-effective research ” [PHP2site1] .

One public health practitioner reported that she participated in several research activities with the embedded researcher at site two. She recognised that working with the researcher enabled her to do research work that she would not have ordinarily done. This suggests that not working together with practitioners to co-produce research may potentially prevent practitioners from being meaningfully involved in the research process. In such situations, the gap between the development and implementation of research evidence may actually become wider. One practitioner from site one explained that embedded research was adopted in the LA so that the authority could conduct cost-effective research. This only further indicates that having an embedded researcher on-site working collaboratively with practitioners and stakeholders to conduct cost-effective research can help bridge the research implementation gap.

However, it was noted that the process of co-producing research between the embedded researchers and the public health practitioners and other stakeholders also facilitated shared learning.

“Despite the fact that we went in obviously thinking of teaching them but the fact that we can learn from them about what was important to them, what was important to young pupils in schools, and how to speak to young pupils because that is schooling in itself. [..] and I think also you learn new skills [..] so I think you get sort of practical experience and learn new skills sort of more practical skills I suppose, not just research skills, so yeah that is why I think I say it’s the most important thing” [ERsite3] .

“[..] and when I have been out with staff members, they will ask questions that I would never have thought of asking, because of their knowledge at work. [..] I have been learning a lot as well from the staff, and that shows the importance of doing it together” [ ERsite2] .

One embedded researcher from site three (school) reported that although their aim was to teach the students how to conduct research, they were able to learn what was important to the young people among other things from the students. Another embedded researcher from site two shared a similar experience and confirmed that during the co-production work, the public health practitioners used their tacit knowledge of their field to ask relevant questions that had not occurred to her. Since the practitioners are more knowledgeable than the researcher regarding actual on-site practices, they added substantial value to the project. This indicates just how much learning is a two-way process, and demonstrates co-production of knowledge which involves the amalgamation of the practitioners’ tacit knowledge and the researchers’ explicit knowledge.

Researchers were explicitly recognised for their ability to co-produce research with the public health practitioners and other stakeholders. Thus, the co-produced research was jointly owned by those involved in the embedded research projects. As the research was co-produced with the intention to assist the organisations to improve the service they render to the public, thus, the embedded researchers’ role assisted in facilitating the utilisation of research evidence. In addition, given the embedded research projects focused on meeting the needs of the host organisations, there were no instances where there were conflicts related to the research emerged.

Building research capacity

The embedded researchers explained that they conducted training, and other developmental activities to help develop the practitioners’ and other stakeholders’ research skill-set.

“I have done a kind of number of training sessions with staff and actually with volunteers that will want to get involved in collecting data [..] so I have run workshops, training workshop, so that means that when I go out there for collection the staff can come and do it with me” [ERsite4] .

“[..] another element of my role is to deliver training to staff around the use of data around the benefits of collecting relevant information, how that information can be used to inform practice in decisions and planning and things like that, we just had a conference couple of weeks ago which was very much about kind of sharing the learning and then sort of getting people involved in the work that we do really, so they are my kind of key targets really” [ ERsite1] .

Research-based training were offered by the embedded researchers in a variety of forms, such as using workshop training, one to one training and through seminars and conferences. For instance, an embedded researcher from site four (sports organisation) reported that she taught the practitioners to collect data at a training workshop that she organised. She explained that this training assisted the embedded research project because it helped the practitioners to get involved in the data collection phase as they had the skills from the training. Similarly, another embedded researcher from site one reported that getting the practitioners involved in the embedded research work facilitated the sharing of learning, which was one of her main goals while working at the LA. This particular researcher trained the public health practitioners to collect data and taught them how research evidence can inform practical decision making.

The participants agreed that working together with the embedded researchers strengthened their ability to conduct high-quality research capable of benefiting their respective organisations.

“ It also allowed us to utilise and build the capacity of public health practitioners who would often not undertake any research for some time” [PHP2site1] .

“So, it’s more like continuous professional development [..] So, the research skills are learnt such that at the end of the day, next time the research could be conducted independently, even if we didn’t have somebody coming from the outside. That’s the whole approach [..] is for developing public health practitioners to the extent that research can be conducted in a rigorous manner” [PHP1site1] .

“I think probably when I attended two beneficiary interviews with her and just seeing how to speak to people when you are asking them questions so there is a way to ask the questions so that they understand, probably by listening to the researcher at that point I sort of learnt how” [ PHP5site2] .

As the above suggests, the embedded researchers encouraged some practitioners who would ordinarily not participate in research to engage in research activities. This implies that working together with researchers may be a significant facilitator to building practitioners’ research capacity and closing the research implementation gap. The absence of an embedded researcher may even serve to widen the gap. Indeed, the public health practitioners observed that working with embedded researchers could eventually build their research capacity to independently conduct high-quality research in the future.

Overall, it was clear that the participants were aware of the importance of working together with embedded researchers, and the researchers were acknowledged for their ability to assist greatly with research-related training and support to build their research capacity. It would have been difficult for these organisations to generate high-quality on-site research if the embedded researchers had not been present. Consequently, the embedded researchers helped work to close the research evidence implementation gap.

Informing practice

The embedded researchers built and maintained relationships with the practitioners and other stakeholders to work together with them to co-produce research. The participants from the four case study sites reflected upon how the embedded researchers informed the sites of relevant research-based evidence, which helped in the development of future practice and research. By doing so, the embedded researchers bridged the gap between the discovery and implementation of research-based evidence. The results showed that all participants across all the four case study sites, irrespective of age, years of experience, and education, agreed that the role of the embedded researchers includes this theme.

The strategies adopted by the embedded researchers include: (1) identifying challenges in the host organisations, (2) utilising research experience, (3) implementing research evidence, (4) disseminating findings, identifying future research areas, and applying for funding, (5) presenting and publishing findings.

Identifying challenges in the host organisations

Participants agreed that the research skills of the embedded researchers are essential to the process of identifying the practical challenges facing the research sites. For instance, an embedded researcher used their research skill to unravel the root cause of the challenges facing a school (site three) through a thorough investigation by developing and conducting relevant research with the students and the teachers.

“[…] the GSCE reforms of the time that was taking place, it was causing a significant amount of stress and pressure for the teachers. In the first instance, teachers were having to grasp new skills at work, they were having to understand the new curriculum and subject knowledge. Some of the teachers weren’t particularly strong, there was a level of undue pressure and stress being put on the students, so pupils nationally were having to learn lots of different contents, they were sort of taken away the security blankets of things like modular testing in course work and what that meant was that students will now have to recall so much more knowledge in exam conditions” [TR1site3] .

Following the identification of these challenges, research-based recommendations were offered through the co-production research. By using research evidence to help tackle the school’s challenges, the researcher bridged the gap between the discovery and implementation of research-based evidence.

Utilising research experience

It is worth noting that the embedded researchers used their research experience to inform their host organisations of relevant existing and newly co-produced research evidence. The embedded researchers’ research-related expertise and the time they spent searching for relevant evidence were both seen as useful to the public health practitioners and other stakeholders.

“The beauty is that because it is their bread and butter, doing reviews and searching for evidence […] one of the things the embedded researcher did to help me with it was to do that literature review [..] it would have taken me much longer [..], so that’s the benefit [..] it is their strength and their experience and skills which they have got and which we may not have and the time to do it which we may not also have because we are constantly under the treadmill” [ PHP1site1] .

It was evident that the practitioners’ busy work schedules often restrict their ability to develop and implement their own research skills. Thankfully, the embedded researchers were able to assist the practitioners by using their research skills to overcome research-related challenges, and in the process taught them how to look for research evidence effectively. This, therefore, facilitates the implementation of evidence-based practice. The implication of this is that practitioners’ lack of research skills and time would have served as a barrier for evidence-based practice in the research sites.

It was clear that the research-based evidence searched for, or co-produced by the embedded researchers and the public health practitioners including other stakeholders was used to inform practice and make positive changes. Evidence showed that the embedded researchers had informed the host organisations of relevant research evidence and had used their research experience and skills to make research-based recommendations. In other words, the embedded researchers made valuable research evidence, and knowledge accessible. As such, this brought about desirable changes that improved service and delivery in the research sites.

“ So the way this works here is that you do the final report which has the recommendations in form of what we feel there should be changes to in practice, and that goes to their public management team and then they will look at that” [ERsite2] .

Furthermore, the embedded researchers also discussed how they helped make positive on-site changes occur. For instance, an embedded researcher from site two reported that positive changes were made in practice after developing recommendations in the form of a report submitted for management’s approval. It was clear that the practitioners take evidence-based advice from the embedded researcher to improve the quality of the services being offered to the public. Thus, this closes the gap between research evidence and its implementation.

Implementing research evidence

The interviews inquired as to how research-based evidence was translated into practice at the four research sites. As the interview process continued, it became clear that desired changes and improvements were achieved through the on-site application of research-based evidence. The results showed that across the four research sites, this process did indeed happen.

“[..] as it is very much about kind of being a resource to implement the recommendations and embed kind of the key findings from the research, again my role is trying to get some of these things into practice really so its embedded research but the main one of the main things is around embedding the recommendations as well, so that’s sort of work my role is around doing” [ ERsite1] .

“ [..] at the same time, it also helps the researcher coming in to understand what goes on in practice so that you don’t just go and conduct a piece of research that goes on the shelves. [..] So we would then need to weigh the evidence and the circumstances under which we are going to implement an intervention but we still take advice from the researcher on the evidence of what works. They could advice on what works [..] It’s more about the outcome of research being used to influence practice for quality improvement” [ PHP1site1] “There are changes that are made with how they recruit their staff for the delivery staff […] that changes were made and that was in practice, and they also kind of put it in a set of recommendations as to the ones to be delivered in schools” [ERsite4] .

Participants reported that the embedded researchers recommended existing research evidence, co-produced research evidence with the intent of informing practice, and also used relevant evidence to help improve service and delivery. In other words, the role of embedded researchers provided accessibility to research-based evidence that was utilised to develop solutions to on-site challenges and create positive change.

Disseminating findings, identifying future research areas, and applying for funding

The embedded researchers reported that having to present reports to diverse audiences prompted them to produce easily understandable, user-friendly reports that did not rely heavily on academic language.

“[..] so I have quarterly reports that I have to produce which has to be user-friendly and appeal to a various range of agencies within the organisation [..] we had, basically we have had quite a few different presentations to different kind of groups or the senior management team and departmental teams and things which was about and sharing the results and recommendations, we have follow-ups sort of things from that” [ ERsite1] .

“[..] Yeah, just into writing report so she will do like verbal update or she provides like some blueprints in an email ” [PHP5site2] .

The reports created by the embedded researchers avoided scientific terms that might be difficult for public health practitioners and other stakeholders to understand. Furthermore, practitioners and other stakeholders were informed of relevant research evidence in an unambiguous way. It is important to add that it would have been difficult for the embedded researchers to appropriately simplify their language if they had not had the opportunity to spend time on-site becoming familiar with the language used by the practitioners and stakeholders.

The participants also reported that the embedded research projects effectively discovered potential areas for future research. By making suggestions regarding future research, the embedded researchers furthered each host organisation’s potential to engage in relevant, change-creating research.

“[..] then the research outcomes were used to inform the next phase, so obviously that was the first phase, which we felt was really successful and worked really well, so then we took those sort of the things we learnt to the next phase” [ERsite3] .

For example, an embedded researcher from site three (school) stated that the first phase of their embedded research project was such a success that the findings of the first phase informed the direction of the second phase, thereby ensuring continuous research activities in the school.

Furthermore, participants agreed that the outcomes of the embedded research projects assisted with the application for future funding.

“[..] the results of the work that we did has been kind of used in terms of future funding opportunities, for providing data, providing kind of context information that was used in sort of proposals and in bids pushing and for applying for future funding” [ERsite1] .

It was evident that the presence of the embedded researchers in their host organisations encouraged the push to apply for funding to develop projects. This, therefore, facilitates continuous engagement in research activities. The practitioners felt that the role of the embedded researchers is crucial to producing funding applications and program development.

Presenting and publishing findings

Once embedded researchers succeeded at co-producing relevant on-site research evidence with practitioners and other stakeholders, and offering practical solutions to on-site challenges, it became clear that it would be necessary to present and publish the outcomes of the projects. Consequently, embedded researchers used their academic skills to publish the findings with practitioners and other stakeholders as co-authors. One of the benefits of publication is that published research can inform the host organisation, and other organisations facing similar challenges. Another significance of the role of embedded research pertaining to this, is that as the embedded research project is co-produced by both the embedded researcher and the host organisation, the findings from the research are jointly owned by both parties. This also assisted in integrating research into the host organisations culture.

“We wrote a book chapter with their names on the published book chapter. We got all of them involved with the writing of the chapter [..] that makes a sort of massive difference ” [ERsite3] .

“We co-authored a chapter of a book. We used the findings to create a book chapter but all of us has input into it including the researchers” [ST2site3] .

