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Case Study – Methods, Examples and Guide

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Case Study Research

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.

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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case study method for research

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

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

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

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

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

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

Research bias

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

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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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Writing a Case Study

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What is a case study?

A Map of the world with hands holding a pen.

A Case study is: 

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

What are the different types of case studies?

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

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

Who are your case study participants?

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What is triangulation ? 

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

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How to write a Case Study?

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

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Case Study Research: Methods and Designs

Case study research is a type of qualitative research design. It’s often used in the social sciences because it involves…

Case Study Method

Case study research is a type of qualitative research design. It’s often used in the social sciences because it involves observing subjects, or cases, in their natural setting, with minimal interference from the researcher.

In the case study method , researchers pose a specific question about an individual or group to test their theories or hypothesis. This can be done by gathering data from interviews with key informants.

Here’s what you need to know about case study research design .

What Is The Case Study Method?

Main approaches to data collection, case study research methods, how case studies are used, case study model.

Case study research is a great way to understand the nuances of a matter that can get lost in quantitative research methods. A case study is distinct from other qualitative studies in the following ways:

  • It’s interested in the effect of a set of circumstances on an individual or group.
  • It begins with a specific question about one or more cases.
  • It focuses on individual accounts and experiences.

Here are the primary features of case study research:

  • Case study research methods typically involve the researcher asking a few questions of one person or a small number of people—known as respondents—to test one hypothesis.
  • Case study in research methodology may apply triangulation to collect data, in which the researcher uses several sources, including documents and field data. This is then analyzed and interpreted to form a hypothesis that can be tested through further research or validated by other researchers.
  • The case study method requires clear concepts and theories to guide its methods. A well-defined research question is crucial when conducting a case study because the results of the study depend on it. The best approach to answering a research question is to challenge the existing theories, hypotheses or assumptions.
  • Concepts are defined using objective language with no reference to preconceived notions that individuals might have about them. The researcher sets out to discover by asking specific questions on how people think or perceive things in their given situation.

They commonly use the case study method in business, management, psychology, sociology, political science and other related fields.

A fundamental requirement of qualitative research is recording observations that provide an understanding of reality. When it comes to the case study method, there are two major approaches that can be used to collect data: document review and fieldwork.

A case study in research methodology also includes literature review, the process by which the researcher collects all data available through historical documents. These might include books, newspapers, journals, videos, photographs and other written material. The researcher may also record information using video cameras to capture events as they occur. The researcher can also go through materials produced by people involved in the case study to gain an insight into their lives and experiences.

Field research involves participating in interviews and observations directly. Observation can be done during telephone interviews, events or public meetings, visits to homes or workplaces, or by shadowing someone for a period of time. The researcher can conduct one-on-one interviews with individuals or group interviews where several people are interviewed at once.

Let’s look now at case study methodology.

The case study method can be divided into three stages: formulation of objectives; collection of data; and analysis and interpretation. The researcher first makes a judgment about what should be studied based on their knowledge. Next, they gather data through observations and interviews. Here are some of the common case study research methods:

One of the most basic methods is the survey. Respondents are asked to complete a questionnaire with open-ended and predetermined questions. It usually takes place through face-to-face interviews, mailed questionnaires or telephone interviews. It can even be done by an online survey.

2. Semi-structured Interview

For case study research a more complex method is the semi-structured interview. This involves the researcher learning about the topic by listening to what others have to say. This usually occurs through one-on-one interviews with the sample. Semi-structured interviews allow for greater flexibility and can obtain information that structured questionnaires can’t.

3. Focus Group Interview

Another method is the focus group interview, where the researcher asks a few people to take part in an open-ended discussion on certain themes or topics. The typical group size is 5–15 people. This method allows researchers to delve deeper into people’s opinions, views and experiences.

4. Participant Observation

Participant observation is another method that involves the researcher gaining insight into an experience by joining in and taking part in normal events. The people involved don’t always know they’re being studied, but the researcher observes and records what happens through field notes.

Case study research design can use one or several of these methods depending on the context.

Case studies are widely used in the social sciences. To understand the impact of socio-economic forces, interpersonal dynamics and other human conditions, sometimes there’s no other way than to study one case at a time and look for patterns and data afterward.

It’s for the same reasons that case studies are used in business. Here are a few uses:

  • Case studies can be used as tools to educate and give examples of situations and problems that might occur and how they were resolved. They can also be used for strategy development and implementation.
  • Case studies can evaluate the success of a program or project. They can help teams improve their collaboration by identifying areas that need improvements, such as team dynamics, communication, roles and responsibilities and leadership styles.
  • Case studies can explore how people’s experiences affect the working environment. Because the study involves observing and analyzing concrete details of life, they can inform theories on how an individual or group interacts with their environment.
  • Case studies can evaluate the sustainability of businesses. They’re useful for social, environmental and economic impact studies because they look at all aspects of a business or organization. This gives researchers a holistic view of the dynamics within an organization.
  • We can use case studies to identify problems in organizations or businesses. They can help spot problems that are invisible to customers, investors, managers and employees.
  • Case studies are used in education to show students how real-world issues or events can be sorted out. This enables students to identify and deal with similar situations in their lives.

And that’s not all. Case studies are incredibly versatile, which is why they’re used so widely.

Human beings are complex and they interact with each other in their everyday life in various ways. The researcher observes a case and tries to find out how the patterns of behavior are created, including their causal relations. Case studies help understand one or more specific events that have been observed. Here are some common methods:

1. Illustrative case study

This is where the researcher observes a group of people doing something. Studying an event or phenomenon this way can show cause-and-effect relationships between various variables.

2. Cumulative case study

A cumulative case study is one that involves observing the same set of phenomena over a period. Cumulative case studies can be very helpful in understanding processes, which are things that happen over time. For example, if there are behavioral changes in people who move from one place to another, the researcher might want to know why these changes occurred.

3. Exploratory case study

An exploratory case study collects information that will answer a question. It can help researchers better understand social, economic, political or other social phenomena.

There are several other ways to categorize case studies. They may be chronological case studies, where a researcher observes events over time. In the comparative case study, the researcher compares one or more groups of people, places, or things to draw conclusions about them. In an intervention case study, the researcher intervenes to change the behavior of the subjects. The study method depends on the needs of the research team.

Deciding how to analyze the information at our disposal is an important part of effective management. An understanding of the case study model can help. With Harappa’s Thinking Critically course, managers and young professionals receive input and training on how to level up their analytic skills. Knowledge of frameworks, reading real-life examples and lived wisdom of faculty come together to create a dynamic and exciting course that helps teams leap to the next level.

Explore Harappa Diaries to learn more about topics such as Objectives Of Research , What are Qualitative Research Methods , How To Make A Problem Statement and How To Improve your Cognitive Skills to upgrade your knowledge and skills.

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What Is a Case Study?

Weighing the pros and cons of this method of research

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

case study method for research

Cara Lustik is a fact-checker and copywriter.

case study method for research

Verywell / Colleen Tighe

  • Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

  • Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
  • Gives researchers the chance to collect information on why one strategy might be chosen over another
  • Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

  • It cannot necessarily be generalized to the larger population
  • Cannot demonstrate cause and effect
  • It may not be scientifically rigorous
  • It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

  • Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
  • Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
  • Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

  • Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
  • Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
  • Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
  • Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
  • Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
  • Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

  • Archival records : Census records, survey records, and name lists are examples of archival records.
  • Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
  • Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
  • Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
  • Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
  • Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

  • Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
  • Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
  • Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
  • Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

  • Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
  • Read examples of case studies to gain an idea about the style and format.
  • Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

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

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

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Distinguishing case study as a research method from case reports as a publication type

The purpose of this editorial is to distinguish between case reports and case studies. In health, case reports are familiar ways of sharing events or efforts of intervening with single patients with previously unreported features. As a qualitative methodology, case study research encompasses a great deal more complexity than a typical case report and often incorporates multiple streams of data combined in creative ways. The depth and richness of case study description helps readers understand the case and whether findings might be applicable beyond that setting.

Single-institution descriptive reports of library activities are often labeled by their authors as “case studies.” By contrast, in health care, single patient retrospective descriptions are published as “case reports.” Both case reports and case studies are valuable to readers and provide a publication opportunity for authors. A previous editorial by Akers and Amos about improving case studies addresses issues that are more common to case reports; for example, not having a review of the literature or being anecdotal, not generalizable, and prone to various types of bias such as positive outcome bias [ 1 ]. However, case study research as a qualitative methodology is pursued for different purposes than generalizability. The authors’ purpose in this editorial is to clearly distinguish between case reports and case studies. We believe that this will assist authors in describing and designating the methodological approach of their publications and help readers appreciate the rigor of well-executed case study research.

Case reports often provide a first exploration of a phenomenon or an opportunity for a first publication by a trainee in the health professions. In health care, case reports are familiar ways of sharing events or efforts of intervening with single patients with previously unreported features. Another type of study categorized as a case report is an “N of 1” study or single-subject clinical trial, which considers an individual patient as the sole unit of observation in a study investigating the efficacy or side effect profiles of different interventions. Entire journals have evolved to publish case reports, which often rely on template structures with limited contextualization or discussion of previous cases. Examples that are indexed in MEDLINE include the American Journal of Case Reports , BMJ Case Reports, Journal of Medical Case Reports, and Journal of Radiology Case Reports . Similar publications appear in veterinary medicine and are indexed in CAB Abstracts, such as Case Reports in Veterinary Medicine and Veterinary Record Case Reports .

As a qualitative methodology, however, case study research encompasses a great deal more complexity than a typical case report and often incorporates multiple streams of data combined in creative ways. Distinctions include the investigator’s definitions and delimitations of the case being studied, the clarity of the role of the investigator, the rigor of gathering and combining evidence about the case, and the contextualization of the findings. Delimitation is a term from qualitative research about setting boundaries to scope the research in a useful way rather than describing the narrow scope as a limitation, as often appears in a discussion section. The depth and richness of description helps readers understand the situation and whether findings from the case are applicable to their settings.

CASE STUDY AS A RESEARCH METHODOLOGY

Case study as a qualitative methodology is an exploration of a time- and space-bound phenomenon. As qualitative research, case studies require much more from their authors who are acting as instruments within the inquiry process. In the case study methodology, a variety of methodological approaches may be employed to explain the complexity of the problem being studied [ 2 , 3 ].

Leading authors diverge in their definitions of case study, but a qualitative research text introduces case study as follows:

Case study research is defined as a qualitative approach in which the investigator explores a real-life, contemporary bounded system (a case) or multiple bound systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information, and reports a case description and case themes. The unit of analysis in the case study might be multiple cases (a multisite study) or a single case (a within-site case study). [ 4 ]

Methodologists writing core texts on case study research include Yin [ 5 ], Stake [ 6 ], and Merriam [ 7 ]. The approaches of these three methodologists have been compared by Yazan, who focused on six areas of methodology: epistemology (beliefs about ways of knowing), definition of cases, design of case studies, and gathering, analysis, and validation of data [ 8 ]. For Yin, case study is a method of empirical inquiry appropriate to determining the “how and why” of phenomena and contributes to understanding phenomena in a holistic and real-life context [ 5 ]. Stake defines a case study as a “well-bounded, specific, complex, and functioning thing” [ 6 ], while Merriam views “the case as a thing, a single entity, a unit around which there are boundaries” [ 7 ].

Case studies are ways to explain, describe, or explore phenomena. Comments from a quantitative perspective about case studies lacking rigor and generalizability fail to consider the purpose of the case study and how what is learned from a case study is put into practice. Rigor in case studies comes from the research design and its components, which Yin outlines as (a) the study’s questions, (b) the study’s propositions, (c) the unit of analysis, (d) the logic linking the data to propositions, and (e) the criteria for interpreting the findings [ 5 ]. Case studies should also provide multiple sources of data, a case study database, and a clear chain of evidence among the questions asked, the data collected, and the conclusions drawn [ 5 ].

Sources of evidence for case studies include interviews, documentation, archival records, direct observations, participant-observation, and physical artifacts. One of the most important sources for data in qualitative case study research is the interview [ 2 , 3 ]. In addition to interviews, documents and archival records can be gathered to corroborate and enhance the findings of the study. To understand the phenomenon or the conditions that created it, direct observations can serve as another source of evidence and can be conducted throughout the study. These can include the use of formal and informal protocols as a participant inside the case or an external or passive observer outside of the case [ 5 ]. Lastly, physical artifacts can be observed and collected as a form of evidence. With these multiple potential sources of evidence, the study methodology includes gathering data, sense-making, and triangulating multiple streams of data. Figure 1 shows an example in which data used for the case started with a pilot study to provide additional context to guide more in-depth data collection and analysis with participants.

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Key sources of data for a sample case study

VARIATIONS ON CASE STUDY METHODOLOGY

Case study methodology is evolving and regularly reinterpreted. Comparative or multiple case studies are used as a tool for synthesizing information across time and space to research the impact of policy and practice in various fields of social research [ 9 ]. Because case study research is in-depth and intensive, there have been efforts to simplify the method or select useful components of cases for focused analysis. Micro-case study is a term that is occasionally used to describe research on micro-level cases [ 10 ]. These are cases that occur in a brief time frame, occur in a confined setting, and are simple and straightforward in nature. A micro-level case describes a clear problem of interest. Reporting is very brief and about specific points. The lack of complexity in the case description makes obvious the “lesson” that is inherent in the case; although no definitive “solution” is necessarily forthcoming, making the case useful for discussion. A micro-case write-up can be distinguished from a case report by its focus on briefly reporting specific features of a case or cases to analyze or learn from those features.

DATABASE INDEXING OF CASE REPORTS AND CASE STUDIES

Disciplines such as education, psychology, sociology, political science, and social work regularly publish rich case studies that are relevant to particular areas of health librarianship. Case reports and case studies have been defined as publication types or subject terms by several databases that are relevant to librarian authors: MEDLINE, PsycINFO, CINAHL, and ERIC. Library, Information Science & Technology Abstracts (LISTA) does not have a subject term or publication type related to cases, despite many being included in the database. Whereas “Case Reports” are the main term used by MEDLINE’s Medical Subject Headings (MeSH) and PsycINFO’s thesaurus, CINAHL and ERIC use “Case Studies.”

Case reports in MEDLINE and PsycINFO focus on clinical case documentation. In MeSH, “Case Reports” as a publication type is specific to “clinical presentations that may be followed by evaluative studies that eventually lead to a diagnosis” [ 11 ]. “Case Histories,” “Case Studies,” and “Case Study” are all entry terms mapping to “Case Reports”; however, guidance to indexers suggests that “Case Reports” should not be applied to institutional case reports and refers to the heading “Organizational Case Studies,” which is defined as “descriptions and evaluations of specific health care organizations” [ 12 ].

PsycINFO’s subject term “Case Report” is “used in records discussing issues involved in the process of conducting exploratory studies of single or multiple clinical cases.” The Methodology index offers clinical and non-clinical entries. “Clinical Case Study” is defined as “case reports that include disorder, diagnosis, and clinical treatment for individuals with mental or medical illnesses,” whereas “Non-clinical Case Study” is a “document consisting of non-clinical or organizational case examples of the concepts being researched or studied. The setting is always non-clinical and does not include treatment-related environments” [ 13 ].

Both CINAHL and ERIC acknowledge the depth of analysis in case study methodology. The CINAHL scope note for the thesaurus term “Case Studies” distinguishes between the document and the methodology, though both use the same term: “a review of a particular condition, disease, or administrative problem. Also, a research method that involves an in-depth analysis of an individual, group, institution, or other social unit. For material that contains a case study, search for document type: case study.” The ERIC scope note for the thesaurus term “Case Studies” is simple: “detailed analyses, usually focusing on a particular problem of an individual, group, or organization” [ 14 ].

PUBLICATION OF CASE STUDY RESEARCH IN LIBRARIANSHIP

We call your attention to a few examples published as case studies in health sciences librarianship to consider how their characteristics fit with the preceding definitions of case reports or case study research. All present some characteristics of case study research, but their treatment of the research questions, richness of description, and analytic strategies vary in depth and, therefore, diverge at some level from the qualitative case study research approach. This divergence, particularly in richness of description and analysis, may have been constrained by the publication requirements.

As one example, a case study by Janke and Rush documented a time- and context-bound collaboration involving a librarian and a nursing faculty member [ 15 ]. Three objectives were stated: (1) describing their experience of working together on an interprofessional research team, (2) evaluating the value of the librarian role from librarian and faculty member perspectives, and (3) relating findings to existing literature. Elements that signal the qualitative nature of this case study are that the authors were the research participants and their use of the term “evaluation” is reflection on their experience. This reads like a case study that could have been enriched by including other types of data gathered from others engaging with this team to broaden the understanding of the collaboration.

As another example, the description of the academic context is one of the most salient components of the case study written by Clairoux et al., which had the objectives of (1) describing the library instruction offered and learning assessments used at a single health sciences library and (2) discussing the positive outcomes of instruction in that setting [ 16 ]. The authors focus on sharing what the institution has done more than explaining why this institution is an exemplar to explore a focused question or understand the phenomenon of library instruction. However, like a case study, the analysis brings together several streams of data including course attendance, online material page views, and some discussion of results from surveys. This paper reads somewhat in between an institutional case report and a case study.

The final example is a single author reporting on a personal experience of creating and executing the role of research informationist for a National Institutes of Health (NIH)–funded research team [ 17 ]. There is a thoughtful review of the informationist literature and detailed descriptions of the institutional context and the process of gaining access to and participating in the new role. However, the motivating question in the abstract does not seem to be fully addressed through analysis from either the reflective perspective of the author as the research participant or consideration of other streams of data from those involved in the informationist experience. The publication reads more like a case report about this informationist’s experience than a case study that explores the research informationist experience through the selection of this case.

All of these publications are well written and useful for their intended audiences, but in general, they are much shorter and much less rich in depth than case studies published in social sciences research. It may be that the authors have been constrained by word counts or page limits. For example, the submission category for Case Studies in the Journal of the Medical Library Association (JMLA) limited them to 3,000 words and defined them as “articles describing the process of developing, implementing, and evaluating a new service, program, or initiative, typically in a single institution or through a single collaborative effort” [ 18 ]. This definition’s focus on novelty and description sounds much more like the definition of case report than the in-depth, detailed investigation of a time- and space-bound problem that is often examined through case study research.

Problem-focused or question-driven case study research would benefit from the space provided for Original Investigations that employ any type of quantitative or qualitative method of analysis. One of the best examples in the JMLA of an in-depth multiple case study that was authored by a librarian who published the findings from her doctoral dissertation represented all the elements of a case study. In eight pages, she provided a theoretical basis for the research question, a pilot study, and a multiple case design, including integrated data from interviews and focus groups [ 19 ].

We have distinguished between case reports and case studies primarily to assist librarians who are new to research and critical appraisal of case study methodology to recognize the features that authors use to describe and designate the methodological approaches of their publications. For researchers who are new to case research methodology and are interested in learning more, Hancock and Algozzine provide a guide [ 20 ].

We hope that JMLA readers appreciate the rigor of well-executed case study research. We believe that distinguishing between descriptive case reports and analytic case studies in the journal’s submission categories will allow the depth of case study methodology to increase. We also hope that authors feel encouraged to pursue submitting relevant case studies or case reports for future publication.

Editor’s note: In response to this invited editorial, the Journal of the Medical Library Association will consider manuscripts employing rigorous qualitative case study methodology to be Original Investigations (fewer than 5,000 words), whereas manuscripts describing the process of developing, implementing, and assessing a new service, program, or initiative—typically in a single institution or through a single collaborative effort—will be considered to be Case Reports (formerly known as Case Studies; fewer than 3,000 words).

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Case Study Method

  • Edited by: Roger Gomm , Martyn Hammersley & Peter Foster
  • Publisher: SAGE Publications Ltd
  • Publication year: 2009
  • Online pub date: January 01, 2011
  • Discipline: Anthropology
  • Methods: Case study research , Generalizability , Induction
  • DOI: https:// doi. org/10.4135/9780857024367
  • Keywords: inquiry , knowledge , law , population , social science , sociology , tacit knowledge Show all Show less
  • Print ISBN: 9780761964148
  • Online ISBN: 9780857024367
  • Buy the book icon link

Subject index

This is the most comprehensive guide to the current uses and importance of case study methods in social research. The editors bring together key contributions from the field, which reflect different interpretations of the purpose and capacity of case study research. They address issues such as: the problem of generalizing from the study of a small number of cases; and the role of case study in developing and testing theories. The editors offer in-depth assessments of the main arguments. An annotated bibliography of the literature dealing with case study research makes this an exhaustive and indispensable guide.

Front Matter

  • ACKNOWLEDGEMENTS
  • Introduction
  • 1 | The Case Study Method in Social Inquiry
  • 2 | The Only Generalization Is: There Is No Generalization
  • 3 | Generalizability and the Single-Case Study
  • 4 | Increasing the Generalizability of Qualitative Research
  • 5 | Case Study and Generalization
  • 6 | Case Study and Theory in Political Science
  • 7 | Case and Situation Analysis
  • 8 | The Logical Structure of Analytic Induction
  • 9 | The Quest for Universals in Sociological Research
  • 10 | Small N's and Big Conclusions: An Examination of the Reasoning in Comparative Studies Based on A Small Number of Cases
  • 11 | Cases, Causes. Conjunctures. Stories and Imagery
  • 12 | Case Study and Theory

Back Matter

  • An Annotated Bibliography

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  • Knowledge Base
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  • Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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

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

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

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

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

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

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

case study

 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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  • Open access
  • Published: 22 February 2024

Understanding implementation of findings from trial method research: a mixed methods study applying implementation frameworks and behaviour change models

  • Taylor Coffey   ORCID: orcid.org/0000-0002-6921-8230 1 ,
  • Paula R. Williamson 2 &
  • Katie Gillies 1

on behalf of the Trials Methodology Research Partnership Working Groups

Trials volume  25 , Article number:  139 ( 2024 ) Cite this article

Metrics details

Trial method research produces recommendations on how to best conduct trials. However, findings are not routinely implemented into practice. To better understand why, we conducted a mixed method study on the challenges of implementing trial method research findings into UK-based clinical trial units.

Three stages of research were conducted. Firstly, case studies of completed projects that provided methodological recommendations were identified within trial design, conduct, analysis, and reporting. These case studies were used as survey examples to query obstacles and facilitators to implementing method research. Survey participants were experienced trial staff, identified via email invitations to UK clinical trial units. This survey assessed the case studies’ rates of implementation, and demographic characteristics of trial units through the Consolidated Framework for Implementation Research. Further, interviews were conducted with senior members of trial units to explore obstacles and facilitators in more detail. Participants were sampled from trial units that indicated their willingness to participate in interviews following the survey. Interviews, and analysis, were structured via the Capability, Opportunity, Motivation Model of Behaviour. Finally, potential strategies to leverage lessons learned were generated via the Behaviour Change Wheel.

A total of 27 UK trial units responded to the survey. The rates of implementation across the case studies varied, with most trial units implementing recommendations in trial conduct and only few implementing recommendations in reporting. However, most reported implementing recommendations was important but that they lacked the resources to do so. A total of 16 senior members of trial units were interviewed. Several themes were generated from interviews and fell broadly into categories related to the methods recommendations themselves, the trial units, or external factors affecting implementation. Belief statements within themes indicated resources issues and awareness of recommendations as frequent implementation obstacles. Participation in trial networks and recommendations packaged with relevant resources were cited frequently as implementation facilitators. These obstacles and facilitators mirrored results from the survey. Results were mapped, via the Behaviour Change Wheel, to intervention functions likely to change behaviours of obstacles and facilitators identified. These intervention functions were developed into potential solutions to reduce obstacles and enhance facilitators to implementation.

Conclusions

Several key areas affecting implementation of trial method recommendations were identified. Potential methods to enhance facilitators and reduce obstacles are suggested. Future research is needed to refine these methods and assess their feasibility and acceptability.

Peer Review reports

Clinical trials provide evidence to support decisions about practice in many aspects of healthcare. As well as generating evidence to inform decision making, trials need to, themselves, be informed by evidence in how they are designed, conducted, analysed, and reported to ensure they produce the highest quality outputs [ 1 , 2 , 3 ]. This is essential to guarantee not only that trials contribute to evidence-based practice, but that all phases of the trial ‘lifecycle’ also support efforts to minimise research waste by building on best practice for how to design, conduct, analyse, and report trials [ 1 , 2 , 4 , 5 ].

Research into how best to design, conduct, analyse, and report clinical trials, known as trial method research [ 1 , 3 ], has expanded in recent years. For example, a widely studied aspect of trial conduct is recruitment. One project, the Online Resource for Research in Clinical triAls (ORRCA), is an ongoing effort to scope methodological work in recruitment. In their initial publication, the ORRCA team identified 2804 articles, published up to 2015, regarding recruitment [ 6 ]. Their most recent update in February 2023 found 4813 eligible papers, an increase of 70% in less than 5 years from the initial publication [ 6 , 7 ]. As this is just one area of trial methodology, it represents only a fraction of the work being done in this space. With such a large volume of research being generated, coordinated efforts are needed to ensure that learning is shared across research groups to prevent duplication of effort and promote collaboration. There is recognition across the trial method research community that there is significant variability in terms of whether and how the findings from this methodological research influence ‘practice’ with regard to trial design, conduct, analysis, or reporting [ 3 , 8 , 9 ]. Similar to clinical practice, where evidence can fail to be implemented [ 10 , 11 ], it is critical that the challenges and opportunities to implementing trial method research findings into practice are understood. This understanding will then maximise the potential for this research to improve health by improving the trials themselves.

Barriers to implementation are known to be complex and involve multifactorial influences [ 12 , 13 , 14 ]. Whilst this is established for clinical evidence [ 15 ], it is also likely to be the case for methodological evidence—yet the specific challenges may be different. Implementation science (and in particular the use of behavioural approaches which are theory-informed) provides a rigorous method for identifying, diagnosing, and developing solutions to target factors with the potential to enhance or impede behaviour change and subsequent integration of those changes [ 2 , 10 , 14 , 16 ]. Data generated using these theoretical approaches are likely more reproducible and generalisable than alternatives [ 2 , 16 , 17 , 18 ]. The potential for lessons from behavioural science to investigate who needs to do what differently, to whom, when, and where, within the context of clinical trials is receiving attention across various stages of the trial lifecycle [ 2 ]. The overall aim of this study was to generate evidence for the challenges and opportunities trialists experience with regard to implementing the results from trial method projects that target the design, conduct, analysis, or reporting of trials.

Overall study description

We designed a sequential exploratory mixed methods study with three linked components:

Case studies : which identified existing examples of trial method research projects with actionable outputs that were believed to influence trial design, conduct, analysis, or reporting practice. “Actionable outputs” were defined broadly as any resource, generated from these projects, that has led to an actual or potential change in the design, conduct, analysis, or reporting of trials.

Survey : which identified the broad range, and frequency, of challenges and opportunities to the implementation of trial method research. Participants were trialists from across the UK, specifically the Clinical Research Collaboration (UKCRC) Network of Registered Clinical Trials Units (CTUs). The UKCRC was established to “help improve the quality and quantity of available expertise to carry out UK clinical trials.” ( https://www.ukcrc.org/research-infrastructure/clinical-trials-units/registered-clinical-trials-units/ ).

Interviews : which explored in depth the challenges and opportunities for implementing trial method research from case study examples and general experience in CTU management.

Theoretical considerations and rationale

It is important when selecting theoretical frameworks, and even more so when combining them within one study, to provide an explicit rationale for the choice of framework(s) [ 14 ]. This study utilised a combined theoretical approach, with the Consolidated Framework of Implementation Research (CFIR) [ 13 ] guiding the survey development, and the Capability, Motivation, and Opportunity Model of Behaviour (COM-B) [ 18 ] guiding the interview guide and analysis. CFIR was designed to synthesise the key elements that underpin implementation efforts [ 13 ]. It was selected in this study to guide the survey design because it provided a systematic framework to structure our inquiry. The CFIR is comprehensive in its descriptions of constructs and how they affect implementation across different organisational levels [ 13 ]. As the survey was intended to focus more explicitly on the organisational structure of the CTUs, the CFIR possessed the context-specific language and concepts to describe and prioritise our initial findings. The COM-B, in contrast, is broader in its scope as a general theory of behaviour and behaviour change. As implementation efforts largely rely on the adoption and maintenance of new behaviours, or changes to existing ones, behaviour change theory is useful to describe the determinants of behaviour and how they relate to one another [ 18 ]. This latter point is particularly relevant for implementation efforts as they are likely to consist of multiple changed behaviours, across different contexts, within an organisation to deliver the ultimate objective of research findings [ 19 ]. The COM-B’s capacity to accommodate such complexity outside the prescribed constructs of the CFIR ensured that all relevant factors to implementation are considered [ 14 ]. The approaches are further complementary in their conception of the socio-ecological layers within CTUs in which implementation takes place. Again, the CFIR provides the context-specific labels to, and ability to prioritise, these layers, with the COM-B acting as a methodological “safety net” to further describe or categorise findings. And finally, the COM-B is linked to a method of intervention development (and policy functions), known as the Behaviour Change Wheel (BCW). Through the BCW, nine potential categories of interventions are linked to the behavioural domains of the COM-B [ 18 ]. This link allows potential solutions to be identified based on the domains found to be most relevant or targetable for the behaviour intended to change.

Case studies

Participants.

Members of the Trials Methodology Research Partnership (TMRP) Working Groups ( https://www.methodologyhubs.mrc.ac.uk/about/tmrp/ ) were invited to contribute. Members of these working groups specialise in one or more areas of clinic trial methodology, and all have academic and/or professional interests in improving the quality of trials.

Data collection

An email was sent directly to members of the TMRP Working Group co-leads to solicit case studies of trial method implementation projects with actionable outputs. The email included a brief description of the project and aims of the case study selection, followed by two questions. The first question asked for any examples of trial method research that respondents were aware of. Question 2 asked respondents to provide what they believed were the “actionable outputs” (i.e. the resources generated that lead to implementation of findings) of those methods research projects. Examples of potential actionable outputs could include published papers, guidelines or checklists, template documents, or software packages.

Data analysis

Responses were collated and reviewed by the research team (TC, PW, KG) for their relevance to the four aspects of design, conduct, analysis, and reporting of trials. These responses were compared with a list of published outputs collected by the HTMR ( Network Hubs:: Guidance pack (mrc.ac.uk) ) to ensure a wide-reaching range of available trial method research. One case study was chosen for each domain of trial method research through team consensus, resulting in four case studies incorporated into the survey.