For example, participants from site three (school) reported that a book chapter based on co-produced research that they had worked on with the embedded researcher had been published [ 43 ]. Co-produced and co-published research evidence informs the school and research community of the institutional value of embedded research projects. The embedded researcher from site three (school) added that the names of the students and staff involved in the research and writing processes were included in the book chapter. The book chapter was co-edited by both an academic and a public health consultant. This publication has made a tremendous positive difference to how a school labelled as ‘deprived’ views itself. Indeed, being involved in the co-production of valuable research has encouraged both students and teachers.

To further explore how embedded researchers can inform public health practice, the participants were asked whether any other evidence-sharing processes had been used by the embedded researchers. The embedded researchers in this study were connected to more than one organisation. Consequently, they have access to organisations with information that can benefit public health practitioners and other stakeholders. The participants felt that participating in other organisations helped the embedded researchers fulfil their role as the discoverers and sharers of information. The participants viewed this role of the embedded researcher in their sites important as it informs them of the latest research evidence and activities in the field. This could also be seen as a way to sustain evidence-based practice in the sites. As the practitioners are regularly informed of the latest relevant evidence by attending research-based programmes, it facilitates the integration of research into the host organisations’ culture.

“When I see opportunities for conferences or local events, I will send an email or circulating them, there might be public health conference, it might be a Fuse conference that’s linked in erm linked in heavily with the thing we have worked on and I circulate that to the staff member, to say here is an opportunity” [ERsite2] .

For instance, an embedded researcher from site two stated that she regularly informed the practitioners of programmes and events presenting research relevant to their practice. By attending such events, practitioners can stay informed and up to date and are more likely to make changes to their practice based on timely research evidence. Consequently, the findings of this study indicate that staying familiar with the latest relevant research is one of the ways to close the gap between the discovery and implementation of research-based evidence.

Overall, it was evident that the embedded researchers’ ability to inform the organisations with relevant co-produced research evidence, and the ability to identify relevant information and opportunities and then circulate these to public health practitioners and stakeholders helped to inform the sites in creating relevant, research-based changes to benefit their public health practices. The positive outcomes they generated indicate that the role of embedded researchers can seriously contribute to closing the gap between the discovery and implementation of research-based evidence in the research sites.

Critical reflection

Twelve out of seventeen participants across the four sites discussed this theme as part of the role of the embedded researcher in their respective organisations. Participants felt that critical reflection was an important process an embedded researcher must engage in throughout the ‘journey’ of becoming an agent of closing the gap between research evidence and its implementation in practice. The identified strategy adopted by the embedded researchers within this theme is continuous reflection.

“I constantly reflect on my role to know what I am doing right, and what can be done differently” [ERsite1] .

“I have to spend really more time reflecting” [ERsite2] .

“It might be while you drive home [..] might be in the shower [..] might be when I take the dog out for a walk and tea time to reflect because you do need time to reflect on your research, on your methodology [..] about what the findings need to show [..] at times my bag is full of paper everywhere, millions of notes in here and I have to open and jot down some questions so that I won’t forget them because they are so important” [ERsite4] .

“I think it’s always good to sort of like reflect on what we have done, how we do things I personally want to think about whether I could have done things better […] so I think it’s quite important to sort of reflect on how you have done things, and how you could do things in the future, like what lessons you have learnt, I think it’s important to sort of reflect, to sort of think more about how you have done things and whether it could be practiced in the future” [ERsite3] .

Overall, the participants agreed that reflection helps embedded researchers assess their roles and constantly improve their work. Therefore, reflection is crucial to successfully co-producing research and closing the research implementation gap.

All participants, irrespective of their age, working experience and education, acknowledged that the relationships between the people involved in an embedded research project are crucial to the project’s success. This is in keeping with those made in previous studies that have concluded that building and maintaining mutually beneficial relationships with practitioners and other stakeholders significantly helps embedded researchers co-produce public health knowledge in non-clinical settings [ 33 , 44 ]. The study participants were also unanimous in their view that the ‘embeddedness’ of the researchers, or the degree to which they become part of or spend time within the host organisation, is significant. A higher degree of embeddedness appears to lead to the development of beneficial relationships and also helps researchers develop a better understanding of organisational contexts, that in turn leads to the development of effective solutions and useful, co-produced research. Notably, becoming embedded to a significant degree helps others see the researchers as part of the team. Previous studies have also indicated it is the duty of the embedded researcher to become part of the host organisation by working collaboratively with practitioners and other stakeholders [ 17 , 45 ].

Although the amount of time each embedded researcher spent within their host organisation varied, the interview data gathered from all sites confirmed that embedded researchers felt they were able to develop meaningful relationships with the host organisation. The National Institute for Health Research (NIHR) embedded research team reported similar findings and observed that the amount of time spent within an organisation can depend on the intensity of a project [ 46 ].

Among other strategies, informal conversations with the practitioners and other stakeholders also assisted the embedded researchers to build relationships. This was confirmed only by the embedded researchers in case study sites two and four who had worked in the host organisations for more than three years. This might be because the embedded researchers from the local authority (site two) and the sports organisation (site four) had worked and familiarised themselves with the members of the host organisation staff. Consequently, this could have facilitated easier informal conversations, unlike the embedded researcher in site one who has just spent seven months in the site. This confirms that it takes time for embedded researchers to build trustworthy relationships in the host organisation and they recommend an ‘introductory period’ of a minimum of three months for familiarisation before an embedded research project starts [ 39 ]. This was beneficial to the three case studies explored in an earlier study as it allowed the embedded researchers to familiarise themselves with their host organisations and as well build relationships with the host organisations’ staff [ 39 ]. This also aligns with the view of other scholars that an ‘introductory period’ is important before the commencement of an embedded research project [ 44 ]. It is worth noting that the practicability of an ‘introductory period’ may depend on the agreement between the parties involved.

Furthermore, embedded researchers must build relationships not only with practitioners and other stakeholders, but also with their academic supervisors. Having a successful relationship with the academic supervisor can help the embedded researcher overcome the challenges that arise as a consequence of having a dual affiliation and needing to manage diverse expectations and competing interests. The embedded researchers interviewed in this study had the support of their academic supervisors. Thanks to the vast experience of their supervisors, they are often excellent at mitigating unforeseen challenges. Indeed, among other factors, the success of an embedded researcher depends on the relationship between the researcher and his or her academic supervisor [ 13 , 39 ].

The interview participants recounted that it is important to work together to co-produce relevant research which is useful to the organisations. Other scholars have similarly concluded that embedded researchers work with members of their host organisations to identify, plan, and conduct research that will meet the needs of the organisation [ 36 ]. By working collaboratively, embedded researchers were able to train the practitioners and other stakeholders and improve their ability to help co-produce meaningful and valuable research that can be used to implement evidence-based adjustments to on-site practices.

The findings of this study indicate that working together produces meaningful research and also teaches practitioners and other stakeholders who assist embedded researchers, how to conduct research. Similarly, an earlier study concluded that embedded researchers encourage practitioners and other stakeholders to participate in research activities and increase an organisation’s capacity to conduct research [ 17 ]. In other words, the collaborative work that accompanies embedded research helps close the research implementation gap. However, it was noted in this current qualitative inquiry that having the right researchers assisted in carrying out the projects successfully. This is similar to an earlier study that argue that having the right combination of researchers and practitioners in co-production is crucial to the success of such project [ 13 ]. Also, other scholars pointed out that not all researchers have the relevant skills to conduct co-produced research [ 17 ]. Therefore, it is essential to have the right combination of researchers, practitioners, and other stakeholders while working together to co-produce research to ensure its success.

Based on the current qualitative inquiry, the role of the embedded researchers includes informing practice by making recommendations and positive changes that utilise both existing and newly co-produced research evidence. Doing so makes research evidence more accessible to public health practitioners and other stakeholders and ultimately improves service and delivery. An earlier study similarly revealed that informing practice has been identified as a way by which embedded researchers communicate new and existing relevant research evidence and integrate research findings into practice [ 3 ].

As discussed earlier, two of the factors responsible for the gap between the discovery and implementation of research evidence are the disparity between the language spoken by the researchers and practitioners and the complexity of the language spoken by researchers, which is often include scientific jargon. Such complex language can be difficult for practitioners to understand or lead to ambiguities in interpretation [ 12 ]. To discover whether language differences was an issue in this study, the interviews included questions regarding how research evidence and recommendations were communicated to public health practitioners and other stakeholders. These questions were designed to create an understanding of how the embedded researchers had communicated. The interviews revealed that the embedded researchers communicated research outcomes and recommendations effectively to the practitioners by using simple, unambiguous language. Using such language helped make research evidence more accessible to the practitioners.

Providing evidence for reports and future funding applications was identified as an important part of the embedded researchers’ work within their host organisations [ 17 , 47 ]. The interview participants agreed that the researchers sometimes helped secure funds needed to conduct research at the host organisation. Doing so encouraged each host organisation’s staff to participate in research that could prove useful to the organisation in the future.

Critical reflection helps embedded researchers evaluate the role they play within their host organisation and keep track of their progress [ 33 , 48 ]. In other words, reflection helps researchers identify and improve upon the areas that are not meeting expectations and discover what approaches are working successfully. This corresponds with the findings from this current qualitative inquiry. The interview participants acknowledged that the embedded researchers continuously reflect on their role and their work in order to identify what is and is not working. This assists embedded researchers to think of ways to apply acquired learning to daily on-site practice to improve their role in the co-production of research to bridge the gap between research evidence and its implementation in public health practice.

Limitations of the study

One of the limitations of this study was the sample size. A total of 17 participants was recruited for this study, although the sample size would have been larger than 17 but for the COVID-19 pandemic. Another consideration of this piece of work, being qualitative research, was subjectivity. The information provided by the participants was based on their point of view. Hence, it might be difficult to objectively verify the qualitative information provided to ensure that accurate information was provided by the participant regarding the phenomenon of interest. Nevertheless, some practical measures were undertaken to ensure the credibility of this work. Data triangulation and site triangulation [ 49 ] were adopted in this study. These were done to increase the confidence in the outcome of the qualitative multi-site case study.

Overall, the success that the embedded researchers experienced, including building relationships, co-producing research, translating research into practical changes, evaluating projects, and informing future public health practices as well as future research, justifies increasing the amount of embedded research being conducted in public health practice. Embedded researchers also bring the tremendous benefit of strengthening the research capacities of public health practitioners and other stakeholders by providing research-based training and support. Such developments have the ability to prove the potential of embedded research projects. Finally, the relevant research-based recommendations made from the co-produced research guided by the embedded researchers are used to inform practice. The positive outcomes generated by the embedded research process indicate that embedded researchers can meaningfully contribute to closing the gap between the discovery and implementation of research evidence.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available. They are available from the corresponding author on reasonable request, subject to approval from the Teesside University School of Health and Life Sciences Research Governance and Ethics Committee.

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Acknowledgements

We thank the participants for sharing their expertise and time. We are grateful for the contribution of Ronnie Ramlogan who supported us in the preparation of this manuscript.

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Abisope Akintola

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This study is part of AA’s PhD work, as such, AA conducted this piece of work with the supervision of DNB and SK.

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Interview schedule for embedded researchers

Role identification and background information about the embedded research initiative.

What is your role in your organisation? Prompt - Job title, Daily task, Responsibilities. B) How long have you been in this role? C) Can you tell me about your background and what you do? Prompt -The journey so far- How do you get to where you are now?  D) As an embedded researcher where is your academic affiliation?

How long has your embedded research initiative been going on in your organisation? B) Do you know the rationale for employing an embedded researcher in your organisation? C) Who funds your project? D)What is the management arrangement?

Moving on to look at the embedded research initiative more specifically

What is the aim of the embedded research project you are involved in? B) How many hours/days do you spend in your host organisation in a week, and in the academic institution?  C) Why? D) How often do you contact your academic supervisor?

How has embedded research gone so far in your organisation?  B) How many people are involved in the co-production/embedded research you are involved in? or who do you work with? C) How many embedded researchers are involved in the project? Prompt - How many professionals/stakeholders?

What are your views and experience of embedded research? Prompt - what have you learnt? What, if anything, has helped?  (Why do you say that?) What, if anything, has been more difficult or challenging? (Why do you say that)? What difference has embedded research made in your organisation?  (so if embedded research has been useful, why and how?)

Looking more specifically at the role of the embedded researcher in the organisation

What is your role, as an embedded researcher in bridging the gap between research evidence and its implementation in practice? Prompts - How do you inform practice with research evidence?  How do you communicate research evidence to practitioners and other stakeholders to facilitate its use in practice? B) Does your role involve the translation of research evidence into practice? If yes, what is the process? can you please cite an example? What evidence-sharing methods or processes do you use?

Can you think of any changes in practice/policy as a result of research evidence being used? Prompt – What role did you play? Who was involved? What changed? How? For who?

Tell me what you think are the benefits of working as an embedded researcher? Why do you say that? B) How do you manage the dual affiliation? Prompt -what are the benefits (What has helped?) and also what are the challenges?

Tell me what you think are the challenges of working as an embedded researcher? Prompt - Why do you say that? B) What are the barriers to data sharing, if any?