Directors (or individuals nominated by Directors) of the 52 UKCRC-registered CTUs were invited to participate via email from a central list server independent to the research team.

Inclusion and exclusion criteria

Participants were included if they had been involved in any aspect of trial design, delivery, analysis, or reporting within the network of UKCRC-registered CTUs. Any individuals identifying as not reading, writing, or speaking English sufficiently well to participate, or those unable to consent, were excluded.

The survey was designed, and data collected, via the online survey platform Snap (Version 11). A weblink was distributed to the 52 UK CRC-registered CTUs, along with a description of the study, and a Word document version of the survey (available in Additional file 1 : Appendix 1). CTU staff were instructed to distribute this Word version of the survey to members of staff and collate their responses. Collated responses were then entered into the survey at the provided weblink. The survey was designed utilising the Inner Domains of the CFIR [ 13 ] to broadly capture participant views on how trial method research informed the design, conduct, analysis, and reporting of trials run through their CTU. It assessed the perceived organizational structure of the CTU and how those demographics influence the adoption of trial method research. It also asked specific questions about each of the case studies selected from the previous phase. Responses consisted of a mixture of single-choice, Likert scales from 1 to 9 (1 being negative valence and 9 being positive valence), and free-text.

Examples of trial method research projects suggested by respondents (or research area, e.g., recruitment, if no specific project name was given) were collated and frequency counts for each generated. Frequency counts for the types of actionable outputs from these projects were also calculated. Likert scale responses (ranging from 1 to 9) were analysed through descriptive statistics (mean, standard deviation) to compare responses within and between CTUs, the unit of analysis. Some CFIR domains were assessed by more than one question, and so responses to those questions were averaged to give an overall score for the domain. Scores across all domains for a given site were averaged to give a “general implementation” score. The individual scores on measures of these constructs are presented below using a coloured heatmap to highlight areas of high (green) to low (red) activity and provide easy comparison across and within sites. Additional free-text data were analysed using a directed content analysis approach [ 20 ]. Terms and phrases that occurred frequently within this data were collated and then themes summarising barriers and opportunities were generated.

Survey responders indicated their willingness to be contacted for participation in an interview. Emails were sent directly to those who indicated interest in participating.

Recruitment and data collection

Interviews were conducted by a trained qualitative researcher (TC) and structured using a theory-informed topic guide. This topic guide (Additional file 2 : Appendix 2) was developed using the COM-B Model of Behaviour [ 18 ]. Questions prompted interview participants to consider the behavioural influences relevant to implementing findings from trial method research generally and from the selected case studies. Interviews were conducted and recorded through Microsoft Teams. Verbal consent to participate in interviews was obtained and recorded prior to interviews beginning. Recordings were transcribed verbatim by a third party (approved by the University of Aberdeen), de-identified, and checked for accuracy.

Data from interviews were imported into NVivo (V12, release 1.6.1) and analysed initially using a theory-based (COM-B) content analysis [ 20 ], which allowed data to be coded deductively informed by the domains of the COM-B. This involved highlighting utterances within the transcripts and assigning them to one of the six behavioural sub-domains: “psychological capability”, “physical capability”, “social opportunity”, “physical opportunity”, “reflective motivation”, or “automatic motivation”. The next phase of analysis was inductive, allowing identification of additional themes that may have been outside the COM-B domains but were still deemed relevant to the research question. One author (TC) completed coding independently for all interviews. A second author (KG) reviewed a 10% sample of interviews and coded them independently. Coding was then compared for agreement and any discrepancies resolved. Data were compared and coded through a process of constant comparison to provide a summary of key points that interview participants considered to be important. Interview data were specifically explored for any difficulties reported by trialists with regard to the challenges, opportunities, and potential strategies to facilitate the implementation of findings. These data were collected under “belief statements”, which collected similar statements made across participants under a descriptive heading informed by the statements’ COM-B domain. For instance, similar statements on the availability of resources could be collected under a belief statement, “We do not have enough resources”, representing a barrier within the COM-B domain of “physical opportunity”. Belief statements were then analysed for themes across COM-B domains. These themes were developed as narrative summaries of recurrent experiences, barriers, and facilitators to implementation of methods findings. Themes are presented below with their component COM-B domains indicated within the theme’s title. This thematic framework was reviewed, refined, and agreed by consensus of the research team.

Identifying potential solutions

Relevant COM-B domains identified during the interviews and agreed by group consensus were mapped to behavioural intervention functions. Mapping of intervention functions was based on instructions within a behavioural intervention guideline known as the Behaviour Change Wheel (BCW) [ 18 ]. The BCW describes the intervention functions that are believed to influence the individual domains of the COM-B. For example, a lack of psychological capability could be targeted with the intervention function “Education”, which is defined as “increasing knowledge or understanding” [ 18 ]. More than one intervention function is available for each COM-B domain and domains often share one or more intervention functions in common. Utilising the definitions and examples of intervention functions applied to interventions, the research team generated potential solutions based on the available intervention functions targeting the relevant COM-B domains. These solutions were additionally based on the research team’s impressions of targetable belief statements within relevant COM-B domains. For example, if a lack of knowledge was identified (and thus psychological capability) a blanket educational intervention would not necessarily be fit for purpose if only a particular group within an organisation lacked that knowledge whilst others did not. The potential solutions were refined through application of the Affordability, Practicability, Effectiveness and cost-effectiveness, Acceptability, Side-effects and safety, Equity (APEASE) criteria. Application of these criteria to the selection of intervention functions is recommended by the BCW so that research teams can reflect on factors that may limit the relevance and suitability of potential solutions to stakeholders [ 18 ].

Six of 16 Working Group co-leads responded with potential case studies for inclusion. Participants identified a number of trial method research projects, and the project’s outputs, via free-text response to the email prompts. A total of 13 distinct projects were reported by the respondents, primarily in the areas of trial design and analysis, with a particular emphasis on statistical and data collection methods. As a result, case studies for methods research targeting the other two areas of a trial lifecycle, conduct, and reporting, were selected from the list collated by the research team. The four case studies [ 21 , 22 , 23 , 24 ] were selected to consider the variability of project focus across the four areas of trial method research. The selected case studies are described below in Table  1 .

Site demographics

A total of 27 UK CTUs (Table  2 ) responded to the survey, just over half of all UK CRC-registered CTUs ( N  = 52). CTUs were primarily in operation from 10 to 20 years (55%) or more than 20 years (30%). The size of CTUs, by staff number, were divided fairly equally between the small (< 50), medium (50–100), and large (100 +) categories. Most sites characterised themselves as moderately ( n  = 12) to highly stable ( n  = 12) in regard to staff turnover.

Inner domains of the CFIR: culture, implementation climate, networks, and communication

Alongside the structural demographic characteristics described above, we assessed other constructs within the CFIR’s Inner domains. The individual scores on our measures of these constructs are presented in Table  3 below using a coloured heatmap to highlight areas of high to low activity and provide easy comparison across and within sites. Most sites ( n  = 24) achieved general implementation scores between 5 and 7. Typically, scores were reduced due to low ratings for available resources (i.e. money, training, time) within the CTU. Time possessed the lowest individual score, with an average of 3.2 (SD = 1.9). The individual item with the highest average score, 8.2 (SD = 1.3), asked whether relevant findings were believed to be important for the CTU to implement. Finally, available training/education resources were the item with the highest variability across sites, with a standard deviation of 2.2.

Implementation of example case studies

The two case studies that were the most widely implemented were the DAMOCLES charter and the guidelines for statistical analysis plans. Both case studies were implemented fully by a majority of sites ( n  = 21) with a further minority implementing them at least partially ( n  = 5). The recommendations for internal pilots was fully implemented in some sites ( n  = 8), partially in others ( n  = 9), but was not implemented at all in still others ( n  = 10). The RECAP guidance was not implemented at all in 20 sites, partially in five, and fully in two.

Survey participants reported several key obstacles and facilitators to implementation of the case studies. These factors are summarised, along with the degree of implementation of each case study across the CTUs, in Table  4 below. Two of the most frequently cited factors to enhance or hinder implementation related to the dissemination of findings. The first concerned how findings were packaged for dissemination, with survey respondents noting the utility of templates and write-ups of examples. The second related to the communication of new findings. Respondents mentioned professional networks and conferences as useful in keeping CTU staff up to date on relevant methods research. Workshops, presentations, and other events within those networks also provided these same opportunities with the additional benefit of being tailored to translating findings into practice. A frequently mentioned barrier described potentially inadequate dissemination efforts, as participants cited a lack of capacity to “ horizon scan ” for new findings. Time and funding constraints were described as leading to this lack of capacity. Finally within communication, participants reported that if a member of their CTU had been involved in methods research, it was more likely to be implemented.

Participant characteristics

Sixteen individuals (Table  5 ) participated in interviews, representing CTUs from across the UK. Participants were primarily directors or other senior members of their respective CTUs. Half of respondents ( n  = 8) had been in these roles for less than 5 years, with a further seven being in their roles from 5 to 10 years. Most ( n  = 11) had been working in trials generally for 20–29 years.

Interview findings

Interviews were conducted remotely and typically lasted 30–45 min. Belief statements were generated under the domains of the COM-B. Those domains were psychological capability, reflective motivation, automatic motivation, physical opportunity, and social opportunity. Cross-domain themes were generated from related belief statements to summarise overall content. Seven themes were identified: “The influence of funders”, “The visibility of findings”, “The relevance and feasibility of findings”, “Perceived value of implementation research”, “Interpersonal communication”, “Existing work commitments”, and “Cultural drivers of implementation”. Themes are presented in detail below with the relevant COM-B domains to which they are linked presented in parentheses. The themes are further organised into the socio-ecological levels for which they are most relevant, i.e. at the level of the CTU (Internal), outside the CTU (External), or to do with the findings themselves (Findings).

External factors

Theme 1—The influence of funders (social/physical opportunity and reflective motivation).

Interview participants spoke of the influence of funders as important to what trial method research findings are implemented. These influences were comprised of both the resource implication of funding allocation (physical opportunity) as well as the cultural influence that funders possess (social opportunity). With regard to resource implications, there were restrictions on what implementation-related activities trial staff could perform based on the lack of protected time within their roles that could be allocated to implementation (physical opportunity). Secondly, limitations on time were superseded by requirements set out by funders on which trial method research findings needed to be implemented within their trials. If particular findings were deemed necessary by bodies like the NIHR, CTU staff had no choice but to find time to implement them (reflective motivation). Related to these beliefs was the idea that clear efforts at implementing relevant trial method research findings could signal to funders that the CTU team possessed the skills required to conduct trials, thereby increasing the opportunities for funding through a sort of “competitive edge” (reflective motivation).

“I think the progression criteria, as I said, I think is being driven more by the funders expectations rather than anything else, and then other people go, “Well, if the funder expects to see it, I just have to do it,” so then... they might grumble, basically, but if you’re going to put your grant application in, and you want it to be competitive, this is what we have to do.” – Site 7, director

Theme 2—The visibility of findings (social/physical opportunity and psychological capability).

One of the main barriers cited by interviewees was simply knowing about trial method research findings. Participants described the limits on their own time and capacity in “horizon scanning” for new publications and resources, which was often compounded by the sheer volume of outputs (psychological capability).

“I mean probably the greatest competing demand is being up to speed on what’s coming out that’s new. That’s probably where I would feel that… yes, trying to… I know everyone feels like they don’t have enough time to just read and be aware of the stuff coming out, so that’s… I’m more anxious, and I know others are, that there’s stuff being done that we don’t even know about to try and implement, so in some ways we might almost be repeating the wheel of trying to improve best practice in a topic area, and actually someone’s done loads of work on it.” – Site 3, director.

However, interviewees highlighted several resources as means to close this knowledge gap. Dedicated channels for dissemination of important trial method research findings were one means to stay on top of emerging literature. These could be newsletters, websites, or meetings where part, or all, of the agenda was set aside for updates on findings (physical opportunity). Other resources mentioned included more social opportunities to hear about the latest research, at conferences like the International Clinical Trials Methodology Conference (ICTMC) or network events like training and workshops. These events were also cited as important venues to share lessons learned in implementing trial method research findings or to air general frustrations on the complexities of trial conduct and management (social opportunity). Finally, these networking opportunities were identified by interviewees as potent incubators for collaborations, inspiring new trial method projects or establishing links to assess existing ones. Interviewees reported that the opportunity to be involved in these methods projects worked to also raise awareness of their outputs as well as increasing the perceived relevance of these outputs to CTU staff (psychological capability).

“Again, I think I was very aware of [statistical analysis plans] in my previous role as well, so I’d been along to some of the stats group meetings that the CTU networks have run where this had been discussed before it was published. I think they certainly involved a lot of the CTUs in developing that as well and in canvassing comments that went into the paper. I think potentially that would have been easier for people to implement because we’d had some involvement in the developmental bit as well as it went along.” – Site 22, academic

Internal factors

Theme 3—Interpersonal communication (psychological capability, social/physical opportunity, and automatic motivation).

As our participants were senior members of their respective CTUs, they often described aspects of their role and how their efforts mesh with the overall culture of the CTU. A recurrent feature reported by interviewees relating to their role was to be the central figure in communicating the importance of implementation convincingly to their staff and trial sites. This meant they had to advocate for the relevance of trial method research findings to their CTU staff and motivate staff on changing their processes to align with the findings (reflective motivation). This aspect of communication could be more challenging with chief investigators if they were not convinced of the utility of implementation within their own trials, particularly if they anticipated opportunity or resource cost to hosting the research itself or the process changes of implementing findings (social/physical opportunity). Regardless of where it originated, such resistance to change could be frustrating and draining to senior members that were attempting to spearhead implementation efforts (automatic motivation).

“R – Was it ever stressful or frustrating to implement certain things? P – Yes, I would say it can definitely be. I would be lying if I said no. Because change is always.. there’s always a resistance to change in every institution, so it’s not easy to change things. Yes, it can be frustrating, and it can be painful. Things that help are probably when it’s a requirement and when it’s... whatever you do it goes into your SOPs, and then you say, ‘This is how I have to do it, so this is how we will do it.’ But getting to the step of the institution to recognise it, and the people you’re working with, it can be frustrating because there could be arguments like are hard to argue back like, ‘We don’t have the resources, we don’t have the time. Now is not the moment, we’re...’ so there’s all of these things, but also there’s the effort that it takes to convince people that it’s worthwhile doing the change. It’s definitely... it can be frustrating and disappointing, and it takes a lot of energy.” – Site 21, group lead

However, some broader cultural aspects of the CTU appeared to reduce such frustrations. Participants described that their CTU members were often open to new ideas and that such receptivity facilitated implementation (social opportunity). This openness to change was leveraged through the communication skills of senior staff that were previously mentioned and their ability to solicit opinions and feedback from their staff (psychological capability). Such discussions often took place at internal trainings or meetings that incorporated some focus on implementation efforts for the CTU staff (physical opportunity). These opportunities not only afforded discourse on the practicalities of implementation but also helped to raise general awareness of trial method research findings as well as potential adaptations of findings to better suit the individual requirements of the CTU.

“Yes, I mean at our Trials Unit, I run our monthly trial methodology meetings, so these are predominantly attended by statisticians, so we do focus more on trial methodology that’s more statistical in flavour, but we do always cover the new updates and any key publications we’ve seen. I find that’s a great format for getting people interested and excited in these new methods and distilling them down. Generally, across the unit, we have wider… they’re like two forums, just where everyone gets together, and we tend to have bitesize sessions there where we can distil something. Actually, they’re quite useful because internally, we can distil something new to people but in a bitesize chunk so that people are aware and then can take it further and develop specific… if it’s something quite big, then we can develop working groups to look into it and come to a more solid plan of how we can actually implement it if it seems useful.” – Site 25, academic

Theme 4—Existing work commitments (physical opportunity).

Whilst openness to implementation at the CTU, driven by leadership advocating for its importance, was often present in the interviews, resource restrictions were still an ever-present factor impacting the opportunities for CTU staff to improve practice. Interviewees reported that because any change to be implemented required time and effort to action, mentions of these opportunity costs were reflected universally across our sample. The CTU staff, according to their directive, must prioritise the design of new trials and the delivery of ongoing trials.

“But you know, it’s real, it’s a real challenge and intention to be able to keep your eye on the ball and the many different competing priorities that there are. It does sound like a bit of a weak excuse when you say it out loud. So, our focus is on doing the trials, but of course we should always be trying to have an eye on what is the evidence that it’s underpinning what we do in those trials. We should. But with the best will in the world, it’s writing applications, responding to board comments, getting contracts done once things are funded, getting trials underway. The focus is just constantly on that work of trying to win funding and delivering on what you said you were going to deliver, in amongst all the other business of running a CTU or recruiting staff, managing funding contracts, dealing with our institutions, our universities, our local trusts. All the efforts that go into getting trials underway in terms of writing documents and approvals and recruiting sites, you know?” – Site 10, director

Mitigating these resource restrictions often meant looking to other strategies (mentioned in the next theme) that might allow CTU staff to carve out some capacity towards implementation.

Theme 5—Cultural drivers of implementation (psychological capability, physical opportunity, reflective motivation).

As senior members of their respective CTUs, our participants displayed clear motivations to implement trial method research. They expressed that they would like to see the staff in the CTU improve both the uptake of trial method research findings, as well as generating their own method research. This was part of a larger desire to create a culture within their CTUs that encourages and supports research (reflective motivation).

“I hope that within the Trials Unit, I also create an environment where I’m trying to encourage people to not always work to capacity, so they do have the headroom to go away and explore things and to try things and to develop their own research ideas, so that we can say to people okay. Whether it’s looking at different patient information sheets, whether it’s looking at different recruitment strategies, whether it’s looking at different ways of doing data cleaning across sites, looking at different ways of delivering training to people for data entry because we’ve lots of different ways of delivering training and we still get a very high error rate. I’m sure there are other Trials Units that are doing the same thing, so we should be publishing and sharing that with Trials Units. I’m trying to create that environment.” – Site 1, director

Some potential avenues to promote that development were offered by participants. Firstly, participants were confident in their team’s expertise and ability to either generate or implement trial method research findings. This was evidenced through ongoing work being done within their CTU or discussions with their staff on areas they would like to dedicate time to (psychological capability). An important role for the senior members of staff is then to set out expectations for their teams around how they can leverage their expertise within implementing or generating trial method research findings and for senior members to offer the necessary support for that to happen. One option put forward to facilitate this leveraging of expertise was to provide career development opportunities centred on implementation. This could simply be allocating staff’s time to focus on implementation projects, protecting their time from usual work commitments. A further development opportunity would be appointing so-called “ champions ” within the CTU whose explicit role is to identify trial method research findings and coordinate their implementation (physical opportunity).

“Because sometimes what I think is [...] you need a champion, you need every CTU to implement these things and because every trial or every trials unit is composed of different people, so I would probably champion the SAPs part because I’m the statistician, and I make sure that that goes ahead, but someone else needs to champion the one on the patients, probably. Not necessarily. I would champion for all of these things, but because... I think it's finding these people that are the ones that see the value and then be the drivers of the unit. I think that will probably help. […] But I honestly think the best way is just reaching a champion for each of these areas and reaching out to them and saying, ‘Can you... what do you think of this, and what would you do to implement it in your own unit?’” – Site 21, group lead

Factors related to findings

Theme 6—Relevance and feasibility of findings (physical opportunity, reflective motivation, and psychological capability).

Not all findings from trial method research are applicable to all trials and there to all CTUs. For instance, some of our participants mentioned that the progression criteria recommendations were not widely implemented by their CTU staff because they did not often include internal pilots in their trials. So, once the challenges of knowing about trial method research findings are overcome, CTU staff then need to make decisions on what is most relevant to their trial portfolio and what they would like to prioritise implementing (reflective motivation). This prioritisation was dependent on two factors, the CTU staff’s ability to adapt findings to their needs and the implementation resources that findings are packaged with. These factors appeared to be interconnected as sufficient resources to aid implementation, such as training workshops, could reduce the burden of adaptation (physical opportunity). Conversely, staff that perceived their CTU as capable of adaptation could do so even when implementation resources were lacking, such as when trial method research findings are only shared via publication (psychological capability).

“I think that resources that are guidance types widely available, well-advertised, are probably the most... the easiest way. Everything that makes it easier for a person that has this little win of saying, ‘Oh, yes, we’ve probably considered doing things differently,’ anything that minimises that burden in a system I do. For example, with the SAPs, it’s not just the paper and the guidance, but it’s the templates and the little things that you say, ‘Oh, I can start from here, and then if I just use this and this, then the work is so much less […]’ It’s just that thinking of resources that at least create an easy start point for a person that is the right person. I think that would be the best strategy for me, and make them widely available and well-advertised and probably, I don’t know, distribute them, contact the CTUs and say, ‘By the way, here’s a nice resource that you can use if you want to improve this and that.’ I think anything like that could probably be the way I would go around improving the implementation and the uptake because I feel that the goodwill is there.” – Site 21, group lead

Theme 7—Perceived value of implementation (reflective motivation).

Following on from the idea that there is the “ goodwill ” to implement trial method research findings, it was unsurprising that our participants reported believing that implementation research is important. Many believed that uptake of findings had clear benefits to improving the practice of their CTU. Even for those findings of trial method research that were less enthusiastically received, this appeared to be because the CTU staff were already operating at a high standard and that trial method research findings served to simply reassure them of the quality of their practices.

“I guess yes, I would say so, they help enhance them. Thinking about the first one on progression criteria, we didn’t really have any standard in house guidance on that, so actually reaching out and using that was great because we needed something to base it on. Whereas I’d say for the others, with the Damocles ones and the one on SAP guidance, we did already have in house guidelines for SAPs and DMC charters, but these bits of work have helped to inform them. In a way, they help clarify that most of what you are doing is good practice and then some additional things that could be added in.” – Site 25, academic

Alongside the efficiency and quality benefits to the CTU and its practices, participants also described a desire to implement findings from trial method research because of their promise to improve the quality of trials, and the evidence they generate, more broadly. For example, this could be improved efficiency leading to cost-effective trials to free up funding for other research. It could also be participant-centred improvements that have both ethical implications as well as bolstering the public’s trust in the research process. And, most importantly it seemed, improvements across trials would lead to better evidence to base healthcare decisions on. Finally, implementation of findings from trial method research helps to signal that the CTU is dedicated to best practice and is innovative in pursuing those ideals. There was a perception that it can lead to increased reputation amongst peers and the public as well as making the applications from the CTU attractive to funders.

“I think they maybe come under some of the reasons that you said already, but they are incentives to do [implementing trials methods research findings] because we’re all in the business of trying to produce evidence for interventions that are going to make a difference usually in the NHS, not always, but depending what it is that we’re trialling. But ultimately, you know, we’re all in the business of trying to produce evidence that’s going to get used and make a difference to the patients, and if that can happen more quickly, cheaply, more efficiently, trials that are run better with an evidence base underpinning what happens in the trials, then yeah, that’s why we should be doing it. That’s all incentives to do it.” – Site 10, director

As stated above in “Interview findings”, the COM-B domains identified were psychological capability, reflective motivation, automatic motivation, physical opportunity, and social opportunity. These five domains map to all nine intervention functions within the BCW. Two, “Restriction” and “Coercion”, were eliminated due to limited practicability and acceptability. Potential solutions were generated that targeted specific aspects of beliefs within our themes. The primary factors identified across themes were distilled into three intervention targets. Those targets were as follows: awareness of trial method research findings, the effort required to implement findings, and the culture around implementing findings. Eight potential interventions were generated which are listed in Table  6 .

Awareness of trial method research findings

The first proposed intervention is the incorporation of sessions specific to sharing research findings into the agendas of clinical and methodology conferences. These sessions would serve as a dedicated conduit for trialists to share and receive new methods research findings, giving dedicated time and space to do so. The social elements of these sessions would also benefit implementation through less formal opportunities to share feedback and other comments on recommendations that can then be addressed by the associated researchers present.

Effort required to implement findings

The second proposed intervention would target the effort required to implement findings. As time is at a premium within CTUs, any pre-emptive efforts on the part of the methods research teams to ensure their recommendations are accessible, translatable, and clearly relevant to CTU staff will assist in those recommendations being implemented. This could include template documents, case studies of implementation, software packages, etc. Any resource beyond the publication of results would seem desirable to CTU staff to assist in their efforts at implementation.

Changes to culture

The third potential solution identified would target the cultural changes needed to re-prioritise the directions of CTUs towards implementation of findings. This would proceed mainly through a change in funder attitudes towards the importance of trial method research. Funders would need to provide dedicated funding/time within CTU’s contracts and/or trial grants to allow for the proper conduct and/or implementation of trial method research.

Other potential solutions

As many of our reported barriers are interconnected, so too do several of our proposed solutions target multiple barriers/opportunities to improve implementation. Many of these rely primarily on cultural shifts within the CTUs themselves, where existing structures are modified to accommodate implementation efforts. For example, ensuring that CTU meeting agendas incorporate dedicated time towards discussing implementation efforts or for roles to be established/re-structured that focus on championing these efforts.

This paper presents findings from our mixed methods study on the challenges and opportunities to implementing trial method research findings. Exploration of notable trial method research findings generated four cases studies that were used to solicit implementation experiences from trial staff through survey and interviews. The survey data allowed us to identify trends in the adoption of the case studies in a sample of half of the registered CTUs within the UK. Demographic data from participating CTUs demonstrated some similarities in implementation factors that are consistent across sites, such as a lack of resources. More positive similarities were identified as well, such as the shared belief that implementation research is important. Participants volunteered a number of motivators, such as adhering to best practice, or barriers, such as time/resource limitations, that affected their CTU’s implementation of these case studies and trial method research findings more generally. Our interviews with senior CTU staff further explored these motivators and barriers to implementation through a behavioural lens. A range of relevant themes across three socio-ecological levels (Findings, Internal, and External) were identified from our behavioural analysis.

Findings-level factors that affected implementation related to the quality and accessibility of the research and its outputs, and its perceived relevance to the trials undertaken in the CTUs. Trial method research findings that were ‘well-packaged’ (e.g., included templates or easy to follow guidance) were believed to assist in implementation. Findings that had clear benefits to the work done at a CTU, such as streamlining processes, or the outcomes of the trials themselves, such as improving their quality, were more readily implemented. Factors internal to the CTUs included the interpersonal communication of the staff, their existing workloads, and the culture surrounding implementation. Open communication between members of the CTU, spearheaded by senior staff, seemed to increase buy-in from staff on the relevance of trial method research findings. This buy-in would appear essential to motivate staff that are already stretched thin by their commitments to design and deliver trials. Efforts to improve cultural expectations around implementation were seen as a mechanism to create further opportunities for staff to dedicate to adopting findings. These efforts could be restructuring current staff roles or establishing new ones with a greater focus on implementation rather than strictly trial delivery. External factors affecting implementation of trial method research findings were primarily those linked with the expectations of funders and the availability of findings. Funders were said to drive both cultural expectations related to best practice, as well as creating capacity (or not) for CTU staff through provision of funds that could allow dedicated time for implementation efforts. The availability of findings had to do largely with the channels available for dissemination of findings. The more opportunities trialists had to be exposed to findings, the more likely they were to adopt those findings in their respective CTUs.

Strengths and limitations

Our project has several key strengths. The mixed methods nature of its design allowed for a more complete investigation of implementation factors than either quantitative or qualitative measures alone. The project utilised a combined theoretical approach, taking advantage of the CFIR in survey design and the COM-B in interview design and analysis. The combination of these approaches ensured that our project had the investigative potential to explore the specific implementation factors and general behavioural factors undermining the successful implementation of trial method research. Others have taken a similar epistemological approach in combining the CFIR and COM-B (and the related Theoretical Domains Framework) to investigate challenges in other contexts [ 14 , 25 , 26 , 27 ].

Our project solicited input from a variety of stakeholders in CTUs across the UK to ensure a diverse perspective on implementation challenges. However, our sample was primarily those with a statistics background, along with the number of responses to identify case studies being relatively low. We attempted to correct for this low response rate and homogeneity of response by agreeing as a team which case studies to include outside those offered by our respondents. However, we cannot say how selection of other case studies may have affected our responses to the surveys and interviews. It may be that particular projects had inherently different challenges to implementation that are not represented here. However, by including general organisational-level factors that may influence implementation, we have identified factors that are likely to be generalisable to a range of implementation efforts. A further bias is one of self-selection. It is possible that the CTUs and members that responded to our invitations are more active in implementing trial method research findings and would thus be more interested in participating in the project. It may also be that those CTUs that face the most challenges did not have the capacity or motivation to respond to our invitation due to the time it would take away from trial delivery. This may help to explain our response rate of about half of the 52 registered CTUs. Responses could have also been limited in our surveys as we asked CTUs to collate their answers. This may have led to unintended desirability effects, with some staff feeling unable to offer honest opinions on their CTU.

Recommendations for future

This project has identified a number of areas for future efforts in improving the implementation of trial method research findings. The themes described here can provide a starting point for trial method researchers to consider when implementing and/or disseminate findings from method research. This could include creating plans for how the findings will reach the appropriate CTU teams, how to articulate the importance of findings to those teams, or how to best package those findings to make them more readily accessible, and thus implementable, for the CTU teams. Further, it could prompt methods researchers to consider who should be involved in their research and when, potentially incorporating members from different institutions and organisations who would be required to implement any findings and doing so earlier in the process.

Where these obstacles still exist, future research on the implementation of findings can bridge the gap between research and practice. Our approach describes obstacles and facilitators in a standardised language common to behavioural and implementation science. Along with this clearer articulation of what works, for whom, how, why, and when, links to behavioural theory provides a process to design interventions [ 18 , 28 ]. Although we have identified some preliminary intervention options, future work could produce potential options not accounted for here, but utilising lessons learned from our findings. Further development of these strategies through selection of BCTs targeting one or more of the identified areas for improvement, refined through co-production with stakeholders, would be the next stage of the intervention design process [ 18 , 29 ]. Finally, assessment of the effectiveness of these interventions in improving the implementation of trial method research findings would be warranted. Additionally, as our project was sampled from UK CTUs, further work could explore the generalisability of these findings to settings outside the UK, particularly where trial units are noticeably different in their organisation.