Do you think building mutually beneficial relationships with the host organisation staff is important to the success of an embedded research project? If yes, Why? B) How do you build relationships with the host organisation’s staff?

Can you cite an example of where you have built practitioners and other stakeholders’ confidence in conducting their own research?

Does your role requires managing research funds? If yes, how do you manage this?

 How often do you reflect on your role? Prompt- To know what works and what needs to be improved?  Why is this important?

Do you think the development of a toolkit on the role of embedded research in bridging the gap between research evidence and its implementation in public health practice would be useful? If yes, Why and how do you think it could be used in practice?”

Any top tips for other researchers considering embedded research?

Please don’t mention names, but can you think of any potential participants- people you are working with or have worked with that you can pass on the details of this research?  B) Would you be happy to be contacted afterward to circulate details of this research to those you have identified, to see if they will be willing to participate in this research?

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Akintola, A., Newbury-Birch, D. & Kilinc, S. Bridging the gap between research evidence and its implementation in public health practice: case studies of embedded research model. BMC Public Health 24 , 1299 (2024). https://doi.org/10.1186/s12889-024-18727-z

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DOI : https://doi.org/10.1186/s12889-024-18727-z

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

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.

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 ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

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 ​ (Table5), 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.

Definitions of a case study

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 ​ (Table1), 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 ​ Tables2, 2 , ​ ,3 3 and ​ and4) 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 ​ (Table2) 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 ​ Tables2 2 and ​ and3, 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 ​ (Table4 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 ​ (Table6). 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 ].

Example of epistemological approaches that may be used in case study research

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 ​ Table7 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 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 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 ​ (Table1) 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 ​ Table3) 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 ​ (Table2 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 ​ (Table1 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 ​ (Table3 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 ​ (Table4 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 ​ Table3, 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 ​ (Table4), 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 ​ Table8 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 ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 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.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

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.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

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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• Introduction
• Conclusions
• Article Information

Social Deprivation Index ranges from 0 (least deprived) to 100 (most deprived) and was divided by 10 for reporting. Adjustments were for age, sex, pediatric intensive care unit length of stay before screening date, Pediatric Risk of Mortality III score, year of screening, elective or nonelective admission, origin of admission, and study type (observational or interventional). Other race included American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, multiracial, other, and refused. NA indicates not applicable; OR, odds ratio.

Social Deprivation Index ranges from 0 (least deprived) to 100 (most deprived) and was divided by 10 for reporting. Adjustments were for age, sex, pediatric intensive care unit length of stay before screening date, Pediatric Risk of Mortality III score, year of screening, elective or nonelective admission, origin of admission, and study type (observational or interventional). Other race included American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, multiracial, other, and refused. NA indicates not applicable.

It was hypothesized that reduced probability of approach would act as a mediator of the association between Black race and reduced consent. In this framework, we assessed the natural indirect effect (NIE) via mediation and the natural direct effect (NDE) of Black race (reference group, White race) on consent rates. Odds ratios (ORs) are presented, adjusted for age, sex, pediatric intensive care unit length of stay before screening date, Pediatric Risk of Mortality III score, year of screening, elective or nonelective admission, origin of admission, and study type (observational or interventional).

eMethods. Data Source, Eligibility, Definitions, Statistical Analysis

eFigure 1 . Directed Acyclic Graph Informing Regression Models

eFigure 2. Flowchart of Study Participation

eTable 1. Odds of Approach for Study Participation According to Race and Ethnicity, Preferred Language, Religion, or Social Deprivation Index

eTable 2. Reasons for Not Approaching by Race and Ethnicity

eTable 3. Reasons for Not Approaching by Language Preferred

eTable 4. Reasons for Not Approaching by Religion

eTable 5. Odds of Approach Stratified According to Study Type

eTable 6. Odds of Consent for Study Participation Among All Eligible Patients According to Race and Ethnicity, Preferred Language, Religion, or Social Deprivation Index

eTable 7. Odds of Consent Stratified According to Study Type

eTable 8. Odds of Consent for Study Participation Among Approached Patients According to Race and Ethnicity, Preferred Language, Religion, or Social Deprivation Index

eTable 9. Odds of Consent Restricted to Those Approached for a Study, Stratified According to Study Type

eTable 10. Odds of Approach, Consent, and Consent Restricted to Those Approached for a Study, With Multiple Imputation of Missing Data

eTable 11. Odds of Approach, Consent, and Consent Restricted to Those Approached for a Study, With All Exposures Included in the Same Model, Using the Dataset With Imputed Missing Variables

eTable 12. Results of a Multinomial Logistic Regression for Odds of Approached and Declined Consent and Approached and Provided Consent, With Not Approached Used as the Reference

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Mayer SL , Brajcich MR , Juste L , Hsu JY , Yehya N. Racial and Ethnic Disparity in Approach for Pediatric Intensive Care Unit Research Participation. JAMA Netw Open. 2024;7(5):e2411375. doi:10.1001/jamanetworkopen.2024.11375

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Racial and Ethnic Disparity in Approach for Pediatric Intensive Care Unit Research Participation

• 1 Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia and University of Pennsylvania, Philadelphia
• 2 Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
• 3 Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia

Question   Are sociodemographic factors associated with rates of approach and consent for pediatric intensive care unit (PICU) research?

Findings   This cohort study of 3154 children found disparities in approach and consent according to race and ethnicity, language, religion, and degree of social deprivation. Lower consent rates were partly mediated by lower approach rates, with reduced approach mediating approximately half of the lower rates of consent for Black children.

Meaning   In this study, multiple sociodemographic variables were associated with disparate consent rates for PICU research, and strategies to increase approaches could contribute to equitable enrollment in PICU studies.

Importance   While disparities in consent rates for research have been reported in multiple adult and pediatric settings, limited data informing enrollment in pediatric intensive care unit (PICU) research are available. Acute care settings such as the PICU present unique challenges for study enrollment, given the highly stressful and emotional environment for caregivers and the time-sensitive nature of the studies.

Objective   To determine whether race and ethnicity, language, religion, and Social Deprivation Index (SDI) were associated with disparate approach and consent rates in PICU research.

Design, Setting, and Participants   This retrospective cohort study was performed at the Children’s Hospital of Philadelphia PICU between July 1, 2011, and December 31, 2021. Participants included patients eligible for studies requiring prospective consent. Data were analyzed from February 2 to July 26, 2022.

Exposure   Exposures included race and ethnicity (Black, Hispanic, White, and other), language (Arabic, English, Spanish, and other), religion (Christian, Jewish, Muslim, none, and other), and SDI (composite of multiple socioeconomic indicators).

Main Outcomes and Measures   Multivariable regressions separately tested associations between the 4 exposures (race and ethnicity, language, religion, and SDI) and 3 outcomes (rates of approach among eligible patients, consent among eligible patients, and consent among those approached). The degree to which reduced rates of approach mediated the association between lower consent in Black children was also assessed.

Results   Of 3154 children included in the study (median age, 6 [IQR, 1.9-12.5] years; 1691 [53.6%] male), rates of approach and consent were lower for Black and Hispanic families and those of other races, speakers of Arabic and other languages, Muslim families, and those with worse SDI. Among children approached for research, lower consent odds persisted for those of Black race (unadjusted odds ratio [OR], 0.73 [95% CI, 0.55-0.97]; adjusted OR, 0.68 [95% CI, 0.49-0.93]) relative to White race. Mediation analysis revealed that 51.0% (95% CI, 11.8%-90.2%) of the reduced odds of consent for Black individuals was mediated by lower probability of approach.

Conclusions and Relevance   In this cohort study of consent rates for PICU research, multiple sociodemographic factors were associated with lower rates of consent, partly attributable to disparate rates of approach. These findings suggest opportunities for reducing disparities in PICU research participation.

Inclusive representation in research is important for ensuring generalizability of results, equitable access to medical advances, and improved trust between patients and clinicians. Disparities in research enrollment have been demonstrated in oncology, 1 - 3 COVID-19 trials, 4 , 5 and the general adult population. 6 , 7 Importantly, racial and ethnic disparities in research are often indexed to census data, 8 - 12 which may differ from the population eligible for studies. In US pediatric trials from 2011 to 2020, relative to census demographics, Black children appear overrepresented, whereas American Indian and Alaska Native, Asian, and Native Hawaiian and Other Pacific Islander children appear to be underrepresented, 11 and no racial or ethnic disparities were identified in pediatric drug or device studies. 9 However, defining overrepresentation relative to census data masks actual disparities in consent rates among eligible participants, given higher rates of hospital 13 and pediatric intensive care unit (PICU) 14 - 16 admission for Black children and residents of high-poverty neighborhoods. In addition to race and ethnicity, disparities in enrollment have also been demonstrated based on language preference, 17 - 19 although this has been less studied in critical care.

The PICU presents unique challenges for study enrollment, given the highly stressful and emotional environment for caregivers and the time-sensitive nature of enrolling participants with severe and rapidly changing disease. 20 , 21 This limits a research team’s ability to build trust and rapport with a family prior to study introduction and restricts families’ time to consider a study prior to consenting. Research investigating disparities in study enrollment in the PICU is limited to a reanalysis of a cluster-randomized interventional trial of sedation management (Randomized Evaluation of Sedation Titration for Respiratory Failure [RESTORE]) 22 and an evaluation of enrollment in a biorepository at a single center. 23 Both studies identified lower rates of research approach and consent of patients who were members of racial and ethnic minority groups. These results contrast with the conclusions of studies referencing census data, and further work is necessary to determine whether these disparities are seen across a larger sample of pediatric critical care research.

Therefore, we analyzed all research studies, interventional and observational, from a large academic PICU over 10 years that required prospective informed consent. We hypothesized that disparities in study approach and consent existed according to race and ethnicity, religion, spoken language, and socioeconomic status and that disparities in consent rate were partly mediated by the probability of approaching families to offer study participation.

This retrospective cohort study reviewed all screening and consent logs for all research studies prospectively enrolling in the Children’s Hospital of Philadelphia (CHOP) PICU from July 1, 2011, to December 31, 2021. The CHOP Institutional Review Board reviewed this study and provided an exempt determination from approval and informed consent because it was a retrospective cohort. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) guideline. Additional details are provided in the eMethods in Supplement 1 .

All patients eligible for research requiring consent were potentially eligible for this study. Protocols for each study were examined for specific inclusion and exclusion criteria to determine eligibility for our study. Detailed eligibility criteria are provided in the eMethods in Supplement 1 .

Screening logs were linked to the electronic medical record (EMR) for data collection. We examined 4 distinct exposures: race and ethnicity, preferred language, religion, and Social Deprivation Index (SDI). Since our EMR permits Hispanic to be reported as either a race or ethnicity, we combined race and ethnicity into groupings of Hispanic, non-Hispanic Black, non-Hispanic White, and other (including American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, Indian, multiracial, other, and refused). Preferred language was encoded as Arabic, English, Spanish, and other. Religion was coded as Christian, Jewish, Muslim, none, and other. Zip code was used to assign the SDI, a validated composite of area-level deprivation extracted from the American Community Survey. 24

We modeled 3 distinct outcomes: approach for research, consent to research among eligible patients, and consent to research among those approached. Confounders included age, sex, PICU length of stay prior to screening, illness severity as defined by Pediatric Risk of Mortality (PRISM) III score at 12 hours (range, 0-74, with higher scores indicating greater mortality risk), year of screening, elective or nonelective admission, origin of admission (emergency department, inpatient floor, neonatal ICU, operating room, or outside hospital), and study type (observational or interventional). Entries reflecting the same patient eligible for different trials or on separate admissions were retained as separate encounters. Quantitative variables were treated as continuous variables.

Data were analyzed from February 2 to July 26, 2022. Separate logistic regression models were used to separately test the association between the 4 exposures of interest (race and ethnicity, language, religion, and SDI) and the 3 outcomes (approach for study [all eligible patients], consent to study [all eligible patients], and consent if approached [restricted to those approached]). All analyses (unadjusted and adjusted) used robust variance estimators to account for 2-way nonnested clustering (by patient and by study), and all multivariable analyses were adjusted for confounders selected using a causal framework. Exposures had less than 5% missingness except for language (187 [5.9%]), and as the nonrandom missingness of language made imputation conceptually difficult with the available variables, 25 only complete case analyses were conducted in the primary analyses. Exposures were analyzed in independent models, given the complex interactions and potential collinearity among race and ethnicity, language, religion, and SDI (eFigure 1 in Supplement 1 ). We performed multiple additional analyses. First, given possible differences between observational and interventional studies, we a priori tested for differential associations between exposures and outcomes according to study type. Second, to test whether data missingness affected our conclusions, we repeated analyses using multiple imputation by chained equations 26 (10 imputations over the entire cohort) to impute missing values for language, religion, and SDI. Third, we performed an exploratory analysis by including all exposure variables in the model, in addition to confounders, on the dataset with imputed missing data. Fourth, as an alternative method to model the data, we performed multinomial regression for odds of approach and declining consent and approach and providing consent, with not approached set as the reference.