We have presented findings exploring the obstacles and facilitators to the implementation of trial method research findings. Challenges facing CTUs at multiple levels, including demands on time and resources, internal organisational structure, and quality of findings, greatly affect their staff’s ability to incorporate findings into their workflow. We have suggested several potential areas to target with further intervention development based on behavioural theory to maximise the potential for change. These strategies, and others, would need to face refinement and the scrutiny of stakeholders, as well as evaluation of their effectiveness. Ultimately, our project highlights the motivation of trial staff to deliver quality trials underpinned by the latest evidence. However, this motivation is hindered by the realities of ongoing trial logistics and the difficulties faced in identifying this evidence. Trial methodologists will need to work closely with CTU staff, funders, and regulatory bodies to set priorities on what needs to be implemented and how to make that more achievable in light of the challenges faced.

Availability of data and materials

The dataset supporting the conclusions of this article is included within the article (and its additional files). Additional data is available upon reasonable request.

Abbreviations

Affordability, Practicability, Effectiveness and cost-effectiveness, Acceptability, Side-effects and safety, Equity

Behaviour change technique

Behaviour change wheel

Consolidated Framework of Implementation Research

Capability, Motivation, and Opportunity Model of Behaviour

Clinical trial unit

DAta MOnitoring Committees: Lessons, Ethics, Statistics

Enhancing the QUAlity and Transparency Of health Research

Hubs for Trial Methodology Research

International Clinical Trials Methodology Conference

Medical Research Council

National Institute for Health and care Research

Online Resource for Research in Clinical triAls

REporting Clinical trial results Appropriately to Participants

Statistical analysis plans

Trials Methodology Research Partnership

UK Clinical Research Collaboration

Welcome to ORRCA. https://www.orrca.org.uk/ . 2023

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Acknowledgements

We would like to thank the members of the TMRP working groups that participated in the case study exercise. We would also like to thank all the participants within the survey and interviews.

This project was supported by the MRC – NIHR funded Trials Methodology Research Partnership (MR/S014357/1).

The Health Services Research Unit, Institute of Applied Health Sciences (University of Aberdeen), is core-funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. They were not involved in the design of the study or the collection, analysis, and interpretation of data.

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TC contributed to the conceptualisation of the study and was responsible for the design and conduct of the case study selection, surveys, and interviews. TC also analysed all data and was the primary author of the manuscript. KG contributed to the conceptualisation of the study, data quality and analysis checks, along with contributing to drafting of the manuscript, providing edits and final approval. PW contributed to the conceptualisation of the study, edits and final approval of the manuscript.

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This study was approved by the University of Aberdeen College Ethics Review Board (CERB) (Application No. SERB/2022/4/2340). Informed consent was obtained from all participants.

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Supplementary Information

Additional file 1: appendix 1..

Survey with PIL. Word document version of the survey circulated to CTUs, which includes a PIL section.

Additional file 2: Appendix 2.

COM-B topic guide. Topic guide used during interviews.

Additional file 3:

Domain 1. Research team and reflexivity.

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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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Sarah Crowe & Anthony Avery

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

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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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BMC Medical Research Methodology

ISSN: 1471-2288

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Qualitative methods and evaluation research. The case of evaluation of a postgraduate program

  • Μιχάλης Χριστοδούλου Aristotle University of Thessaloniki
  • Ελένη Μαρία Κουϊμτζή Aristotle University of Thessaloniki

The aim of the present article is to highlight the contribution of qualitative methods to evaluation research. For this purpose, we present the research design and the findings from evaluation research that we carried out in a postgraduate program (MSc) of a Greek university. Emphasis is placed on methodological details, such as the relevance between interview guide and data analysis, the process of analysis through Template Analysis (TA) and the way of presenting the data. TA was chosen because codes are depicted as in the form of hierarchical dendrograms. The use of TA has an additional methodological benefit since dendrograms can provide the base for constructing a quantitative research instrument in a succeeding study. In that sense we embrace the idea that qualitative methods can fruitfully be brought into dialogue with quantitative methods when designing evaluation research.

Author Biographies

Μιχάλης χριστοδούλου, aristotle university of thessaloniki.

Επίκουρος Καθηγητής Παιδαγωγικού Τμήματος Δημοτικής Εκπαίδευσης Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης

Ελένη Μαρία Κουϊμτζή, Aristotle University of Thessaloniki

Εργαστηριακό & Διδακτικό Προσωπικό Παιδαγωγικό Τμήμα Δημοτικής Εκπαίδευσης Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης

Education Sciences 2024, Issue 1 COVER

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  • Published: 12 February 2024

Markers of imminent myocardial infarction

  • Stefan Gustafsson 1   na1 ,
  • Erik Lampa 1   na1 ,
  • Karin Jensevik Eriksson   ORCID: orcid.org/0000-0003-1946-7068 2 ,
  • Adam S. Butterworth   ORCID: orcid.org/0000-0002-6915-9015 3 , 4 , 5 , 6 ,
  • Sölve Elmståhl 7 ,
  • Gunnar Engström 7 ,
  • Kristian Hveem 8 , 9 ,
  • Mattias Johansson 10 ,
  • Arnulf Langhammer 8 , 11 ,
  • Lars Lind 1 ,
  • Kristi Läll 12 ,
  • Giovanna Masala 13 ,
  • Andres Metspalu 12 ,
  • Conchi Moreno-Iribas 14 , 15 ,
  • Peter M. Nilsson 7 ,
  • Markus Perola 16 ,
  • Birgit Simell 16 ,
  • Hemmo Sipsma 17 ,
  • Bjørn Olav Åsvold 8 , 9 , 18 ,
  • Erik Ingelsson 1 ,
  • Ulf Hammar 1 , 19 ,
  • Andrea Ganna 20 , 21 , 22 ,
  • Bodil Svennblad 2 , 23 ,
  • Tove Fall 1 , 19 &
  • Johan Sundström   ORCID: orcid.org/0000-0003-2247-8454 1 , 24  

Nature Cardiovascular Research volume  3 ,  pages 130–139 ( 2024 ) Cite this article

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  • Myocardial infarction
  • Prognostic markers

Myocardial infarction is a leading cause of death globally but is notoriously difficult to predict. We aimed to identify biomarkers of an imminent first myocardial infarction and design relevant prediction models. Here, we constructed a new case–cohort consortium of 2,018 persons without prior cardiovascular disease from six European cohorts, among whom 420 developed a first myocardial infarction within 6 months after the baseline blood draw. We analyzed 817 proteins and 1,025 metabolites in biobanked blood and 16 clinical variables. Forty-eight proteins, 43 metabolites, age, sex and systolic blood pressure were associated with the risk of an imminent first myocardial infarction. Brain natriuretic peptide was most consistently associated with the risk of imminent myocardial infarction. Using clinically readily available variables, we devised a prediction model for an imminent first myocardial infarction for clinical use in the general population, with good discriminatory performance and potential for motivating primary prevention efforts.

Despite declining age-standardized rates, myocardial infarction remains the leading and increasing cause of death globally 1 . Prevention of myocardial infarction is highly prioritized 2 , but the targeting of primary preventive efforts is hampered by inefficient means of identifying individuals at the highest risk for an imminent myocardial infarction (IMI). This could be partially explained by the inability of most risk prediction models to account for the highly dynamic nature of the period leading up to a myocardial infarction. For instance, traumatic events, such as a cancer diagnosis or loss of a spouse, markedly increase the risk of myocardial infarction 3 , 4 . In addition, the degree of stenosis in the culprit lesion in the coronary artery appears to increase in the months just before the myocardial infarction 5 . Nonetheless, to date, most biomarkers have been investigated over several years of follow-up because of a low number of individuals with a first myocardial infarction shortly after baseline in the general population. Hence, a large population-based study focusing on identifying biomarkers of an IMI is needed.

Primary prevention for asymptomatic risk factors over a long period is costly, and motivation among patients and providers is limited even for secondary prevention 6 . Risk prediction in the short term based on biomarkers of IMI might tilt the scales for prevention, as the knowledge of an increased risk of a first myocardial infarction within the ensuing few months might motivate patients and doctors to consider preventive strategies.

We hypothesized that circulating biomarkers of the dynamic biological processes that operate in the months preceding a myocardial infarction could be measured and used to assess risk. We tested this in a new nested case–cohort study and devised a prediction model for an imminent first myocardial infarction.

We assembled a new nested case–cohort study, the Markers of Imminent Myocardial Infarction (MIMI) study. The study includes initially cardiovascular disease-free individuals in six European general population-based cohorts who developed a myocardial infarction within the first 6 months after the baseline examination, with up to four cohort representatives per case (Fig. 1 and Supplementary Table 1 ). The case–cohort design allows for time-to-event analyses and derivation of accurate prediction models; it is also less prone to certain biases than the case–control design 7 . After exclusions, data of 2,018 individuals weighted to represent the full cohort of 169,053 persons were available for analysis (420 IMI cases and 1,598 subcohort representatives). Their characteristics at baseline are shown in Extended Data Table 1 .

figure 1

The distribution of MIMI participants across Europe is shown, with the participating countries and cohort centers indicated. Cases ( n  = 420) were initially sampled, and center-specific strata based on sex and median age were constructed. From each cohort center, up to four subcohort representatives were drawn for each case from the same stratum. A subcohort ( n  = 1,598) weighted to represent the total cohort ( N  = 169,053) based on the number of individuals in the age and sex strata in the total cohort was thus assembled. NA, not applicable.

Thereafter, we determined the levels of 817 proteins (some duplicates) and 1,025 metabolites in biobanked plasma samples from the cohort baseline examinations in a core laboratory and harmonized 16 clinical variables between the cohorts. We divided the study sample into a discovery sample (EpiHealth, Trøndelag Health Study (HUNT) and Lifelines; 70% of the sample) and an external validation sample (European Prospective Investigation into Cancer and Nutrition—Cardiovascular Disease (EPIC-CVD), Estonian Biobank study and Malmö Preventive Project (MFM); 30% of the sample). Considering the limited sample size of the study, we also performed an internal validation as an exploratory analysis by randomly splitting the study sample into a 70:30 discovery/validation sample, repeated in 100 random draws.

We investigated the associations of proteins, metabolites and clinical variables with the risk of a first myocardial infarction within 6 months after baseline using weighted, stratified Cox proportional-hazards regression models in the discovery sample. Biomarkers that passed multiple testing bounds (a Benjamini–Hochberg false discovery rate (FDR) of <0.05) were verified in the same models in the validation sample (this was done in the external and internal validation sets), with directionally consistent results at P  < 0.05 considered replicated.

In one-by-one models adjusting for technical covariates (season, storage time and plate; Fig. 2 ), 48 proteins, 43 metabolites and 3 clinical variables (age, sex and systolic blood pressure) were found to be associated with IMI after the discovery–validation process (Fig. 3 and Supplementary Table 2 ).

figure 2

The associations of 817 proteins, 1,025 metabolites and 16 clinical variables with the risk of a first myocardial infarction within 6 months in the full MIMI study, adjusted for technical covariates, are shown by biomarker category (clinical, metabolite or protein). HR relates to a doubling of the concentration of proteins and metabolites and a one-unit higher level of clinical biomarkers on their original scale (for example, years, mmol l −1 ). The top 25 biomarkers that passed external validation and ranked on how many internal validation splits the biomarker passed the replication criteria in the model adjusted for technical covariates in addition to the external validation are highlighted. a IL-6 and b KIM1 were measured on multiple Olink panels and tested in separate statistical tests. n  = 420 cases and 1,598 noncases.

figure 3

The top 25 biomarkers that passed external validation and ranked on how many internal validation splits the biomarker passed the replication criteria in the model adjusted for technical covariates in addition to the external validation are shown. Each predictor is represented by two rows, with the discovery result (blue) presented first and the validation result presented second (red). The results are sorted by predictor type (clinical, metabolite or protein) and effect size from the combined analysis of the discovery and validation samples. P value was calculated based on a 2 d.f. Wald test for metabolites analyzed using the missing indicator method (biomarker and missing indicator) and a 1 d.f. Wald test otherwise (biomarker only), two-sided in both cases. The 95% CI of the point estimate (log(HR)) was calculated for the biomarker only and might include 1 even if P  < 0.05 from the 2 d.f. (biomarker + indicator) Wald test. a IL-6 and b KIM1 values were determined from multiple Olink panels and tested in separate statistical tests. n  = 296 cases and 1,121 noncases in the discovery sample; n  = 124 cases and 477 noncases in the validation sample.

Thereafter, we investigated promising markers in models further adjusting for age and sex. Among them, brain natriuretic peptide (BNP) was the only biomarker with a borderline significant association with IMI (HR per doubling of BNP level (95% confidence interval (95% CI)) = 1.33 (1.15, 1.55), P  = 1.63 × 10 −4 , FDR = 0.11 in the discovery sample and 1.40 (1.00, 1.94), P  = 0.049 in the validation sample; Extended Data Fig. 2 ). BNP was the only biomarker with a suggestive association in the internal validation, passing the formal replication criteria in 22 of 100 random splits. By comparison, stem cell factor (SCF) and interleukin-6 (IL-6), biomarkers with a weaker support of an association, replicated in only 5 or 4 of 100 random splits. The cumulative hazard of IMI by fourths of BNP is shown in Extended Data Fig. 3 . The associations of BNP with IMI in sensitivity analyses excluding one cohort at a time and in a random-effects meta-analysis were similar, as shown in Extended Data Figs. 4 and 5 . For some of the 94 variables, we observed substantial between-cohort heterogeneity in the estimates when they were evaluated in a random-effects meta-analysis (Supplementary Table 3 ). The addition of interaction terms between sex and the biomarkers did not reveal any additional associations. Associations with IMI within 3 months (185 cases) were similar to those within 6 months (Extended Data Fig. 6 ).

In a model investigating the total effect of the BNP–IMI association (with a priori selected confounders, not mediators, according to Extended Data Fig. 1 ), adjusting for age, sex, weight, height, creatinine and systolic blood pressure, the association of BNP with IMI remained similar (HR (95% CI) = 1.34 (1.14, 1.57), P  = 3.12 × 10 −4 in the discovery sample and 1.51 (1.05, 2.18), P  = 0.028 in the validation sample; per doubling of BNP level).

We then investigated the association of the most promising marker, BNP, with the coronary artery calcium score (CACS) at a cardiac computer tomography examination in an external population-based cohort of 1,586 participants of the Swedish CArdioPulmonary bioImage Study (SCAPIS) who were free from self-reported cardiovascular disease. Here, a higher CACS was not notably associated with a higher BNP level (odds ratio (95% CI) = 1.14 (0.91, 1.42), P  = 0.25; per doubling of BNP level) in an ordinal regression model adjusting for the same covariates as in the total-effects model.