Finally, causal mediation analysis 27 was performed to estimate the degree to which the association between Black race and odds of consent was mediated by the probability of being approached. This was a 2-step procedure where we first estimated the probability of being approached for all participants using a separate logistic regression model with all variables included as independent variables and being approached as the outcome. We then used this estimated probability of being approached as a mediator of the association between Black (compared with White) race and odds of consent. All analyses were conducted in Stata, version 18 (StataCorp LLC), with 2-sided P  < .05 considered significant for main analyses and 2-sided P  < .10 for assessing the significance of interaction terms.

Forty-four screening logs from studies (10 interventional trials and 34 observational studies) enrolling between 2011 and 2021 included 35 837 encounters. Two studies had no patients screened. Of the total, 31 585 encounters were excluded as ineligible according to the eligibility criteria for the parent studies, 180 for incomplete or ambiguous data regarding eligibility, 299 for suspended enrollment (including during the COVID-19 pandemic), 561 for patient location in the cardiac ICU (rather than PICU), and 58 for inability to determine whether or not a patient was approached (ie, unable to assign a primary outcome), leaving a total of 3154 patients eligible for our study (eFigure 2 in Supplement 1 ). Of these, sex was recorded as male for 1691 patients (53.6%) and female for 1461 (46.4%), with data missing for 2 (0.1%). Median age was 6.0 (IQR, 1.9-12.5) years, and median PRISM III score was 9 (IQR, 4-15). In terms of race and ethnicity, 855 patients (27.1%) were identified in the EMR as Black, 484 (15.3%) as Hispanic, 1204 (38.2%) as White, and 611 (19.4%) as other. English was the preferred language for most patients (2635 of 2967 with data available [88.8%]). Of the 3154 eligible patients, 896 patients were not approached, 816 were approached but declined consent, and 1442 (45.7% of eligible patients and 63.9% of approached patients) consented to studies ( Table ).

Relative to White children, lower odds of approach were seen for Black children (unadjusted odds ratio [OR], 0.64 [95% CI, 0.52-0.79]; adjusted OR [AOR], 0.60 [95% CI, 0.49-0.73]), Hispanic children (OR, 0.59 [95% CI, 0.44-0.80]; AOR, 0.57 [95% CI, 0.42-0.76]), and children of other race (OR, 0.47 [95% CI, 0.36-0.61]; AOR, 0.44 [95% CI, 0.35-0.56]) ( Figure 1 and eTable 1 in Supplement 1 ). Black and Hispanic families were more commonly not approached due to family unavailability, while Hispanic families and families of other race were more commonly not approached due to perceived language barriers (eTable 2 in Supplement 1 ).

Compared with families who preferred English (eTable 1 in Supplement 1 ), families who preferred Arabic (OR, 0.28 [95% CI, 0.16-0.50]; AOR, 0.28 [95% CI, 0.15-0.51]), Spanish (OR, 0.50 [95% CI, 0.29-0.85]; AOR, 0.57 [95% CI, 0.32-1.02]), or other language (OR, 0.12 [95% CI, 0.07-0.22]; AOR, 0.12 [95% CI, 0.07-0.21]) had lower odds for approach, primarily due to perceived language barriers (eTable 3 in Supplement 1 ). Muslim families had lower odds for approach than those with none for religious affiliation (OR, 0.46 [95% CI, 0.32-0.66]; AOR, 0.41 [95% CI, 0.28-0.59]), also primarily due to language barriers (eTable 4 in Supplement 1 ). Higher (worse) SDI was associated with lower odds of approach (OR, 0.95 [95% CI, 0.92-0.97] per 10-point change; AOR, 0.95 [95% CI, 0.93-0.98] per 10-point change).

In stratified analysis, odds of approach were more favorable for other language for interventional (OR, 0.25 [95% CI, 0.08-0.84]) rather than observational (OR, 0.09 [95% CI, 0.05-0.16]) studies ( P  = .09 for interaction) (eTable 5 in Supplement 1 ). No other variables had a differential association with approach according to study type.

Among eligible patients, Black children (OR, 0.65 [95% CI, 0.51-0.82]; AOR, 0.59 [95% CI, 0.46-0.77]) and those of other race (OR, 0.66 [95% CI, 0.50-0.86]; AOR, 0.58 [95% CI, 0.42-0.79]) had lower consent odds ( Figure 2 and eTable 6 in Supplement 1 ) relative to White children. Families preferring Arabic (OR, 0.48 [95% CI, 0.27-0.87]; AOR, 0.45 [95% CI, 0.24-0.85]) or other language (OR, 0.15 [95% CI, 0.07-0.30]; AOR, 0.14 [95% CI, 0.06-0.31]) were less likely to consent relative to English-speaking families. Muslim families also were less likely to consent (OR, 0.56 [95% CI, 0.38-0.82]; AOR, 0.56 [95% CI, 0.36-0.86]) relative to those with none for religious affiliation. Higher (worse) SDI had an OR less than 1 for odds of consent, but the results were not significant in adjusted analysis (OR, 0.97 [95% CI, 0.94-1.00] per 10-point change; AOR, 0.97 [95% CI, 0.94-1.01] per 10-point change). In stratified analysis, odds of consent were more favorable for interventional vs observational studies for other race (ORs, 0.83 [95% CI, 0.62-1.12] and 0.51 [95% CI, 0.35-0.74], respectively; P  = .009 for interaction) and other language (ORs, 0.44 [95% CI, 0.14-1.41] and 0.09 [95% CI, 0.04-0.21], respectively; P  = .03 for interaction) (eTable 7 in Supplement 1 ).

When restricted to patients approached for research participation, odds of consent did not differ by race, language, religion, or SDI, except for Black relative to White children (OR, 0.73 [95% CI, 0.55-0.97]; AOR, 0.68 [95% CI, 0.49-0.93]) and Jewish children relative to those with none for religious affiliation (OR, 0.56 [95% CI, 0.32-0.96]; AOR, 0.57 [95% CI, 0.31-1.04]) ( Figure 3 and eTable 8 in Supplement 1 ). In stratified analysis, odds of consent were more favorable for interventional rather than observational studies for other race (ORs, 1.19 [95% CI, 0.72-1.96] and 0.76 [95% CI, 0.48-1.20], respectively; P  = .07 for interaction) and other language (ORs, 1.24 [95% CI, 0.47-3.26] and 0.24 [95% CI, 0.07-0.87], respectively; P  = .08 for interaction) (eTable 9 in Supplement 1 ).

When we repeated the analyses after imputing missing language, religion, and SDI (eTable 10 in Supplement 1 ), we found similar conclusions as those of the primary analyses (compare Figure 1 with Figure 3 ). Overall effect sizes with imputed data were similar for associations with approach among all eligible patients, consent among all eligible patients, and consent among those approached (compared with eTables 1, 6, and 8 in Supplement 1 ).

In an exploratory analysis, we examined whether conclusions were substantially affected by our choice to model race and ethnicity, language, religion, and SDI separately. Using the fully imputed dataset, we explored all exposure variables in a single model (eTable 11 in Supplement 1 ). In this analysis, effect sizes for associations between demographic variables and odds of approach or consent among all patients were all somewhat attenuated toward the null, although overall conclusions did not change, with the same variables retaining statistical significance. When assessing the odds of consent among those approached, conclusions were unchanged, with Black race and Jewish religion associated with lower odds of consent among those approached, identical to our primary analyses (compared with Figure 3 ; eTables 8 and 10 in Supplement 1 ). Interestingly in this fully adjusted model, the ORs for Black race and Jewish religion were more extreme than in the primary analyses, although we caution that based on the assumptions laid out in our directed acyclic graph (eFigure 1 in Supplement 1 ), this analysis may have a biased interpretation.

Last, we explored multinomial regression as an alternative analytic method (eTable 12 in Supplement 1 ). All racial and ethnic minority patient groups (relative to White patients), all non–English-speaking patients (relative to English-speaking patients), and Muslim patients (relative to those with none for religious affiliation) had lower odds of approach overall and of consent relative to not being approached. Consistent with the primary analysis, Black race and Jewish religion had significant differences between the effect sizes reported for approached and declined and approached and consented.

Given persistently lower odds of approach and consent for Black children in all of our analyses, causal mediation analysis was performed to determine the degree to which a lower estimated probability of being approached mediated overall lower rates of consent ( Figure 4 ). We found that 51.0% (95% CI, 11.8%-90.2%) of the lower rates of consent for Black children were mediated by the lower rates of approach.

In children eligible for research studies from 2011 to 2021 in the CHOP PICU, this cohort study found underrepresentation according to race and ethnicity (Black, Hispanic, and other), preferred language (Arabic or other), religion (Jewish, Muslim, or other), and socioeconomic status (higher SDI). These disparities were primarily attributable to lower odds of being approached by research teams, with attenuation of ORs when analyzing only patients who were approached. Overall, our results suggest that improved rates of representative enrollment can be achieved with increased rates of research approach, including among Black children. However, there may be additional reasons why Black patients are less likely to consent even if approached that require additional investigation.

Our results are concordant with the existing 2 studies regarding consent disparities in the PICU, which showed lower consent in racial and ethnic minority groups and non-English speakers. 22 , 23 Our study adds to this literature by attempting to quantify the degree to which research populations represent the population eligible for the study, rather than the US population as a whole. Our findings of lower odds of consent for Black children contrasts with some recent literature. In a review of 612 pediatric trials conducted between 2011 and 2020, Black patients were reported to be overrepresented relative to the US census (OR, 1.88 [95% CI, 1.87-1.89]), 11 although this was not confirmed in a review of pediatric studies listed on ClinicalTrials.gov between 2007 and 2020. 12 However, using the US census as a reference to identify disparate rates of trial enrollment can be problematic, 8 , 10 , 11 as previous studies have demonstrated disparities according to race and ethnicity and SDI in patients admitted to PICUs. 15 , 16 The magnitude of disparities in study enrollment can be further biased due to systematic undercounting of Black individuals in the US census. 28 Even attempts to identify disparities in enrollment by referencing disease or hospitalization prevalence can be inaccurate, 12 as not every patient with a diagnosis is eligible for a study. Thus, our study may provide a less biased estimate of disparities for participation in PICU research by conditioning on patients actually eligible for the research. By including 42 total studies, both observational and interventional, our study evaluates the effects of multiple sociodemographic exposures across a variety of clinical and research scenarios. In general, even demographic groups with lower odds of consent were more likely to consent to interventional studies. Prior investigations on disparities in PICU research were limited to single-parent studies. 22 , 23 Our study provides some additional nuance regarding the motivations of families affecting their willingness to have their critically ill child participate in research.

Our results highlight the effect of communication between research staff and families on equitable inclusion in research. Differential rates of being approached for research may reflect implicit bias of the research team or the clinical attending physician in assessing likelihood of consent or uncertainty in building rapport with the family. Disparities according to language may reflect inadequacy of or discomfort using interpreter services, limited parental presence at bedside, or accessibility by telephone. Effective interpreter use could mitigate the lower rates of approach seen for non-English speakers, especially Spanish speakers, who were approached at lower rates but had similar odds of consent relative to English speakers. By contrast, speakers of languages other than Arabic, English, or Spanish had the lowest odds of consenting even when approached, which may be due to inadequacy of communication even if approached. Institutional review board inclusion and exclusion criteria varied in their consideration of families with non–English language preference. Parental absence at bedside accounted for nearly half of cases of inability to approach families in the RESTORE trial 22 and was also a factor in our cohort. Improved research staff training (including bias training), increased awareness of and access to interpretation services, availability of telephone consent, enrollment outside business hours (especially for studies not above minimal risk), greater use of video-conferencing technologies and web-based signatures, and deferring immediate consent in appropriate scenarios are all strategies to increase rates of approach.

Lower rates of approach for research participation may not, however, account for all of the observed disparities in study enrollment. Among Black children, half of the observed disparity in consent was mediated by the lower probability of being approached. The reasons why families of Black children are less likely to consent after being approached requires dedicated exploration. Solving this problem is essential for ensuring that conclusions from research are generalizable and applicable to the population at risk for the conditions being studied.

While our study offers some perspectives on disparities in PICU-based research, additional work is necessary. A survey of primary care clinicians found greater mistrust of research among Black parents. 29 Attitudes toward research may differ in the intensive care setting, and existing studies can be affected by participation bias, limiting the utility of such studies to the PICU population. Survey studies carry the additional concerns of social desirability bias and acquiescence bias. Barriers to parental consent to PICU research after being approached may also include time constraints, feeling overwhelmed, perception of research being burdensome, health literacy, trust in the medical system, and research not being explained well. 23 , 30 , 31 Variations in these attitudes by social or cultural group are not well understood and likely differ between institutions. Racism in the health care field, for example, varies geographically in the US. 16 , 32 Additional research is needed to better understand why families are approached at varying frequencies and how a family decides whether to consent following approach, both globally and within specific institutions. Qualitative research methods may provide unique opportunities and insights to address these questions.

Our study has limitations. We were limited to review of consent logs from a single institution, and some findings may not generalize to all North American PICUs. However, our demographics and severity of illness are similar to many other academic PICUs engaged in research. Our study relied on documentation of race and ethnicity, language, and religion in the EMR, which may lead to exposure misclassifications if categories were entered inaccurately. Minority races and ethnicities are more likely to have discordance between EMR- and self-reported race and ethnicity, 33 , 34 although a recent study assessing this in pediatrics found reasonably high overall concordance (κ = 0.77). 33 Local patient demographics led to many racial and ethnic, linguistic, or religious groups being combined into groups designated other. This resulted in substantial heterogeneity within these groups and limited generalizability outside the larger sociodemographic groups we report herein. Overall, these limitations related to documentation of our exposure variables require our results to be interpreted cautiously and highlight the importance of future studies investigating disparities to standardize the recording of sociodemographic variables. The method in which we handled non-English language for eligibility (ie, eligible unless the study protocol excluded) was an effort to capture potential causes of disparities in approach and consent related to language, but this may introduce potential bias. Certain factors that may affect research participation, such as demographic characteristics of research coordinators, consenting parties, and medical teams, were not available. Last, while the availability of detailed screening logs helped to inform conclusions, we have only limited information about why patients were not approached and no information about why families declined.

In this cohort study of consent rates for PICU research participation, we found lower odds of enrollment according to race and ethnicity, language, religion, and degree of social deprivation. These disparities were largely attributable to disparate rates of approach for research participation, with the important exception of Black children, who were less likely to be enrolled even after accounting for lower rates of approach. Future research should seek to better understand cultural attitudes toward pediatric research in the PICU and test interventions to improve communication and trust between research teams and families.

Accepted for Publication: March 13, 2024.

Published: May 15, 2024. doi:10.1001/jamanetworkopen.2024.11375

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Mayer SL et al. JAMA Network Open .

Corresponding Author: Nadir Yehya, MD, MSCE, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, CHOP Main, Room 9NW39, Philadelphia, PA 19104 ( [email protected] ).

Author Contributions: Drs Mayer and Yehya had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Mayer, Yehya.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Mayer, Juste, Yehya.

Critical review of the manuscript for important intellectual content: Mayer, Brajcich, Hsu, Yehya.

Statistical analysis: Mayer, Brajcich, Hsu, Yehya.

Administrative, technical, or material support: Juste.

Supervision: Yehya.

Conflict of Interest Disclosures: Ms Juste reported consulting for Fulcrum Therapeutics Inc outside the submitted work. Dr Yehya reported receiving grant funding from the National Institutes of Health during the conduct of the study and consulting for AstraZeneca outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported by the Endowed Chair of Pediatric Lung Injury at Children’s Hospital of Philadelphia (CHOP) (Dr Yehya).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We wish to thank the multiple families involved in research at CHOP over the past decade, as well as the research coordinators and research assistants involved. Charlotte Z. Woods-Hill, MD, Children’s Hospital of Philadelphia and University of Pennsylvania, made important contributions to the manuscript, for which she was not compensated.

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• Open access
• Published: 14 May 2024

A burden of proof study on alcohol consumption and ischemic heart disease

• Sinclair Carr   ORCID: orcid.org/0000-0003-0421-3145 1 ,
• Dana Bryazka 1 ,
• Susan A. McLaughlin 1 ,
• Peng Zheng 1 , 2 ,
• Sarasvati Bahadursingh 3 ,
• Aleksandr Y. Aravkin 1 , 2 , 4 ,
• Simon I. Hay   ORCID: orcid.org/0000-0002-0611-7272 1 , 2 ,
• Hilary R. Lawlor 1 ,
• Erin C. Mullany 1 ,
• Christopher J. L. Murray   ORCID: orcid.org/0000-0002-4930-9450 1 , 2 ,
• Sneha I. Nicholson 1 ,
• Jürgen Rehm 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ,
• Gregory A. Roth 1 , 2 , 13 ,
• Reed J. D. Sorensen 1 ,
• Sarah Lewington 3 &
• Emmanuela Gakidou   ORCID: orcid.org/0000-0002-8992-591X 1 , 2  

Nature Communications volume  15 , Article number:  4082 ( 2024 ) Cite this article

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• Cardiovascular diseases
• Epidemiology
• Risk factors

Cohort and case-control data have suggested an association between low to moderate alcohol consumption and decreased risk of ischemic heart disease (IHD), yet results from Mendelian randomization (MR) studies designed to reduce bias have shown either no or a harmful association. Here we conducted an updated systematic review and re-evaluated existing cohort, case-control, and MR data using the burden of proof meta-analytical framework. Cohort and case-control data show low to moderate alcohol consumption is associated with decreased IHD risk – specifically, intake is inversely related to IHD and myocardial infarction morbidity in both sexes and IHD mortality in males – while pooled MR data show no association, confirming that self-reported versus genetically predicted alcohol use data yield conflicting findings about the alcohol-IHD relationship. Our results highlight the need to advance MR methodologies and emulate randomized trials using large observational databases to obtain more definitive answers to this critical public health question.

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Introduction.

It is well known that alcohol consumption increases the risk of morbidity and mortality due to many health conditions 1 , 2 , with even low levels of consumption increasing the risk for some cancers 3 , 4 . In contrast, a large body of research has suggested that low to moderate alcohol intake – compared to no consumption – is associated with a decreased risk of ischemic heart disease (IHD). This has led to substantial epidemiologic and public health interest in the alcohol-IHD relationship 5 , particularly given the high prevalence of alcohol consumption 6 and the global burden of IHD 7 .

Extensive evidence from experimental studies that vary short-term alcohol exposure suggests that average levels of alcohol intake positively affect biomarkers such as apolipoprotein A1, adiponectin, and fibrinogen levels that lower the risk of IHD 8 . In contrast, heavy episodic drinking (HED) may have an adverse effect on IHD by affecting blood lipids, promoting coagulation and thus thrombosis risk, and increasing blood pressure 9 . With effects likely to vary materially by patterns of drinking, alcohol consumption must be considered a multidimensional factor impacting IHD outcomes.

A recent meta-analysis of the alcohol-IHD relationship using individual participant data from 83 observational studies 4 found, among current drinkers, that – relative to drinking less than 50 g/week – any consumption above this level was associated with a lower risk of myocardial infarction (MI) incidence and consumption between >50 and <100 g/week was associated with lower risk of MI mortality. When evaluating other subtypes of IHD excluding MI, the researchers found that consumption between >100 and <250 g/week was associated with a decreased risk of IHD incidence, whereas consumption greater than 350 g/week was associated with an increased risk of IHD mortality. Roerecke and Rehm further observed that low to moderate drinking was not associated with reduced IHD risk when accompanied by occasional HED 10 .

The cohort studies and case-control studies (hereafter referred to as ‘conventional observational studies’) used in these meta-analyses are known to be subject to various types of bias when used to estimate causal relationships 11 . First, neglecting to separate lifetime abstainers from former drinkers, some of whom may have quit due to developing preclinical symptoms (sometimes labeled ‘sick quitters’ 12 , 13 ), and to account for drinkers who reduce their intake as a result of such symptoms may introduce reverse causation bias 13 . That is, the risk of IHD in, for example, individuals with low to moderate alcohol consumption may be lower when compared to IHD risk in sick quitters, not necessarily because intake at this level causes a reduction in risk but because sick quitters are at higher risk of IHD. Second, estimates can be biased because of measurement error in alcohol exposure resulting from inaccurate reporting, random fluctuation in consumption over time (random error), or intentional misreporting of consumption due, for example, to social desirability effects 14 (systematic error). Third, residual confounding may bias estimates if confounders of the alcohol-IHD relationship, such as diet or physical activity, have not been measured accurately (e.g., only via a self-report questionnaire) or accounted for. Fourth, because alcohol intake is a time-varying exposure, time-varying confounding affected by prior exposure must be accounted for 15 . To date, only one study that used a marginal structural model to appropriately adjust for time-varying confounding found no association between alcohol consumption and MI risk 16 . Lastly, if exposure to a risk factor, such as alcohol consumption, did not happen at random – even if all known confounders of the relationship between alcohol and IHD were perfectly measured and accounted for – the potential for unmeasured confounders persists and may bias estimates 11 .

In recent years, the analytic method of Mendelian randomization (MR) has been widely adopted to quantify the causal effects of risk factors on health outcomes 17 , 18 , 19 . MR uses single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for the exposure of interest. A valid IV should fulfill the following three assumptions: it must be associated with the risk factor (relevance assumption); there must be no common causes of the IV and the outcome (independence assumption); and the IV must affect the outcome only through the exposure (exclusion restriction or ‘no horizontal pleiotropy’ assumption) 20 , 21 . If all three assumptions are fulfilled, estimates derived from MR are presumed to represent causal effects 22 . Several MR studies have quantified the association between alcohol consumption and cardiovascular disease 23 , including IHD, using genes known to impact alcohol metabolism (e.g., ADH1B/C and ALDH2 24 ) or SNP combinations from genome-wide association studies 25 . In contrast to the inverse associations found in conventional observational studies, MR studies have found either no association or a harmful relationship between alcohol consumption and IHD 26 , 27 , 28 , 29 , 30 , 31 .

To advance the knowledge base underlying our understanding of this major health issue – critical given the worldwide ubiquity of alcohol use and of IHD – there is a need to systematically review and critically re-evaluate all available evidence on the relationship between alcohol consumption and IHD risk from both conventional observational and MR studies.

The burden of proof approach, developed by Zheng et al. 32 , is a six-step meta-analysis framework that provides conservative estimates and interpretations of risk-outcome relationships. The approach systematically tests and adjusts for common sources of bias defined according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria: representativeness of the study population, exposure assessment, outcome ascertainment, reverse causation, control for confounding, and selection bias. The key statistical tool to implement the approach is MR-BRT (meta-regression—Bayesian, regularized, trimmed 33 ), a flexible meta-regression tool that does not impose a log-linear relationship between the risk and outcome, but instead uses a spline ensemble to model non-linear relationships. MR-BRT also algorithmically detects and trims outliers in the input data, takes into account different reference and alternative exposure intervals in the data, and incorporates unexplained between-study heterogeneity in the uncertainty surrounding the mean relative risk (RR) curve (henceforth ‘risk curve’). For those risk-outcome relationships that meet the condition of statistical significance using conventionally estimated uncertainty intervals (i.e., without incorporating unexplained between-study heterogeneity), the burden of proof risk function (BPRF) is derived by calculating the 5th (if harmful) or 95th (if protective) quantile risk curve – inclusive of between-study heterogeneity – closest to the log RR of 0. The resulting BPRF is a conservative interpretation of the risk-outcome relationship based on all available evidence. The BPRF represents the smallest level of excess risk for a harmful risk factor or reduced risk for a protective risk factor that is consistent with the data, accounting for between-study heterogeneity. To quantify the strength of the evidence for the alcohol-IHD relationship, the BPRF can be summarized in a single metric, the risk-outcome score (ROS). The ROS is defined as the signed value of the average log RR of the BPRF across the 15th to 85th percentiles of alcohol consumption levels observed across available studies. The larger a positive ROS value, the stronger the alcohol-IHD association. For ease of interpretation, the ROS is converted into a star rating from one to five. A one-star rating (ROS < 0) indicates a weak alcohol-IHD relationship, and a five-star rating (ROS > 0.62) indicates a large effect size and strong evidence. Publication and reporting bias are evaluated with Egger’s regression and by visual inspection with funnel plots 34 . Further conceptual and technical details of the burden of proof approach are described in detail elsewhere 32 .

Using the burden of proof approach, we systematically re-evaluate all available eligible evidence from cohort, case-control, and MR studies published between 1970 and 2021 to conservatively quantify the dose-response relationship between alcohol consumption and IHD risk, calculated relative to risk at zero alcohol intake (i.e., current non-drinking, including lifetime abstinence or former use). We pool the evidence from all conventional observational studies combined, as well as individually for all three study designs, to estimate mean IHD risk curves. Based on patterns of results established by previous meta-analyses 4 , 35 , we also use data from conventional observational studies to estimate risk curves by IHD endpoint (morbidity or mortality) and further by sex, in addition to estimating risk curves for MI overall and by endpoint. We follow PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines 36 through all stages of this study (Supplementary Information section  1 , Fig.  S1 and Tables  S1 and S2 ) and comply with GATHER (Guidelines on Accurate and Transparent Health Estimates Reporting) recommendations 37 (Supplementary Information section  2 , Table  S3 ). The main findings and research implications of this work are summarized in Table  1 .

We updated the systematic review on the dose-response relationship between alcohol consumption and IHD previously conducted for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 1 . Of 4826 records identified in our updated systematic review (4769 from databases/registers and 57 by citation search and known literature), 11 were eligible based on our inclusion criteria and were included. In total, combined with the results of the previous systematic reviews 1 , 38 , information from 95 cohort studies 26 , 27 , 29 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 27 case-control studies 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , and five MR studies 26 , 27 , 28 , 29 , 31 was included in our meta-analysis (see Supplementary Information section  1 , Fig.  S1 , for the PRISMA diagram). Details on the extracted effect sizes, the design of each included study, underlying data sources, number of participants, duration of follow-up, number of cases and controls, and bias covariates that were evaluated and potentially adjusted for can be found in the Supplementary Information Sections  4 , 5 , and 6 .