Finally, we investigated the possibility of developing a clinical risk prediction algorithm for a first IMI using clinically available variables and a weighted Cox ridge regression model. The prediction model achieved an internally validated C-index of 0.78, indicating a good ability to discriminate between IMI cases and noncases. When validating the model in the UK Biobank, a C-index of 0.82 was obtained, while a calibration plot showed some overestimation of 6-month IMI risks. As a comparison, the recalibrated SCORE2 achieved C-indexes of 0.77 (MIMI cohort) and 0.81 (UK Biobank) and overestimated the IMI risks in both samples (Extended Data Fig. 7 ). A nomogram based on the model is shown in Fig. 4 , with a worked example of its intended use displayed in Extended Data Fig. 8 and its cross-validated calibration presented in Extended Data Fig. 7 . An interactive web application is presented at miscore.org . Coefficients for predicting IMI from the model are shown in Supplementary Table 4 .

figure 4

A nomogram for predicting IMI risk based on the final clinical model is shown. Each variable value contributes points (ruler at the top) that are summed up and translated to the predicted risk of a myocardial infarction within 6 months (bottom two rulers). Equation, β coefficients, 6-month survival and mean variable values are provided in Supplementary Table 4 . A worked example is shown in Extended Data Fig. 8 . The model is also presented as an interactive web application at miscore.org.

No biomarkers improved risk prediction in a LASSO (least absolute shrinkage and selection operator) Cox regression model; the variable selection by the LASSO was unstable, with the 95% bootstrap CI on the model size being 0–128 variables. No biomarkers improved risk prediction in a random forest model using 2,000 trees; it also ranked BNP and N-terminal pro-BNP (NT-proBNP) at the top but with very large CIs (Supplementary Table 5 ).

We here set out to identify and test biomarkers and the predictability of an imminent first myocardial infarction using a new case–cohort consortium of individuals without prior cardiovascular disease and with biobanked blood samples. From more than 1,800 biomarkers, we identified 48 proteins, 43 metabolites and 3 clinical variables associated with the risk of an imminent first myocardial infarction independent of technical covariates. Further analyses revealed BNP as the only biomarker consistently associated with IMI risk. We also derived a prediction model to discriminate between subsequent cases and noncases. The IMI phenotype has rarely been studied prospectively in the general population and with a broad panel of biomarkers. The findings may have implications for both clinical primary prevention studies and further etiological studies.

In the current study, higher BNP levels in individuals without a known cardiovascular disease were linked to a higher risk of a first myocardial infarction within 6 months in several models. Cardiomyocytes produce BNP in response to strain 8 , and NT-proBNP measurement is a pillar of the clinical management of heart failure 9 but is not used in diagnosing myocardial infarction 10 . Diastolic dysfunction is an early feature of myocardial ischemia, and a higher BNP level in this context is likely underpinned by diastolic dysfunction caused by subclinical ischemia 11 in individuals with some degree of coronary stenosis. This is supported by the weak association of BNP and CACS observed herein, although the association should be interpreted carefully. The noncausal explanation is further supported by the noncausality suggested by Mendelian randomization studies (acknowledging that associations of genetically determined lifelong BNP levels with coronary disease may have limited relevance to a temporally boxed-in series of events): a genetic variant affecting the expression of the BNP gene ( NPPB , rs198389) is not associated with cardiovascular endpoints 12 or coronary artery disease 13 . The influence of chance on the finding is low, as NT-proBNP was also significantly associated with IMI in the discovery sample, with a borderline association in the validation sample (Extended Data Fig. 5 ). While BNP may hence reflect an underlying coronary artery disease, it did not add materially to a risk prediction model for IMI composed of more readily available biomarkers.

Several known mechanisms implicated in atherosclerosis and ischemia were represented among the other 94 biomarkers associated with an IMI in both the discovery and validation samples after adjusting for technical covariates, including inflammation (IL-6) 14 , extracellular matrix metabolism (WAP four-disulfide core domain protein 2 (WFDC2)) 15 , hypertrophy (adhesion G-protein-coupled receptor G1 (AGRG1)) 16 , apoptosis (triggering receptor expressed on myeloid cells 1 (TREM1), tumor necrosis factor receptor superfamily member 10B (TRAIL-R2)) and cell adhesion (AGRG1). We also observed associations with markers representing mechanisms less often implicated in coronary diseases, such as markers of kidney injury (kidney injury molecule 1 (KIM1)) 17 , appetite regulation (growth differentiation factor 15 (GDF15)) 18 , and an α-amino acid found in dietary supplements and associated with paracetamol use (pyroglutamine) 19 . While some associations may be causal, others, such as associations with levels of chitinase-3-like protein 1 (CHI3L1) 20 , pleiotrophin (PTN) or KIT, may more likely be responses to myocardial ischemia. These findings may accelerate further etiological studies of acute coronary events.

We here developed a prediction model for IMI in the general population. An imminent infarction is difficult to predict; the signals are weak, and we faced power limitations. The model achieved good discriminative ability, with acceptable calibration in the lower risk range. It is possible to transpose to other settings by entering the base hazards and variable means of those settings, for example, interactively at miscore.org . Given the increasing global burden of deaths from myocardial infarction, the importance of predicting them and increasing the individual motivation for preventing such deaths may be substantial; this can be tested in clinical trials.

The current study has several limitations. First, the use of multiple cohorts introduced heterogeneity. We addressed this at the sampling, biomarker analysis and statistical analysis stages, with the resulting limitation that the heterogeneity decreases statistical power. The strengths are the same as in other multicenter studies, including that only biomarkers with consistent importance in different settings are brought forward. Other study limitations are inherent to the uncertainty of ranking the top findings and the inability of one-by-one strategies to capture complex interrelationships. The instability of the variable importances from the random forest was unsurprising, as such methods are notoriously data hungry and require far larger datasets than classical modeling techniques 21 . While the studied markers are easily obtainable by a simple blood test or clinical assessment, a limitation is that a blood sample will not always capture tissue-specific processes. In addition, our study was limited to proteins and metabolites that remain stable in the freezer for many years. The biomarker analyses used herein are currently not available in clinical practice, and we lacked the clinically available and more precise immunoassay measurements of, for example, NT-proBNP and cardiac troponin; hence, imprecision in the proximity extension assay and ultra-high performance liquid chromatography–mass spectrometry (UPLC–MS) technologies may preclude definitive mechanistic insights and maximal clinical utility. Further, making causal assumptions is fundamentally challenging in a multimarker landscape where many causal pathways are unknown. Most markers could be potential mediators in pathways for known causes of myocardial infarction, including age and sex. Consequently, we provided models adjusted for technical covariates only and models with further biological covariate adjustment. Thus, some associations could be explained by confounding by, for example, age and sex. Notably, mediators of causal effects are also important to identify, with implications for prediction and use as treatment targets.

In conclusion, we identified biomarkers associated with the risk of an imminent first myocardial infarction, including BNP. Delineation of the distinct biological processes that operate in the months before the first myocardial infarction will be key to discovering prevention targets. We developed and validated a prediction model with a fair ability to discriminate between persons with and without risk of an imminent first myocardial infarction. Risk prediction in the short term may enhance the motivation of patients and doctors for primary prevention.

Study sample and outcome

The MIMI study sample draws biobanked blood and data from six European cohorts of the BBMRI-LPC (Biobanking and Biomolecular Research Infrastructure—Large Prospective Cohorts) collaboration 22 , as shown in Fig. 1 and Supplementary Table 1 . After sample size determination, we supplied each cohort with a standardized protocol (in which all definitions are described in detail) and an R script for selecting cohort representatives for the subcohort ( Supplementary Notes ).

Cohort participants with biobanked samples (at least 250 μl of plasma or serum; eventually, only plasma was included) and no previous clinical cardiovascular disease were eligible for inclusion in the present study. The exclusion criteria were previous clinical cardiovascular disease (defined as the presence at any time before baseline of any of the following: myocardial infarction, coronary procedure, heart failure, structural heart disease, tachyarrhythmias, stroke, thromboembolic disease and peripheral vascular disease) and renal failure.

Individuals with acute myocardial infarction (International Classification of Diseases, tenth revision (ICD-10), I21; ICD-9, 410.0–410.6 and 410.8) as the primary cause of hospitalization or death within 6 months after baseline were defined as IMI cases. We included both ST-elevation and non-ST-elevation myocardial infarctions; we encouraged efforts to include only type 1 myocardial infarctions by not counting cases with any of the following ICD codes in secondary positions: anemia (for example, ICD-10, D50–D64; ICD-9, 280–285), tachyarrhythmias (for example, ICD-10, I47–I49; ICD-9, 427), heart failure (for example, ICD-10, I50; ICD-9, 428), renal failure (for example, ICD-10, N17–N19; ICD-9, 584–586), chronic obstructive pulmonary disease (for example, ICD-10, J43–J44; ICD-9, 491, 492 and 496), sepsis and other severe infections (for example, ICD-10, A40–A41; ICD-9, 038), or hypertensive crises.

Up to four cohort representatives per available IMI case were randomly drawn from the full cohort to the subcohort in 50 strata based on sex, age (above/below median) and study center in a stratified case–cohort design 7 . All 420 IMI cases, and 1,598 subcohort representatives, were drawn from the full cohort of 169,053 participants, as summarized in Fig. 1 .

Clinical variables (age, sex, height, weight, waist circumference, systolic and diastolic blood pressure, triglycerides, high-density lipoprotein (HDL) cholesterol, non-HDL cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, glucose, diabetes status, highest education, smoking status, previous smoking exposure, alcohol intake and physical activity) were harmonized between the cohorts ( Supplementary Notes ). Non-HDL cholesterol was calculated as total cholesterol − HDL cholesterol. LDL levels were calculated using the extended Martin–Hopkins equation 23 .

All blood samples were randomized into appropriate measurement plates, stratified by cohort (with a similar number from each cohort on every plate), and aliquoted into the plates. Quality controls are summarized below and described in detail in the Supplementary Notes .

Protein measurements were done using the Olink proximity extension assay (Olink), a highly specific 92-plex immunoassay. Overall, 829 proteins across nine panels (cardiometabolic, cardiovascular II, cardiovascular III, development, immune response, inflammation, metabolism, oncology II and organ damage) were analyzed, including 804 unique proteins (considering overlap between panels). Relative protein values on a log 2 scale are reported, with each protein value normalized by plate by centering all plates at the same median, assuming random plate placement. Values below the assay’s lower limit of detection (LOD) were also included in the analyses.

Metabolites were analyzed using the UPLC–tandem MS (UPLC–MS/MS)-based Metabolon platform (Metabolon) by four different methods: reversed-phase UPLC–MS/MS with positive-mode electrospray ionization (early and late phase), reversed-phase UPLC–MS/MS with negative-mode electrospray ionization, and hydrophilic interaction LC/UPLC–MS/MS with negative-mode electrospray ionization. Overall, 1,135 metabolites were captured, including 925 with known identity and 210 with unknown identity. Relative metabolite levels were determined and normalized by analysis day. Metabolite levels were log 2 transformed, and nondetectable levels (<LOD or metabolite not present in the sample) were constant value imputed to a value below the minimum metabolite value (minimum/sqrt(2)).

Samples that did not satisfy the quality control criteria were initially excluded; exclusion filters were applied separately for the proteomics and metabolomics analyses, and only samples passing quality control for both analyses were included in the analysis set. For the proteomics analysis, samples with more than 50% of panels failing for technical reasons were excluded ( n excluded = 33). For the metabolomics analysis, samples were excluded because of low volume or detection of fewer metabolites than expected ( n excluded = 4). Consequently, samples for 420 cases and 1,598 subcohort representatives remained for analysis.

Next, biomarkers with an extremely high proportion of nondetectable or below-LOD measurements were excluded, with the same exclusion filters for proteins and metabolites. Biomarkers had to be detected in all six cohorts with at least 30 detectable values across all cohorts (~1.5% of the MIMI samples) or were otherwise excluded. Consequently, 817 proteins (some duplicates) and 1,025 metabolites were retained for analysis.

Statistical analysis

All analyses were done using R (version 4.1.1) 24 with the glmnet 25 , mice 26 , rms 27 , ranger 28 and survival 29 add-on packages.

One-by-one etiological analyses

In the discovery sample, the associations of all clinical variables (listed in Extended Data Table 1 ), proteins and metabolites with IMI were analyzed in separate weighted, stratified Cox proportional-hazards regression models adjusting for covariates, as described below. Inverse sampling probability weights (Borgan II) were applied to account for the case–cohort design in a stratified model, allowing for a different shape of the baseline hazard for each MIMI cohort (six levels) and using a robust variance estimator (Huber–White). Nonlinear relationships between continuous covariates (not including the biomarkers) and IMI were modeled using restricted cubic splines, and all factor variables were considered unordered.

Associations with an FDR (Benjamini–Hochberg) of <0.05 were taken forward to the validation sample, in which directionally consistent results with P  < 0.05 were considered replicated.

Cox proportional-hazards models adjusting for technical covariates (season, storage time and plate) were initially applied. Replicating biomarkers from the model adjusting for technical covariates were investigated in a model further adjusting for age and sex. A model allowing for an interaction between the biomarker and sex was further tested. Replicating biomarkers in the model adjusted for age and sex were then subjected to causal assumptions (Extended Data Fig. 1 ), and a bias-minimized model for each biomarker was investigated, estimating the total effects (including the effects of mediators).

Missingness and sensitivity analyses

Clinical variables with high missingness (previous smoking exposure, alcohol intake and physical activity) were not used in the analyses. Protein values below the LOD were included in the analyses; nondetectable metabolite levels were replaced with a constant value, and a missing indicator was added, as described below. The remaining missing values in the covariates were multiple imputed ( n imputations = 20) using chained equations including the outcome, clinical covariates and other variables correlated with the variable in the imputation model 30 . Regression results across imputed datasets were combined using Rubin’s rules 31 .

Interactions with sex were investigated by analyzing an interaction term for sex and each biomarker in models adjusting for technical covariates, age and sex. The interaction terms and all terms including the biomarker were tested using a multivariable chi-squared test with the same multiple-testing correction described above, requiring directionally consistent discovery and validation results.

The following secondary sensitivity analyses were included: random-effects inverse variance-weighted meta-analyses (DerSimonian–Laird) combining per-cohort results, leave-one-out analyses investigating the influence of single cohorts, complete-case analyses not imputing missing values in the clinical covariates, and analyses limiting the follow-up time to 3 months.

Simultaneous modeling and development of a prediction model

To attempt predicting this phenotype, we developed a prediction model for IMI using age, sex, anthropometric variables (height, weight and waist circumference), variables routinely collected in the laboratory (LDL cholesterol, HDL cholesterol, creatinine, glucose and triglycerides), systolic and diastolic blood pressure, smoking status (never, former or current) and education level. Regression coefficients were estimated using a weighted Cox ridge regression model, which shrinks coefficients toward zero using an L 2 penalty to accommodate overfitting. The strength of the penalty (lambda) was determined using tenfold cross-validation over a grid of 250 lambda values, repeated 100 times. The lambda selection was repeated in each imputed dataset, and the coefficients associated with the lambda giving the lowest cross-validated deviance were extracted. The final coefficient set was obtained by taking the median of the coefficients from each imputed dataset. A single-imputed dataset was used for validation and calibration. The C-index, which indicates a model’s ability to rank the risks, was determined using 100 repeats of tenfold cross-validation. A calibration curve was constructed using 100 repeats of tenfold cross-validation 32 . All modeling steps were repeated in each fold to assess the calibration accuracy objectively. The model containing only clinical variables was then reduced by approximating the linear predictor from the full model through stepwise regression. Predictions from the full model were used as the outcome in a linear model wherein variables were dropped sequentially until R 2  > 0.95. This yielded a highly parsimonious final model incorporating the main drivers of predictions. The prediction model was compared to SCORE2, a validated prediction model for the 10-year risk of cardiovascular disease developed using multiple European cohorts 33 . The 10-year survival probability and the covariate mean values used in the SCORE2 equations were replaced with the estimated 6-month survival probability and mean values from the current data to calculate the SCORE2-estimated 6-month cardiovascular disease risk 34 . Two additional external validations of the model were performed in the UK Biobank. First, all coefficients and covariate mean values in Supplementary Table 4 were used to validate the model. Second, the model was recalibrated using mean values and the estimated baseline risk from the UK Biobank cohort before validation.