Table  2 summarizes key metrics of each risk curve modeled, including estimates of mean RR and 95% UI (inclusive of between-study heterogeneity) at select alcohol exposure levels, the exposure level and RR and 95% UI at the nadir (i.e., lowest RR), the 85th percentile of exposure observed in the data and its corresponding RR and 95% UI, the BPRF averaged at the 15th and 85th percentile of exposure, the average excess risk or risk reduction according to the exposure-averaged BPRF, the ROS, the associated star rating, the potential presence of publication or reporting bias, and the number of studies included.

We found large variation in the association between alcohol consumption and IHD by study design. When we pooled the results of cohort and case-control studies, we observed an inverse association between alcohol at average consumption levels and IHD risk; that is, drinking average levels of alcohol was associated with a reduced IHD risk relative to drinking no alcohol. In contrast, we did not find a statistically significant association between alcohol consumption and IHD risk when pooling results from MR studies. When we subset the conventional observational studies to those reporting on IHD by endpoint, we found no association between alcohol consumption and IHD morbidity or mortality due to large unexplained heterogeneity between studies. When we further subset those studies that reported effect size estimates by sex, we found that average alcohol consumption levels were inversely associated with IHD morbidity in males and in females, and with IHD mortality in males but not in females. When we analyzed only the studies that reported on MI, we found significant inverse associations between average consumption levels and MI overall and with MI morbidity. Visualizations of the risk curves for morbidity and mortality of IHD and MI are provided in Supplementary Information Section  9 (Figs.  S2a –c, S3a –c, and S4a–c ). Among all modeled risk curves for which a BPRF was calculated, the ROS ranged from −0.40 for MI mortality to 0.20 for MI morbidity. In the Supplementary Information, we also provide details on the RR and 95% UIs with and without between-study heterogeneity associated with each 10 g/day increase in consumption for each risk curve (Table  S10 ), the parameter specifications of the model (Tables  S11 and S12 ), and each risk curve from the main analysis estimated without trimming 10% of the data (Fig.  S5a–l and Table  S13 ).

Risk curve derived from conventional observational study data

The mean risk curve and 95% UI were first estimated by combining all evidence from eligible cohort and case-control studies that quantified the association between alcohol consumption and IHD risk. In total, information from 95 cohort studies and 27 case-control studies combining data from 7,059,652 participants were included. In total, 243,357 IHD events were recorded. Thirty-seven studies quantified the association between alcohol consumption and IHD morbidity only, and 44 studies evaluated only IHD mortality. The estimated alcohol-IHD association was adjusted for sex and age in all but one study. Seventy-five studies adjusted the effect sizes for sex, age, smoking, and at least four other covariates. We adjusted our risk curve for whether the study sample was under or over 50 years of age, whether the study outcome was consistent with the definition of IHD (according to the International Classification of Diseases [ICD]−9: 410-414; and ICD-10: I20-I25) or related to specified subtypes of IHD, whether the outcome was ascertained by self-report only or by at least one other measurement method, whether the study accounted for risk for reverse causation, whether the reference group was non-drinkers (including lifetime abstainers and former drinkers), and whether effect sizes were adjusted (1) for sex, age, smoking, and at least four other variables, (2) for apolipoprotein A1, and (3) for cholesterol, as these bias covariates were identified as significant by our algorithm.

Pooling all data from cohort and case-control studies, we found that alcohol consumption was inversely associated with IHD risk (Fig.  1 ). The risk curve was J-shaped – without crossing the null RR of 1 at high exposure levels – with a nadir of 0.69 (95% UI: 0.48–1.01) at 23 g/day. This means that compared to individuals who do not drink alcohol, the risk of IHD significantly decreases with increasing consumption up to 23 g/day, followed by a risk reduction that becomes less pronounced. The average BPRF calculated between 0 and 45 g/day of alcohol intake (the 15th and 85th percentiles of the exposure range observed in the data) was 0.96. Thus, when between-study heterogeneity is accounted for, a conservative interpretation of the evidence suggests drinking alcohol across the average intake range is associated with an average decrease in the risk of IHD of at least 4% compared to drinking no alcohol. This corresponds to a ROS of 0.04 and a star rating of two, which suggests that the association – on the basis of the available evidence – is weak. Although we algorithmically identified and trimmed 10% of the data to remove outliers, Egger’s regression and visual inspection of the funnel plot still indicated potential publication or reporting bias.

The panels show the log(relative risk) function, the relative risk function, and a modified funnel plot showing the residuals (relative to 0) on the x-axis and the estimated standard error that includes the reported standard error and between-study heterogeneity on the y-axis. RR relative risk, UI uncertainty interval. Source data are provided as a Source Data file.

Risk curve derived from case-control study data

Next, we estimated the mean risk curve and 95% UI for the relationship between alcohol consumption and IHD by subsetting the data to case-control studies only. We included a total of 27 case-control studies (including one nested case-control study) with data from 60,914 participants involving 16,892 IHD cases from Europe ( n  = 15), North America ( n  = 6), Asia ( n  = 4), and Oceania ( n  = 2). Effect sizes were adjusted for sex and age in most studies ( n  = 25). Seventeen of these studies further adjusted for smoking and at least four other covariates. The majority of case-control studies accounted for the risk of reverse causation ( n  = 25). We did not adjust our risk curve for bias covariates, as our algorithm did not identify any as significant.

Evaluating only data from case-control studies, we observed a J-shaped relationship between alcohol consumption and IHD risk, with a nadir of 0.65 (0.50–0.85) at 23 g/day (Fig.  2 ). The inverse association between alcohol consumption and IHD risk reversed at an intake level of 61 g/day. In other words, alcohol consumption between >0 and 60 g/day was associated with a lower risk compared to no consumption, while consumption at higher levels was associated with increased IHD risk. However, the curve above this level is flat, implying that the association between alcohol and increased IHD risk is the same between 61 and 100 g/day, relative to not drinking any alcohol. The BPRF averaged across the exposure range between the 15th and 85th percentiles, or 0–45 g/day, was 0.87, which translates to a 13% average reduction in IHD risk across the average range of consumption. This corresponds to a ROS of 0.14 and a three-star rating. After trimming 10% of the data, no potential publication or reporting bias was found.

The panels show the log(relative risk) function, the relative risk function, and a modified funnel plot showing the residuals (relative to 0) on the x-axis and the estimated standard deviation that includes the reported standard deviation and between-study heterogeneity on the y-axis. RR relative risk, UI uncertainty interval. Source data are provided as a Source Data file.

Risk curve derived from cohort study data

We also estimated the mean risk curve and 95% UI for the relationship between alcohol consumption and IHD using only data from cohort studies. In total, 95 cohort studies – of which one was a retrospective cohort study – with data from 6,998,738 participants were included. Overall, 226,465 IHD events were recorded. Most data were from Europe ( n  = 43) and North America ( n  = 33), while a small number of studies were conducted in Asia ( n  = 14), Oceania ( n  = 3), and South America ( n  = 2). The majority of studies adjusted effect sizes for sex and age ( n  = 76). Fifty-seven of these studies also adjusted for smoking and at least four other covariates. Out of all cohort studies included, 88 accounted for the risk of reverse causation. We adjusted our risk curve for whether the study outcome was consistent with the definition of IHD or related to specified subtypes of IHD, and whether effect sizes were adjusted for apolipoprotein A1, as these bias covariates were identified as significant by our algorithm.

When only data from cohort studies were evaluated, we found a J-shaped relationship between alcohol consumption and IHD risk that did not cross the null RR of 1 at high exposure levels, with a nadir of 0.69 (0.47–1.01) at 23 g/day (Fig.  3 ). The shape of the risk curve was almost identical to the curve estimated with all conventional observational studies (i.e., cohort and case-control studies combined). When we calculated the average BPRF of 0.95 between the 15th and 85th percentiles of observed alcohol exposure (0–50 g/day), we found that alcohol consumption across the average intake range was associated with an average reduction in IHD risk of at least 5%. This corresponds to a ROS of 0.05 and a two-star rating. We identified potential publication or reporting bias after 10% of the data were trimmed.

Risk curve derived from Mendelian randomization study data

Lastly, we pooled evidence on the relationship between genetically predicted alcohol consumption and IHD risk from MR studies. Four MR studies were considered eligible for inclusion in our main analysis, with data from 559,708 participants from China ( n  = 2), the Republic of Korea ( n  = 1), and the United Kingdom ( n  = 1). Overall, 22,134 IHD events were recorded. Three studies used the rs671 ALDH2 genotype found in Asian populations, one study additionally used the rs1229984 ADH1B variant, and one study used the rs1229984 ADH1B Arg47His variant and a combination of 25 SNPs as IVs. All studies used the two-stage least squares (2SLS) method to estimate the association, and one study additionally applied the inverse-variance-weighted (IVW) method and multivariable MR (MVMR). For the study that used multiple methods to estimate effect sizes, we used the 2SLS estimates for our main analysis. Further details on the included studies are provided in Supplementary Information section  4 (Table  S6 ). Due to limited input data, we elected not to trim 10% of the observations. We adjusted our risk curve for whether the endpoint of the study outcome was mortality and whether the associations were adjusted for sex and/or age, as these bias covariates were identified as significant by our algorithm.

We did not find any significant association between genetically predicted alcohol consumption and IHD risk using data from MR studies (Fig.  4 ). No potential publication or reporting bias was detected.

As sensitivity analyses, we modeled risk curves with effect sizes estimated from data generated by Lankester et al. 28 using IVW and MVMR methods. We also used effect sizes from Biddinger et al. 31 , obtained using non-linear MR with the residual method, instead of those from Lankester et al. 28 in our main model (both were estimated with UK Biobank data) to estimate a risk curve. Again, we did not find a significant association between genetically predicted alcohol consumption and IHD risk (see Supplementary Information Section  10 , Fig.  S6a–c and Table  S14 ). To test for consistency with the risk curve we estimated using all included cohort studies, we also pooled the conventionally estimated effect sizes provided in the four MR studies. We did not observe an association between alcohol consumption and IHD risk due to large unexplained heterogeneity between studies (see Supplementary Information Section  10 , Fig.  S7, and Table  S14 ). Lastly, we pooled cohort studies that included data from China, the Republic of Korea, and the United Kingdom to account for potential geographic influences. Again, we did not find a significant association between alcohol consumption and IHD risk (see Supplementary Information Section  10 , Fig.  S8, and Table  S14 ).

Conventional observational and MR studies published to date provide conflicting estimates of the relationship between alcohol consumption and IHD. We conducted an updated systematic review and conservatively re-evaluated existing evidence on the alcohol-IHD relationship using the burden of proof approach. We synthesized evidence from cohort and case-control studies combined and separately and from MR studies to assess the dose-response relationship between alcohol consumption and IHD risk and to compare results across different study designs. It is anticipated that the present synthesis of evidence will be incorporated into upcoming iterations of GBD.

Our estimate of the association between genetically predicted alcohol consumption and IHD runs counter to our estimates from the self-report data and those of other previous meta-analyses 4 , 35 , 158 that pooled conventional observational studies. Based on the conservative burden of proof interpretation of the data, our results suggested an inverse association between alcohol and IHD when all conventional observational studies were pooled (alcohol intake was associated with a reduction in IHD risk by an average of at least 4% across average consumption levels; two-star rating). In evaluating only cohort studies, we again found an inverse association between alcohol consumption and IHD (alcohol intake was associated with a reduction in IHD risk by an average of at least 5% at average consumption levels; two-star rating). In contrast, when we pooled only case-control studies, we estimated that average levels of alcohol consumption were associated with at least a 13% average decrease in IHD risk (three-star rating), but the inverse association reversed when consumption exceeded 60 g/day, suggesting that alcohol above this level is associated with a slight increase in IHD risk. Our analysis of the available evidence from MR studies showed no association between genetically predicted alcohol consumption and IHD.

Various potential biases and differences in study designs may have contributed to the conflicting findings. In our introduction, we summarized important sources of bias in conventional observational studies of the association between alcohol consumption and IHD. Of greatest concern are residual and unmeasured confounding and reverse causation, the effects of which are difficult to eliminate in conventional observational studies. By using SNPs within an IV approach to predict exposure, MR – in theory – eliminates these sources of bias and allows for more robust estimates of causal effects. Bias may still occur, however, when using MR to estimate the association between alcohol and IHD 159 , 160 . There is always the risk of horizontal pleiotropy in MR – that is, the genetic variant may affect the outcome via pathways other than exposure 161 . The IV assumption of exclusion restriction is, for example, violated if only a single measurement of alcohol consumption is used in MR 162 ; because alcohol consumption varies over the life course, the gene directly impacts IHD through intake at time points other than that used in the MR analysis. To date, MR studies have not succeeded in separately capturing the multidimensional effects of alcohol intake on IHD risk (i.e., effects of average alcohol consumption measured through frequency-quantity, in addition to the effects of HED) 159 because the genes used to date only target average alcohol consumption that encompasses intake both at average consumption levels and HED. In other words, the instruments used are not able to separate out the individual effects of these two different dimensions of alcohol consumption on IHD risk using MR. Moreover, reverse causation may occur through cross-generational effects 160 , 163 , as the same genetic variants predispose both the individual and at least one of his or her parents to (increased) alcohol consumption. In this situation, IHD risk could be associated with the parents’ genetically predicted alcohol consumption and not with the individual’s own consumption. None of the MR studies included accounted for cross-generational effects, which possibly introduced bias in the effect estimates. It is important to note that bias by ancestry might also occur in conventional observational studies 164 . In summary, estimates of the alcohol-IHD association are prone to bias in all three study designs, limiting inferences of causation.