To evaluate whether any biomarkers added to the clinical prediction model improve risk prediction, we used the linear predictor from the prediction model as an offset in a LASSO Cox regression model. Before model fitting, all proteins and metabolites were adjusted for technical variables. Briefly, each biomarker was used as the outcome variable in a regression model with all technical variables as covariates. The residuals from these models were used in place of the original biomarker values in the LASSO model. The LASSO model fitting was bootstrapped 250 times to investigate the stability of the variable selection.

As the biomarkers may have nonlinear associations with the outcome and interact with one another, and prior knowledge about nonlinearities and interactions among these variables is scarce, a random forest with 2,000 trees was fitted to the data as an exploratory analysis. Briefly, the random forest fits survival trees to bootstrap data samples using a random subset of the variables in each tree, handling interactions and nonlinearities naturally. A variable importance measure is associated with each variable and calculated based on the number of splits in which a variable is involved. The random forest was bootstrapped 250 times to obtain CIs for the variable importance measures.

Further analysis of relevant biomarkers

The associations of proteins detected using the Olink panels cardiovascular II and cardiovascular III with the CACS were available for testing in individuals free from cardiovascular disease (self-reported myocardial infarction, angina, coronary intervention, heart failure, atrial fibrillation, stroke and peripheral artery disease) for 1,586 participants at the Malmö or Uppsala centers of SCAPIS 35 . A higher CACS reflects a higher myocardial infarction risk. Proteins replicated in the primary MIMI analysis (BNP) were tested for an association with the CACS using an ordinal regression model adjusting for age, sex, body mass index, systolic blood pressure, creatinine, center, Olink plate, analysis date and season.

This study was approved by the Uppsala Ethics Authority (Dnr 2016/197). All Estonian Biobank participants signed a broad informed consent form. The study was carried out under ethical approval 258/M-21 from the research ethics committee of the University of Tartu and data release J08 from the Estonian Biobank. The Lifelines protocol was approved by the University Medical Center Groningen medical ethical committee under number 2007/152. The study was performed in accordance with the Declaration of Helsinki. The EpiHealth study was approved by the ethics committee of Uppsala University, and all participants provided informed written consent. The MFM was approved by the previous regional research committee in Lund, Sweden (2014/643), and all participants provided informed consent. Ethical review boards of the cohorts in EPIC-CVD approved the study protocol, and all participants provided written informed consent. Participation in the HUNT study was based on informed consent, and the Data Inspectorate and the Regional Ethics Committee for Medical Research in Norway approved the study.

Reporting summary

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

Data availability

Data supporting the findings of this study are provided in the article and related files. Raw data are not publicly available, as they contain sensitive personal information, but may be obtained from the original cohorts upon request, with varying processes, requirements and response times. For example, researchers can apply to use the Lifelines data used in this study; information on how to request access to Lifelines data and the conditions of use can be found at https://lifelines.nl/researcher/how-to-apply . Data accession codes for this study are described below.

Code availability

The prediction model equation is available at miscore.org . Code is available at https://github.com/stefan-gustafsson-work/mimi . All code used to analyze UK Biobank data is deposited at the UK Biobank repository. Questions about the analyses and code can be sent to the corresponding author.

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Acknowledgements

Transnational access to the large European prospective cohorts was provided by the BBMRI-LPC project funded by the European Commission’s Seventh Framework Programme (grant no. 313010, J.S.).

The Estonian Biobank study was supported by the European Union through the European Regional Development Fund (project no. 2014-2020.4.01.15-0012) and by institutional research funding IUT (IUT20-60) of the Estonian Ministry of Education and Research. We thank M. Alver (Estonian Biobank) for help with phenotype data.

The Trøndelag Health Study (HUNT) is a collaboration between the HUNT Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU), Trøndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public Health.

The Lifelines initiative was made possible by a subsidy from the Dutch Ministry of Health, Welfare and Sport; the Dutch Ministry of Economic Affairs; the University Medical Center Groningen (UMCG), Groningen University; and the provinces in the north of the Netherlands (Drenthe, Friesland, Groningen). Lifelines is a multidisciplinary, prospective, population-based cohort study examining the health and health-related behaviors of 167,729 persons living in the north of the Netherlands in a unique three-generation design. The study used a broad range of investigative procedures in assessing the biomedical, sociodemographic, behavioral, physical and psychological factors that contribute to the health and disease of the general population, with a particular focus on multimorbidity and complex genetics.

EPIC-CVD was supported by funding from the European Commission Framework Programme 7 (HEALTH-F2-2012-279233), European Research Council (268834), Novartis, UK Medical Research Council (G0800270, MR/L003120/1), British Heart Foundation (SP/09/002, RG/13/13/30194, RG/18/13/33946), and National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The coordination of EPIC was financially supported by the International Agency for Research on Cancer (IARC) and the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts were supported by the Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro (AIRC), Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (Netherlands); Health Research Fund (FIS)–Instituto de Salud Carlos III (ISCIII), regional governments (Andalucía, Asturias, Basque Country, Murcia and Navarra), Catalan Institute of Oncology (ICO) (Spain); Swedish Cancer Society, Swedish Research Council and county councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk, C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom). We thank all EPIC participants and staff for their contribution to the study, the laboratory teams at the Medical Research Council Epidemiology Unit for sample management and at Cambridge Genomic Services for genotyping, S. Spackman for data management, and the team at the EPIC-CVD Coordinating Centre for study coordination and administration.

SCAPIS was supported by Hjärt-Lungfonden, the Knut and Alice Wallenberg Foundation, the Swedish Research Council and VINNOVA.

This research was conducted using the UK Biobank resource under application no. 52678.

Grants were received from the European Research Council (no. 801965), Swedish Research Council, VR (2019-01471) and Hjärt-Lungfonden (20190505) (T.F.).

Grants were received from AFA Försäkring (160266), the Swedish Research Council (2016-01065), Hjärt-Lungfonden (20160734) and A. Wiklöf (J.S.).

The computations were enabled by resources in projects sens2019006 and sens2020005 provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement no. 2018-05973.

The funding sources for the different cohorts and the sponsors of the current work did not have any part in the collection, analysis and interpretation of data or in the decision to submit the paper for publication. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policies or views of the International Agency for Research on Cancer/World Health Organization.

Open access funding provided by Uppsala University.

Author information

These authors contributed equally: Stefan Gustafsson, Erik Lampa.

Authors and Affiliations

Department of Medical Sciences, Uppsala University, Uppsala, Sweden

Stefan Gustafsson, Erik Lampa, Lars Lind, Erik Ingelsson, Ulf Hammar, Tove Fall & Johan Sundström

Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden

Karin Jensevik Eriksson & Bodil Svennblad

BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Adam S. Butterworth

BHF Centre of Research Excellence, University of Cambridge, Cambridge, UK

NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK

HDR UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK

Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden

Sölve Elmståhl, Gunnar Engström & Peter M. Nilsson

K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

Kristian Hveem, Arnulf Langhammer & Bjørn Olav Åsvold

HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway

Kristian Hveem & Bjørn Olav Åsvold

Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France

Mattias Johansson

Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway

Arnulf Langhammer

Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia

Kristi Läll & Andres Metspalu

Clinical Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy

Giovanna Masala

Navarra Public Health Institute, Pamplona, Spain

Conchi Moreno-Iribas

IdiSNA, Navarra Institute for Health Research, Pamplona, Spain

Finnish Institute for Health and Welfare, Helsinki, Finland

Markus Perola & Birgit Simell

Lifelines Cohort Study, Groningen, Netherlands

Hemmo Sipsma

Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Bjørn Olav Åsvold

Science for Life Laboratory, Uppsala University, Uppsala, Sweden

Ulf Hammar & Tove Fall

Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

Andrea Ganna

Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Department of Surgical Sciences, Uppsala University, Uppsala, Sweden

Bodil Svennblad

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

Johan Sundström

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Contributions

J.S., S.G., E.L., E.I., U.H., A.G., B. Svennblad and T.F. were responsible for the study conception and design. S.G., E.L. and K.J.E. performed the statistical analyses. J.S. wrote the final draft together with S.G. and E.L. A.S.B., S.E., G.E., K.H., M.J., A.L., L.L., K.L., G.M., A.M., C.M.-I., P.M.N., M.P., B. Simell, H.S. and B.O.Å. were responsible for data acquisition for the different cohorts. All authors contributed to manuscript preparation and provided critical comments on the final manuscript. All authors had full access to all study data and had final responsibility for the decision to submit the paper for publication.

Corresponding author

Correspondence to Johan Sundström .

Ethics declarations

Competing interests.

The authors declare the following competing interests: A.S.B. reports grants outside this work (from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Novartis and Sanofi) and personal fees from Novartis. E.I. is now an employee at GlaxoSmithKline. S.G. is an employee of Sence Research AB. J.S. reports stock ownership in Anagram kommunikation AB and Symptoms Europe AB outside the submitted work. All other authors declare no competing interests.

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Extended data

Extended data fig. 1 causal assumptions for developing bias- minimized models..

Directional acyclic graph (DAG) showing the best-guess relationship between the exposure (BNP) and the outcome (IMI) and other factors influencing this relationship together with the expected direction.

Extended Data Fig. 2 Variables associated with risk of an imminent myocardial infarction, further adjusted for age and sex.

Scatter plot comparing the point estimate (log[HR]) and corresponding 95% confidence interval from models adjusting for technical covariates (x-axis) and models additionally adjusting for age and sex (y-axis) in the full MIMI study. The 95% C.I. is calculated for the biomarker only whereas the p-value in the tables is based on the 2 d.f. Wald test (two-sided) of biomarker and missing indicator (when used), hence the 95% C.I. might overlap with null for a p-value < 0.05. N=420 cases and 1598 non-cases.

Extended Data Fig. 3 Association of BNP with Risk of an Imminent Myocardial Infarction.

Kaplan–Meier graph of unadjusted cumulative hazard of an imminent myocardial infarction (IMI) by fourths (Q) of brain natriuretic peptide (BNP), weighted by sampling weights.

Extended Data Fig. 4 Leave-one-out analyses of the association of BNP with imminent myocardial infarction.

Model with BNP and technical covariates in leave-one-out analyses where one cohort was omitted at time.

Extended Data Fig. 5 Associations of BNP and NT-proBNP with imminent myocardial infarction in the individual cohorts.

Forest plot of the regression results in the model adjusting for technical covariates performed per cohort for all available BNP measurements (BNP and N-terminal pro form). Hazard ratio and corresponding 95% confidence interval is presented. NT-proBNP is measured on both the CVD2 (*) and Metabolism panel (**). Individual cohort sample sizes are given in Supplementary Table 1 .

Extended Data Fig. 6 Comparison of associations of biomarkers with risk of IMI within 3 vs 6 months.

Regression estimates from models with time to IMI within 3 vs 6 months as outcomes. MI, myocardial infarction.

Extended Data Fig. 7 Calibration of the prediction model.

a Internal calibration. Cross-validated calibration curves for predicted probabilities of an imminent myocardial infarction from the MIMI model (solid black line) and the SCORE2 model (dashed black line). b External calibration. Calibration curves for predicted probabilities of an imminent myocardial infarction from the original MIMI model (solid black line), the MIMI model recalibrated to the UKBB data (dotted black line), and the original SCORE2 model (dashed black line). The diagonal gray line in both panels is the line of ideal calibration where predicted probabilities match the observed fraction experiencing the event.

Extended Data Fig. 8

Worked example of nomogram use. A 78-year-old (73 points) smoking (13 points) low-educated (10 points) man (23 points) with diabetes (8 points), height 1.71 m (28 points), waist circumference 110 cm (22 points), LDL cholesterol 4.5 mmol/L (21 points) and HDL cholesterol 1.2 mmol/L (39 points) will have a total score of 73 + 13 + 10 + 23 + 8 + 28 + 22 + 21 + 39 = 237 points, corresponding to a 6- month risk of a first myocardial infarction of circa 1.58%. The model is also presented as an interactive web application on miscore.org.

Supplementary information

Supplementary information.

Supplementary Notes and References.

Reporting Summary

Supplementary tables.

Supplementary Tables 1–5.

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Gustafsson, S., Lampa, E., Jensevik Eriksson, K. et al. Markers of imminent myocardial infarction. Nat Cardiovasc Res 3 , 130–139 (2024). https://doi.org/10.1038/s44161-024-00422-2

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Pam Belluck is a health and science reporter, covering a range of subjects, including reproductive health, long Covid, brain science, neurological disorders, mental health and genetics. More about Pam Belluck

College of Nursing

Driving change: a case study of a dnp leader in residence program in a gerontological center of excellence.

View as pdf A later version of this article appeared in Nurse Leader , Volume 21, Issue 6 , December 2023 . 

The American Association of Colleges of Nursing (AACN) published the Essentials of Doctoral Education for Advanced Practice Nursing in 2004 identifying the essential curriculum needed for preparing advanced practice nurse leaders to effectively assess organizations, identify systemic issues, and facilitate organizational changes. 1 In 2021, AACN updated the curriculum by issuing The Essentials: Core Competencies for Professional Nursing Education to guide the development of competency-based education for nursing students. 1 In addition to AACN’s competency-based approach to curriculum, in 2015 the American Organization of Nurse Leaders (AONL) released Nurse Leader Core Competencies (updated in 2023) to help provide a competency based model to follow in developing nurse leaders. 2

Despite AACN and AONL competency-based curriculum and model, it is still common for nurse leaders to be promoted to management positions based solely on their work experience or exceptional clinical skills, rather than demonstration of management and leadership competencies. 3 The importance of identifying, training, and assessing executive leaders through formal leadership development programs, within supportive organizational cultures has been discussed by national leaders. As well as the need for nurturing emerging leaders through fostering interprofessional collaboration, mentorship, and continuous development of leadership skills has been identified. 4 As Doctor of Nursing Practice (DNP) nurse leaders assume executive roles within healthcare organizations, they play a vital role within complex systems. Demonstration of leadership competence and participation in formal leadership development programs has become imperative for their success. However, models of competency-based executive leadership development programs can be hard to find, particularly programs outside of health care systems.

The implementation of a DNP Leader in Residence program, such as the one designed for The Barbara and Richard Csomay Center for Gerontological Excellence, addresses many of the challenges facing new DNP leaders and ensures mastery of executive leadership competencies and readiness to practice through exposure to varied experiences and close mentoring. The Csomay Center , based at The University of Iowa, was established in 2000 as one of the five original Hartford Centers of Geriatric Nursing Excellence in the country. Later funding by the Csomay family established an endowment that supports the Center's ongoing work. The current Csomay Center strategic plan and mission aims to develop future healthcare leaders while promoting optimal aging and quality of life for older adults. The Csomay Center Director created the innovative DNP Leader in Residence program to foster the growth of future nurse leaders in non-healthcare systems. The purpose of this paper is to present a case study of the development and implementation of the Leader in Residence program, followed by suggested evaluation strategies, and discussion of future innovation of leadership opportunities in non-traditional health care settings.