The large difference in the number of available MR versus conventional observational studies, the substantially divergent results derived from the different study types, and the rapidly developing field of MR clearly argue for further investigation of MR as a means to quantify the association between alcohol consumption and IHD risk. Future studies should investigate non-linearity in the relationship using non-linear MR methods. The residual method, commonly applied in non-linear MR studies such as Biddinger et al. 31 , assumes a constant, linear relationship between the genetic IV and the exposure in the study population; a strong assumption that may result in biased estimates and inflated type I error rates if the relationship varies by population strata 165 . However, by log-transforming the exposure, the relationships between the genetic IV and the exposure as expressed on a logarithmic scale may be more homogeneous across strata, possibly reducing the bias effect of violating the assumption of a constant, linear relationship. Alternatively, or in conjunction, the recently developed doubly ranked method, which obviates the need for this assumption, could be used 166 . Since methodology for non-linear MR is an active field of study 167 , potential limitations of currently available methods should be acknowledged and latest guidelines be followed 168 . Future MR studies should further (i) employ sensitivity analyses such as the MR weighted median method 169 to relax the exclusion restriction assumption that may be violated, as well as applying other methods such as the MR-Egger intercept test; (ii) use methods such as g-estimation of structural mean models 162 to adequately account for temporal variation in alcohol consumption in MR, and (iii) attempt to disaggregate the effects of alcohol on IHD by dimension in MR, potentially through the use of MVMR 164 . General recommendations to overcome common MR limitations are described in greater detail elsewhere 159 , 163 , 170 , 171 and should be carefully considered. With respect to prospective cohort studies used to assess the alcohol-IHD relationship, they should, at a minimum: (i) adjust the association between alcohol consumption and IHD for all potential confounders identified, for example, using a causal directed acyclic graph, and (ii) account for reverse causation introduced by sick quitters and by drinkers who changed their consumption. If possible, they should also (iii) use alcohol biomarkers as objective measures of alcohol consumption instead of or in addition to self-reported consumption to reduce bias through measurement error, (iv) investigate the association between IHD and HED, in addition to average alcohol consumption, and (v) when multiple measures of alcohol consumption and potential confounders are available over time, use g-methods to reduce bias through confounding as fully as possible within the limitations of the study design. However, some bias – due, for instance, to unmeasured confounding in conventional observational and to horizontal pleiotropy in MR studies – is likely inevitable, and the interpretation of estimates should be appropriately cautious, in accordance with the methods used in the study.

With the introduction of the Moderate Alcohol and Cardiovascular Health Trial (MACH15) 172 , randomized controlled trials (RCTs) have been revisited as a way to study the long-term effects of low to moderate alcohol consumption on cardiovascular disease, including IHD. In 2018, soon after the initiation of MACH15, the National Institutes of Health terminated funding 173 , reportedly due to concerns about study design and irregularities in the development of funding opportunities 174 . Although MACH15 was terminated, its initiation represented a previously rarely considered step toward investigating the alcohol-IHD relationship using an RCT 175 . However, while the insights from an RCT are likely to be invaluable, the implementation is fraught with potential issues. Due to the growing number of studies suggesting increased disease risk, including cancer 3 , 4 , associated with alcohol use even at very low levels 176 , the use of RCTs to study alcohol consumption is ethically questionable 177 . A less charged approach could include the emulation of target trials 178 using existing observational data (e.g., from large-scale prospective cohort studies such as the UK Biobank 179 , Atherosclerosis Risk in Communities Study 180 , or the Framingham Heart Study 181 ) in lieu of real trials to gather evidence on the potential cardiovascular effects of alcohol. Trials like MACH15 can be emulated, following the proposed trial protocols as closely as the observational dataset used for the analysis allows. Safety and ethical concerns, such as those related to eligibility criteria, initiation/increase in consumption, and limited follow-up duration, will be eliminated because the data will have already been collected. This framework allows for hypothetical trials investigating ethically challenging or even untenable questions, such as the long-term effects of heavy (episodic) drinking on IHD risk, to be emulated and inferences to broader populations drawn.

There are several limitations that must be considered when interpreting our findings. First, record screening for our systematic review was not conducted in a double-blinded fashion. Second, we did not have sufficient evidence to estimate and examine potential differential associations of alcohol consumption with IHD risk by beverage type or with MI endpoints by sex. Third, despite using a flexible meta-regression tool that overcame several limitations common to meta-analyses, the results of our meta-analysis were only as good as the quality of the studies included. We were able, however, to address the issue of varying quality of input data by adjusting for bias covariates that corresponded to core study characteristics in our analyses. Fourth, because we were only able to include one-sample MR studies that captured genetically predicted alcohol consumption, statistical power may be lower than would have been possible with the inclusion of two-sample MR studies, and studies that directly estimated gene-IHD associations were not considered 23 . Finally, we were not able to account for participants’ HED status when pooling effect size estimates from conventional observational studies. Given established differences in IHD risk for drinkers with and without HED 35 and the fact that more than one in three drinkers reports HED 6 , we would expect that the decreased average risk we found at moderate levels of alcohol consumption would be attenuated (i.e., approach the IHD risk of non-drinkers) if the presence of HED was taken into account.

Using the burden of proof approach 32 , we conservatively re-evaluated the dose-response relationship between alcohol consumption and IHD risk based on existing cohort, case-control, and MR data. Consistent with previous meta-analyses, we found that alcohol at average consumption levels was inversely associated with IHD when we pooled conventional observational studies. This finding was supported when aggregating: (i) all studies, (ii) only cohort studies, (iii) only case-control studies, (iv) studies examining IHD morbidity in females and males, (v) studies examining IHD mortality in males, and (vi) studies examining MI morbidity. In contrast, we found no association between genetically predicted alcohol consumption and IHD risk based on data from MR studies. Our confirmation of the conflicting results derived from self-reported versus genetically predicted alcohol use data highlights the need to advance methodologies that will provide more definitive answers to this critical public health question. Given the limitations of randomized trials, we advocate using advanced MR techniques and emulating target trials using observational data to generate more conclusive evidence on the long-term effects of alcohol consumption on IHD risk.

This study was approved by the University of Washington IRB Committee (study #9060).

The burden of proof approach is a six-step framework for conducting meta-analysis 32 : (1) data from published studies that quantified the dose-response relationship between alcohol consumption and ischemic heart disease (IHD) risk were systematically identified and obtained; (2) the shape of the mean relative risk (RR) curve (henceforth ‘risk curve’) and associated uncertainty was estimated using a quadratic spline and algorithmic trimming of outliers; (3) the risk curve was tested and adjusted for biases due to study attributes; (4) unexplained between-study heterogeneity was quantified, adjusting for within-study correlation and number of studies included; (5) the evidence for small-study effects was evaluated to identify potential risks of publication or reporting bias; and (6) the burden of proof risk function (BPRF) – a conservative interpretation of the average risk across the exposure range found in the data – was estimated relative to IHD risk at zero alcohol intake. The BPRF was converted to a risk-outcome score (ROS) that was mapped to a star rating from one to five to provide an intuitive interpretation of the magnitude and direction of the dose-response relationship between alcohol consumption and IHD risk.

We calculated the mean RR and 95% uncertainty intervals (UIs) for IHD associated with levels of alcohol consumption separately with all evidence available from conventional observational studies and from Mendelian randomization (MR) studies. For the risk curves that met the condition of statistical significance when the conventional 95% UI that does not include unexplained between-study heterogeneity was evaluated, we calculated the BPRF, ROS, and star rating. Based on input data from conventional observational studies, we also estimated these metrics by study design (cohort studies, case-control studies), and by IHD endpoint (morbidity, mortality) for both sexes (females, males) and sex-specific. For sex-stratified analyses, we only considered studies that reported effect sizes for both females and males to allow direct comparison of IHD risk across different exposure levels; however, we did not collect information about the method each study used to determine sex. We also estimated risk curves for myocardial infarction (MI), overall and by endpoint, using data from conventional observational studies. As a comparison, we also estimated each risk curve without trimming 10% of the input data. We did not consider MI as an outcome or disaggregate findings by sex or endpoint for MR studies due to insufficient data.

With respect to MR studies, several statistical methods are typically used to estimate the associations between genetically predicted exposure and health outcomes (e.g., two-stage least squares [2SLS], inverse-variance-weighted [IVW], multivariable Mendelian randomization [MVMR]). For our main analysis synthesizing evidence from MR studies, we included the reported effect sizes estimated using 2SLS if a study applied multiple methods because this method was common to all included studies. In sensitivity analyses, we used the effect sizes obtained by other MR methods (i.e., IVW, MVMR, and non-linear MR) and estimated the mean risk curve and uncertainty. We also pooled conventionally estimated effect sizes from MR studies to allow comparison with the risk curve estimated with cohort studies. Due to limited input data from MR studies, we elected not to trim 10% of the observations. Furthermore, we estimated the risk curve from cohort studies with data from countries that corresponded to those included in MR studies (China, the Republic of Korea, and the United Kingdom). Due to a lack of data, we were unable to estimate a risk curve from case-control studies in these geographic regions.

Conducting the systematic review

In step one of the burden of proof approach, data for the dose-response relationship between alcohol consumption and IHD risk were systematically identified, reviewed, and extracted. We updated a previously published systematic review 1 in PubMed that identified all studies evaluating the dose-response relationship between alcohol consumption and risk of IHD morbidity or mortality from January 1, 1970, to December 31, 2019. In our update, we additionally considered all studies up to and including December 31, 2021, for eligibility. We searched articles in PubMed on March 21, 2022, with the following search string: (alcoholic beverage[MeSH Terms] OR drinking behavior[MeSH Terms] OR “alcohol”[Title/Abstract]) AND (Coronary Artery Disease[Mesh] OR Myocardial Ischemia[Mesh] OR atherosclerosis[Mesh] OR Coronary Artery Disease[TiAb] OR Myocardial Ischemia[TiAb] OR cardiac ischemia[TiAb] OR silent ischemia[TiAb] OR atherosclerosis Outdent [TiAb] OR Ischemic heart disease[TiAb] OR Ischemic heart disease[TiAb] OR coronary heart disease[TiAb] OR myocardial infarction[TiAb] OR heart attack[TiAb] OR heart infarction[TiAb]) AND (Risk[MeSH Terms] OR Odds Ratio[MeSH Terms] OR “risk”[Title/Abstract] OR “odds ratio”[Title/Abstract] OR “cross-product ratio”[Title/Abstract] OR “hazards ratio”[Title/Abstract] OR “hazard ratio”[Title/Abstract]) AND (“1970/01/01”[PDat]: “2021/12/31”[PDat]) AND (English[LA]) NOT (animals[MeSH Terms] NOT Humans[MeSH Terms]). Studies were eligible for inclusion if they met all of the following criteria: were published between January 1, 1970, and December 31, 2021; were a cohort study, case-control study, or MR study; described an association between alcohol consumption and IHD and reported an effect size estimate (relative risk, hazard ratio, odds ratio); and used a continuous dose as exposure of alcohol consumption. Studies were excluded if they met any of the following criteria: were an aggregate study (meta-analysis or pooled cohort); utilized a study design not designated for inclusion in this analysis: not a cohort study, case-control study, or MR study; were a duplicate study: the underlying sample of the study had also been analyzed elsewhere (we always considered the analysis with the longest follow-up for cohort studies or the most recently published analysis for MR studies); did not report on the exposure of interest: reported on combined exposure of alcohol and drug use or reported alcohol consumption in a non-continuous way; reported an outcome that was not IHD or a composite outcome that included but was not limited to IHD, or outcomes lacked specificity, such as cardiovascular disease or all-cause mortality; were not in English; and were animal studies. All screenings of titles and abstracts of identified records, as well as full texts of potentially eligible studies, and extraction of included studies, were done by a single reviewer (SC or HL) independently. If eligible, studies were extracted for study characteristics, exposure, outcome, adjusted confounders, and effect sizes and their uncertainty. While the previous systematic review only considered cohort and case-control studies, our update also included MR studies. We chose to consider only ‘one-sample’ MR studies, i.e., those in which genes, risk factors, and outcomes were measured in the same participants, and not ‘two-sample’ MR studies in which two different samples were used for the MR analysis so that we could fully capture study-specific information. We re-screened previously identified records for MR studies to consider all published MR studies in the defined time period. We also identified and included in our sensitivity analysis an MR study published in 2022 31 which used a non-linear MR method to estimate the association between genetically predicted alcohol consumption and IHD. When eligible studies reported both MR and conventionally estimated effect sizes (i.e., for the association between self-reported alcohol consumption and IHD risk), we extracted both. If studies used the same underlying sample and investigated the same outcome in the same strata, we included the study that had the longest follow-up. This did not apply when the same samples were used in conventional observational and MR studies, because they were treated separately when estimating the risk curve of alcohol consumption and IHD. Continuous exposure of alcohol consumption was defined as a frequency-quantity measure 182 and converted to g/day. IHD was defined according to the International Classification of Diseases (ICD)−9, 410-414, and ICD-10, I20-I25.

The raw data were extracted with a standardized extraction sheet (see Supplementary Information Section  3 , Table  S4 ). For conventional observational studies, when multiple effect sizes were estimated from differently adjusted regression models, we used those estimated with the model reported to be fully adjusted or the one with the most covariates. In the majority of studies, alcohol consumption was categorized based on the exposure range available in the data. If the lower end of a categorical exposure range (e.g., <10 g/day) of an effect size was not specified in the input data, we assumed that this was 0 g/day. If the upper end was not specified (e.g., >20 g/day), it was calculated by multiplying the lower end of the categorical exposure range by 1.5. When the association between alcohol and IHD risk was reported as a linear slope, the average consumption level in the sample was multiplied by the logarithm of the effect size to effectively render it categorical. From the MR study which employed non-linear MR 31 , five effect sizes and their uncertainty were extracted at equal intervals across the reported range of alcohol exposure using WebPlotDigitizer. To account for the fact that these effect sizes were derived from the same non-linear risk curve, we adjusted the extracted standard errors by multiplying them by the square root of five (i.e., the number of extracted effect sizes). Details on data sources are provided in Supplementary Information Section  4 .

Estimating the shape of the risk-outcome relationship

In step two, the shape of the dose-response relationship (i.e., ‘signal’) between alcohol consumption and IHD risk was estimated relative to risk at zero alcohol intake. The meta-regression tool MR-BRT (meta-regression—Bayesian, regularized, trimmed), developed by Zheng et al. 33 , was used for modeling. To allow for non-linearity, thus relaxing the common assumption of a log-linear relationship, a quadratic spline with two interior knots was used for estimating the risk curve 33 . We used the following three risk measures from included studies: RRs, odds ratios (ORs), and hazard ratios (HRs). ORs were treated as equivalent to RRs and HRs based on the rare outcome assumption. To counteract the potential influence of knot placement on the shape of the risk curve when using splines, an ensemble model approach was applied. Fifty component models with random knot placements across the exposure domain were computed. These were combined into an ensemble by weighting each model based on model fit and variation (i.e., smoothness of fit to the data). To prevent bias from outliers, a robust likelihood-based approach was applied to trim 10% of the observations. Technical details on estimating the risk curve, use of splines, the trimming procedure, the ensemble model approach, and uncertainty estimation are described elsewhere 32 , 33 . Details on the model specifications for each risk curve are provided in Supplementary Information section  8 . We first estimated each risk curve without trimming input data to visualize the shape of the curve, which informed knot placement and whether to set a left and/or right linear tail when data were sparse at low or high exposure levels (see Supplementary Information Section  10 , Fig.  S5a–l ).

Testing and adjusting for biases across study designs and characteristics

In step three, the risk curve was tested and adjusted for systematic biases due to study attributes. According to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria 183 , the following six bias sources were quantified: representativeness of the study population, exposure assessment, outcome ascertainment, reverse causation, control for confounding, and selection bias. Representativeness was quantified by whether the study sample came from a location that was representative of the underlying geography. Exposure assessment was quantified by whether alcohol consumption was recorded once or more than once in conventional observational studies, or with only one or multiple SNPs in MR studies. Outcome ascertainment was quantified by whether IHD was ascertained by self-report only or by at least one other measurement method. Reverse causation was quantified by whether increased IHD risk among participants who reduced or stopped drinking was accounted for (e.g., by separating former drinkers from lifetime abstainers). Control for confounding factors was quantified by which and how many covariates the effect sizes were adjusted for (i.e., through stratification, matching, weighting, or standardization). Because the most adjusted effect sizes in each study were extracted in the systematic review process and thus may have been adjusted for mediators, we additionally quantified a bias covariate for each of the following potential mediators of the alcohol-IHD relationship: body mass index, blood pressure, cholesterol (excluding high-density lipoprotein cholesterol), fibrinogen, apolipoprotein A1, and adiponectin. Selection bias was quantified by whether study participants were selected and included based on pre-existing disease states. We also quantified and considered as possible bias covariates whether the reference group was non-drinkers, including lifetime abstainers and former drinkers; whether the sample was under or over 50 years of age; whether IHD morbidity, mortality, or both endpoints were used; whether the outcome mapped to IHD or referred only to subtypes of IHD; whether the outcome mapped to MI; and what study design (cohort or case-control) was used when conventional observational studies were pooled. Details on quantified bias covariates for all included studies are provided in Supplementary Information section  5 (Tables  S7 and S8 ). Using a Lasso approach 184 , the bias covariates were first ranked. They were then included sequentially, based on their ranking, as effect modifiers of the ‘signal’ obtained in step two in a linear meta-regression. Significant bias covariates were included in modeling the final risk curve. Technical details of the Lasso procedure are described elsewhere 32 .

Quantifying between-study heterogeneity, accounting for heterogeneity, uncertainty, and small number of studies

In step four, the between-study heterogeneity was quantified, accounting for heterogeneity, uncertainty, and small number of studies. In a final linear mixed-effects model, the log RRs were regressed against the ‘signal’ and selected bias covariates, with a random intercept to account for within-study correlation and a study-specific random slope with respect to the ‘signal’ to account for between-study heterogeneity. A Fisher information matrix was used to estimate the uncertainty associated with between-study heterogeneity 185 because heterogeneity is easily underestimated or may be zero when only a small number of studies are available. We estimated the mean risk curve with a 95% UI that incorporated between-study heterogeneity, and we additionally estimated a 95% UI without between-study heterogeneity as done in conventional meta-regressions (see Supplementary Information Section  7 , Table  S10 ). The 95% UI incorporating between-study heterogeneity was calculated from the posterior uncertainty of the fixed effects (i.e., the ‘signal’ and selected bias covariates) and the 95% quantile of the between-study heterogeneity. The estimate of between-study heterogeneity and the estimate of the uncertainty of the between-study heterogeneity were used to determine the 95% quantile of the between-study heterogeneity. Technical details of quantifying uncertainty of between-study heterogeneity are described elsewhere 32 .

Evaluating potential for publication or reporting bias

In step five, the potential for publication or reporting bias was evaluated. The trimming algorithm used in step two helps protect against these biases, so risk curves found to have publication or reporting bias using the following methods were derived from data that still had bias even after trimming. Publication or reporting bias was evaluated using Egger’s regression 34 and visual inspection using funnel plots. Egger’s regression tested for a significant correlation between residuals of the RR estimates and their standard errors. Funnel plots showed the residuals of the risk curve against their standard errors. We reported publication or reporting bias when identified.

Estimating the burden of proof risk function

In step six, the BPRF was calculated for risk-outcome relationships that were statistically significant when evaluating the conventional 95% UI without between-study heterogeneity. The BPRF is either the 5th (if harmful) or the 95th (if protective) quantile curve inclusive of between-study heterogeneity that is closest to the RR line at 1 (i.e., null); it indicates a conservative estimate of a harmful or protective association at each exposure level, based on the available evidence. The mean risk curve, 95% UIs (with and without between-study heterogeneity), and BPRF (where applicable) are visualized along with included effect sizes using the midpoint of each alternative exposure range (trimmed data points are marked with a red x), with alcohol consumption in g/day on the x-axis and (log)RR on the y-axis.

We calculated the ROS as the average log RR of the BPRF between the 15th and 85th percentiles of alcohol exposure observed in the study data. The ROS summarizes the association of the exposure with the health outcome in a single measure. A higher, positive ROS indicates a larger association, while a negative ROS indicates a weak association. The ROS is identical for protective and harmful risks since it is based on the magnitude of the log RR. For example, a mean log BPRF between the 15th and 85th percentiles of exposure of −0.6 (protective association) and a mean log BPRF of 0.6 (harmful association) would both correspond to a ROS of 0.6. The ROS was then translated into a star rating, representing a conservative interpretation of all available evidence. A star rating of 1 (ROS: <0) indicates weak evidence of an association, a star rating of 2 (ROS: 0–0.14) indicates a >0–15% increased or >0–13% decreased risk, a star rating of 3 (ROS: >0.14–0.41) indicates a >15–50% increased or >13–34% decreased risk, a star rating of 4 (ROS: >0.41–0.62) indicates a >50–85% increased or >34–46% decreased risk, and a star rating of 5 (ROS: >0.62) indicates a >85% increased or >46% decreased risk.

Statistics & reproducibility

The statistical analyses conducted in this study are described above in detail. No statistical method was used to predetermine the sample size. When analyzing data from cohort and case-control studies, we excluded 10% of observations using a trimming algorithm; when analyzing data from MR studies, we did not exclude any observations. As all data used in this meta-analysis were from observational studies, no experiments were conducted, and no randomization or blinding took place.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The findings from this study were produced using data extracted from published literature. The relevant studies were identified through a systematic literature review and can all be accessed online as referenced in the current paper 26 , 27 , 28 , 29 , 31 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 . Further details on the relevant studies can be found on the GHDx website ( https://ghdx.healthdata.org/record/ihme-data/gbd-alcohol-ihd-bop-risk-outcome-scores ). Study characteristics of all relevant studies included in the analyses are also provided in Supplementary Information Section  4 (Tables  S5 and S6 ). The template of the data collection form is provided in Supplementary Information section  3 (Table  S4 ). The source data includes processed data from these studies that underlie our estimates. Source data are provided with this paper.

Code availability

Analyses were carried out using R version 4.0.5 and Python version 3.10.9. All code used for these analyses is publicly available online ( https://github.com/ihmeuw-msca/burden-of-proof ).

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Acknowledgements

Research reported in this publication was supported by the Bill & Melinda Gates Foundation [OPP1152504]. S.L. has received grants or contracts from the UK Medical Research Council [MR/T017708/1], CDC Foundation [project number 996], World Health Organization [APW No 2021/1194512], and is affiliated with the NIHR Oxford Biomedical Research Centre. The University of Oxford’s Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) is supported by core grants from the Medical Research Council [Clinical Trial Service Unit A310] and the British Heart Foundation [CH/1996001/9454]. The CTSU receives research grants from industry that are governed by University of Oxford contracts that protect its independence and has a staff policy of not taking personal payments from industry. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the final report, or the decision to publish.

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G.A.R. has received support for this manuscript from the Bill and Melinda Gates Foundation [OPP1152504]. S.L. has received grants or contracts from the UK Medical Research Council [MR/T017708/1], CDC Foundation [project number 996], World Health Organization [APW No 2021/1194512], and is affiliated with the NIHR Oxford Biomedical Research Centre. The University of Oxford’s Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) is supported by core grants from the Medical Research Council [Clinical Trial Service Unit A310] and the British Heart Foundation [CH/1996001/9454]. The CTSU receives research grants from industry that are governed by University of Oxford contracts that protect its independence and has a staff policy of not taking personal payments from industry. All other authors declare no competing interests.

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Carr, S., Bryazka, D., McLaughlin, S.A. et al. A burden of proof study on alcohol consumption and ischemic heart disease. Nat Commun 15 , 4082 (2024). https://doi.org/10.1038/s41467-024-47632-7

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Research: Negotiating Is Unlikely to Jeopardize Your Job Offer

• Einav Hart,
• Julia Bear,
• Zhiying (Bella) Ren

A series of seven studies found that candidates have more power than they assume.

Job seekers worry about negotiating an offer for many reasons, including the worst-case scenario that the offer will be rescinded. Across a series of seven studies, researchers found that these fears are consistently exaggerated: Candidates think they are much more likely to jeopardize a deal than managers report they are. This fear can lead candidates to avoid negotiating altogether. The authors explore two reasons driving this fear and offer research-backed advice on how anxious candidates can approach job negotiations.

Imagine that you just received a job offer for a position you are excited about. Now what? You might consider negotiating for a higher salary, job flexibility, or other benefits , but you’re apprehensive. You can’t help thinking: What if I don’t get what I ask for? Or, in the worst-case scenario, what if the hiring manager decides to withdraw the offer?

• Einav Hart is an assistant professor of management at George Mason University’s Costello College of Business, and a visiting scholar at the Wharton School. Her research interests include conflict management, negotiations, and organizational behavior.
• Julia Bear is a professor of organizational behavior at the College of Business at Stony Brook University (SUNY). Her research interests include the influence of gender on negotiation, as well as understanding gender gaps in organizations more broadly.
• Zhiying (Bella) Ren is a doctoral student at the Wharton School of the University of Pennsylvania. Her research focuses on conversational dynamics in organizations and negotiations.

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