Development of the DNP Leader in Residence Program

The Plan-Do-Study-Act (PDSA) cycle has garnered substantial recognition as a valuable tool for fostering development and driving improvement initiatives. 5 The PDSA cycle can function as an independent methodology and as an integral component of broader quality enhancement approaches with notable efficacy in its ability to facilitate the rapid creation, testing, and evaluation of transformative interventions within healthcare. 6 Consequently, the PDSA cycle model was deemed fitting to guide the development and implementation of the DNP Leader in Residence Program at the Csomay Center.

PDSA Cycle: Plan

Existing resources. The DNP Health Systems: Administration/Executive Leadership Program offered by the University of Iowa is comprised of comprehensive nursing administration and leadership curriculum, led by distinguished faculty composed of national leaders in the realms of innovation, health policy, leadership, clinical education, and evidence-based practice. The curriculum is designed to cultivate the next generation of nursing executive leaders, with emphasis on personalized career planning and tailored practicum placements. The DNP Health Systems: Administration/Executive Leadership curriculum includes a range of courses focused on leadership and management with diverse topics such as policy an law, infrastructure and informatics, finance and economics, marketing and communication, quality and safety, evidence-based practice, and social determinants of health. The curriculum is complemented by an extensive practicum component and culminates in a DNP project with additional hours of practicum.

New program. The DNP Leader in Residence program at the Csomay Center is designed to encompass communication and relationship building, systems thinking, change management, transformation and innovation, knowledge of clinical principles in the community, professionalism, and business skills including financial, strategic, and human resource management. The program fully immerses students in the objectives of the DNP Health Systems: Administration/Executive Leadership curriculum and enables them to progressively demonstrate competencies outlined by AONL. The Leader in Residence program also includes career development coaching, reflective practice, and personal and professional accountability. The program is integrated throughout the entire duration of the Leader in Residence’s coursework, fulfilling the required practicum hours for both the DNP coursework and DNP project.

The DNP Leader in Residence program begins with the first semester of practicum being focused on completing an onboarding process to the Center including understanding the center's strategic plan, mission, vision, and history. Onboarding for the Leader in Residence provides access to all relevant Center information and resources and integration into the leadership team, community partnerships, and other University of Iowa College of Nursing Centers associated with the Csomay Center. During this first semester, observation and identification of the Csomay Center Director's various roles including being a leader, manager, innovator, socializer, and mentor is facilitated. In collaboration with the Center Director (a faculty position) and Center Coordinator (a staff position), specific competencies to be measured and mastered along with learning opportunities desired throughout the program are established to ensure a well-planned and thorough immersion experience.

Following the initial semester of practicum, the Leader in Residence has weekly check-ins with the Center Director and Center Coordinator to continue to identify learning opportunities and progression through executive leadership competencies to enrich the experience. The Leader in Residence also undertakes an administrative project for the Center this semester, while concurrently continuing observations of the Center Director's activities in local, regional, and national executive leadership settings. The student has ongoing participation and advancement in executive leadership roles and activities throughout the practicum, creating a well-prepared future nurse executive leader.

After completing practicum hours related to the Health Systems: Administration/Executive Leadership coursework, the Leader in Residence engages in dedicated residency hours to continue to experience domains within nursing leadership competencies like communication, professionalism, and relationship building. During residency hours, time is spent with the completion of a small quality improvement project for the Csomay Center, along with any other administrative projects identified by the Center Director and Center Coordinator. The Leader in Residence is fully integrated into the Csomay Center's Leadership Team during this phase, assisting the Center Coordinator in creating agendas and leading meetings. Additional participation includes active involvement in community engagement activities and presenting at or attending a national conference as a representative of the Csomay Center. The Leader in Residence must mentor a master’s in nursing student during the final year of the DNP Residency.

Implementation of the DNP Leader in Residence Program

PDSA Cycle: Do

Immersive experience. In this case study, the DNP Leader in Residence was fully immersed in a wide range of center activities, providing valuable opportunities to engage in administrative projects and observe executive leadership roles and skills during practicum hours spent at the Csomay Center. Throughout the program, the Leader in Residence observed and learned from multidisciplinary leaders at the national, regional, and university levels who engaged with the Center. By shadowing the Csomay Center Director, the Leader in Residence had the opportunity to observe executive leadership objectives such as fostering innovation, facilitating multidisciplinary collaboration, and nurturing meaningful relationships. The immersive experience within the center’s activities also allowed the Leader in Residence to gain a deep understanding of crucial facets such as philanthropy and community engagement. Active involvement in administrative processes such as strategic planning, budgeting, human resources management, and the development of standard operating procedures provided valuable exposure to strategies that are needed to be an effective nurse leader in the future.

Active participation. The DNP Leader in Residence also played a key role in advancing specific actions outlined in the center's strategic plan during the program including: 1) the creation of a membership structure for the Csomay Center and 2) successfully completing a state Board of Regents application for official recognition as a distinguished center. The Csomay Center sponsored membership for the Leader in Residence in the Midwest Nurse Research Society (MNRS), which opened doors to attend the annual MNRS conference and engage with regional nursing leadership, while fostering socialization, promotion of the Csomay Center and Leader in Residence program, and observation of current nursing research. Furthermore, the Leader in Residence participated in the strategic planning committee and engagement subcommittee for MNRS, collaborating directly with the MNRS president. Additional active participation by the Leader in Residence included attendance in planning sessions and completion of the annual report for GeriatricPain.org , an initiative falling under the umbrella of the Csomay Center. Finally, the Leader in Residence was involved in archiving research and curriculum for distinguished nursing leader and researcher, Dr. Kitty Buckwalter, for the Benjamin Rose Institute on Aging, the University of Pennsylvania Barbara Bates Center for the Study of the History of Nursing, and the University of Iowa library archives.

Suggested Evaluation Strategies of the DNP Leader in Residence Program

PDSA Cycle: Study

Assessment and benchmarking. To effectively assess the outcomes and success of the DNP Leader in Residence Program, a comprehensive evaluation framework should be used throughout the program. Key measures should include the collection and review of executive leadership opportunities experienced, leadership roles observed, and competencies mastered. The Leader in Residence is responsible for maintaining detailed logs of their participation in center activities and initiatives on a semester basis. These logs serve to track the progression of mastery of AONL competencies by benchmarking activities and identifying areas for future growth for the Leader in Residence.

Evaluation. In addition to assessment and benchmarking, evaluations need to be completed by Csomay Center stakeholders (leadership, staff, and community partners involved) and the individual Leader in Residence both during and upon completion of the program. Feedback from stakeholders will identify the contributions made by the Leader in Residence and provide valuable insights into their growth. Self-reflection on experiences by the individual Leader in Residence throughout the program will serve as an important measure of personal successes and identify gaps in the program. Factors such as career advancement during the program, application of curriculum objectives in the workplace, and prospects for future career progression for the Leader in Residence should be considered as additional indicators of the success of the program.

The evaluation should also encompass a thorough review of the opportunities experienced during the residency, with the aim of identifying areas for potential expansion and enrichment of the DNP Leader in Residence program. By carefully examining the logs, reflecting on the acquired executive leadership competencies, and studying stakeholder evaluations, additional experiences and opportunities can be identified to further enhance the program's efficacy. The evaluation process should be utilized to identify specific executive leadership competencies that require further immersion and exploration throughout the program.

Future Innovation of DNP Leader in Residence Programs in Non-traditional Healthcare Settings

PDSA Cycle: Act

As subsequent residents complete the program and their experiences are thoroughly evaluated, it is essential to identify new opportunities for DNP Leader in Residence programs to be implemented in other non-health care system settings. When feasible, expansion into clinical healthcare settings, including long-term care and acute care environments, should be pursued. By leveraging the insights gained from previous Leaders in Residence and their respective experiences, the program can be refined to better align with desired outcomes and competencies. These expansions will broaden the scope and impact of the program and provide a wider array of experiences and challenges for future Leaders in Residency to navigate, enriching their development as dynamic nurse executive leaders within diverse healthcare landscapes.

This case study presented a comprehensive overview of the development and implementation of the DNP Leader in Residence program developed by the Barbara and Richard Csomay Center for Gerontological Excellence. The Leader in Residence program provided a transformative experience by integrating key curriculum objectives, competency-based learning, and mentorship by esteemed nursing leaders and researchers through successful integration into the Center. With ongoing innovation and application of the PDSA cycle, the DNP Leader in Residence program presented in this case study holds immense potential to help better prepare 21 st century nurse leaders capable of driving positive change within complex healthcare systems.

Acknowledgements

         The author would like to express gratitude to the Barbara and Richard Csomay Center for Gerontological Excellence for the fostering environment to provide an immersion experience and the ongoing support for development of the DNP Leader in Residence program. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

  • American Association of Colleges of Nursing. The essentials: core competencies for professional nursing education. https://www.aacnnursing.org/Portals/42/AcademicNursing/pdf/Essentials-2021.pdf . Accessed June 26, 2023.
  • American Organization for Nursing Leadership. Nurse leader core competencies. https://www.aonl.org/resources/nurse-leader-competencies . Accessed July 10, 2023.
  • Warshawsky, N, Cramer, E. Describing nurse manager role preparation and competency: findings from a national study. J Nurs Adm . 2019;49(5):249-255. DOI:  10.1097/NNA.0000000000000746
  • Van Diggel, C, Burgess, A, Roberts, C, Mellis, C. Leadership in healthcare education. BMC Med. Educ . 2020;20(465). doi: 10.1186/s12909-020-02288-x
  • Institute for Healthcare Improvement. Plan-do-study-act (PDSA) worksheet. https://www.ihi.org/resources/Pages/Tools/PlanDoStudyActWorksheet.aspx . Accessed July 4, 2023.
  • Taylor, M, McNicolas, C, Nicolay, C, Darzi, A, Bell, D, Reed, J. Systemic review of the application of the plan-do-study-act method to improve quality in healthcare. BMJ Quality & Safety. 2014:23:290-298. doi: 10.1136/bmjqs-2013-002703

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COMMENTS

  1. Case Study

    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.

  2. What Is a Case Study?

    Step 1: Select a case 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

  3. Case Study Methodology of Qualitative Research: Key Attributes and

    In a case study research, multiple methods of data collection are used, as it involves an in-depth study of a phenomenon. It must be noted, as highlighted by Yin ( 2009 ), a case study is not a method of data collection, rather is a research strategy or design to study a social unit.

  4. Case Study Methods and Examples

    The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case. It is unique given one characteristic: case studies draw from more than one data source.

  5. The case study approach

    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 ].

  6. Case Study Method: A Step-by-Step Guide for Business Researchers

    Case study method is the most widely used method in academia for researchers interested in qualitative research ( Baskarada, 2014 ). Research students select the case study as a method without understanding array of factors that can affect the outcome of their research.

  7. Methodology or method? A critical review of qualitative case study

    Findings were grouped into five themes outlining key methodological issues: case study methodology or method, case of something particular and case selection, contextually bound case study, researcher and case interactions and triangulation, and study design inconsistent with methodology reported.

  8. Writing a Case Study

    A case study is a research method that involves an in-depth analysis of a real-life phenomenon or situation. Learn how to write a case study for your social sciences research assignments with this helpful guide from USC Library. Find out how to define the case, select the data sources, analyze the evidence, and report the results.

  9. Case Study

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

  10. Case Study Research: Methods and Designs

    Main Approaches To Data Collection A fundamental requirement of qualitative research is recording observations that provide an understanding of reality. When it comes to the case study method, there are two major approaches that can be used to collect data: document review and fieldwork.

  11. Case Study: Definition, Examples, Types, and How to Write

    A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

  12. Case Study

    The definitions of case study evolved over a period of time. Case study is defined as "a systematic inquiry into an event or a set of related events which aims to describe and explain the phenomenon of interest" (Bromley, 1990).Stoecker defined a case study as an "intensive research in which interpretations are given based on observable concrete interconnections between actual properties ...

  13. (PDF) Qualitative Case Study Methodology: Study Design and

    Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage. 559 The Qualitative Report December 2008. Author Note . Dr. Pamela Baxter is an assistant prof essor at McMaster ...

  14. Distinguishing case study as a research method from case reports as a

    Another type of study categorized as a case report is an "N of 1" study or single-subject clinical trial, which considers an individual patient as the sole unit of observation in a study investigating the efficacy or side effect profiles of different interventions.

  15. Sage Research Methods

    Peter Foster Publisher: SAGE Publications Ltd Publication year: 2009 Online pub date: January 01, 2011 Discipline: Anthropology Methods: Case study research, Generalizability, Induction DOI: https:// doi. org/10.4135/9780857024367 Keywords: inquiry, knowledge, law, population, social science, sociology, tacit knowledge More information Summary

  16. (PDF) Case Study Research

    The case study method is a research strategy that aims to gain an in-depth understanding of a specific phenomenon by collecting and analyzing specific data within its true context (Rebolj, 2013 ...

  17. The Case Study as a Research Method

    December 1942, issues of the REVIEW OF EDUCATIONAL use of the case study in research methodology, progress has. this field. First, the case study has been of increased value. of research in education, psychology, sociology, and anthropology; progress has been made in the technics of gathering and study data for research purposes; and third ...

  18. Case Study

    Step 1: Select a case 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

  19. Case Study Research Method in Psychology

    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.

  20. (PDF) The case study as a type of qualitative research

    In comparison to other types of qualitative research, case studies have been little understood both from a methodological point of view, where disagreements exist about whether case...

  21. Toward Developing a Framework for Conducting Case Study Research

    Nevertheless, the case study researchers mentioned above emphasize different features. Stake points out that crucial to case study research are not the methods of investigation, but that the object of study is a case: "As a form of research, the case study is defined by the interest in individual cases, not by the methods of inquiry used."

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    Overall study description. We designed a sequential exploratory mixed methods study with three linked components: 1. Case studies: which identified existing examples of trial method research projects with actionable outputs that were believed to influence trial design, conduct, analysis, or reporting practice."Actionable outputs" were defined broadly as any resource, generated from these ...

  23. The case study approach

    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 ...

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    The use of TA has an additional methodological benefit since dendrograms can provide the base for constructing a quantitative research instrument in a succeeding study. In that sense we embrace the idea that qualitative methods can fruitfully be brought into dialogue with quantitative methods when designing evaluation research.

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    Case study as a research method June 2007 Authors: Zaidah Zainal Universiti Teknologi Malaysia Abstract Although case study methods remain a controversial approach to data collection, they...

  28. Driving change: a case study of a DNP leader in residence program in a

    The purpose of this paper is to present a case study of the development and implementation of the Leader in Residence program, followed by suggested evaluation strategies, and discussion of future innovation of leadership opportunities in non-traditional health care settings.Development of the DNP Leader in Residence ProgramThe Plan-Do-Study ...

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    Research methods for text recognition in low-resource scenarios are relatively limited. Existing studies primarily focus on languages with abundant resources, and most state-of-the-art methods are based on the Transformer architecture, showing poor adaptability to low-resource datasets. ... The Uyghur Language Case Study" Applied Sciences 14 ...