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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.

Prevent plagiarism. Run a free check.

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 studies as a research methodology

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.

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McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved March 4, 2024, from https://www.scribbr.com/methodology/case-study/

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Case Study – Methods, Examples and Guide

Table of Contents

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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  • Knowledge Base
  • Methodology
  • 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.

Prevent plagiarism, run a free check.

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.

Cite this Scribbr article

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McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 4 March 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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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 studies as a research methodology

Cara Lustik is a fact-checker and copywriter.

case studies as a research methodology

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

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

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

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.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

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

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

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

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

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

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

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

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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Writing a Case Study

Hands holding a world globe

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?

Man and woman looking at a laptop

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?

Boys looking through a camera

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.

Triangulation image with examples

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.

Man holding his hand out to show five fingers.

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Continuing to enhance the quality of case study methodology in health services research

Shannon l. sibbald.

1 Faculty of Health Sciences, Western University, London, Ontario, Canada.

2 Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

3 The Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

Stefan Paciocco

Meghan fournie, rachelle van asseldonk, tiffany scurr.

Case study methodology has grown in popularity within Health Services Research (HSR). However, its use and merit as a methodology are frequently criticized due to its flexible approach and inconsistent application. Nevertheless, case study methodology is well suited to HSR because it can track and examine complex relationships, contexts, and systems as they evolve. Applied appropriately, it can help generate information on how multiple forms of knowledge come together to inform decision-making within healthcare contexts. In this article, we aim to demystify case study methodology by outlining its philosophical underpinnings and three foundational approaches. We provide literature-based guidance to decision-makers, policy-makers, and health leaders on how to engage in and critically appraise case study design. We advocate that researchers work in collaboration with health leaders to detail their research process with an aim of strengthening the validity and integrity of case study for its continued and advanced use in HSR.

Introduction

The popularity of case study research methodology in Health Services Research (HSR) has grown over the past 40 years. 1 This may be attributed to a shift towards the use of implementation research and a newfound appreciation of contextual factors affecting the uptake of evidence-based interventions within diverse settings. 2 Incorporating context-specific information on the delivery and implementation of programs can increase the likelihood of success. 3 , 4 Case study methodology is particularly well suited for implementation research in health services because it can provide insight into the nuances of diverse contexts. 5 , 6 In 1999, Yin 7 published a paper on how to enhance the quality of case study in HSR, which was foundational for the emergence of case study in this field. Yin 7 maintains case study is an appropriate methodology in HSR because health systems are constantly evolving, and the multiple affiliations and diverse motivations are difficult to track and understand with traditional linear methodologies.

Despite its increased popularity, there is debate whether a case study is a methodology (ie, a principle or process that guides research) or a method (ie, a tool to answer research questions). Some criticize case study for its high level of flexibility, perceiving it as less rigorous, and maintain that it generates inadequate results. 8 Others have noted issues with quality and consistency in how case studies are conducted and reported. 9 Reporting is often varied and inconsistent, using a mix of approaches such as case reports, case findings, and/or case study. Authors sometimes use incongruent methods of data collection and analysis or use the case study as a default when other methodologies do not fit. 9 , 10 Despite these criticisms, case study methodology is becoming more common as a viable approach for HSR. 11 An abundance of articles and textbooks are available to guide researchers through case study research, including field-specific resources for business, 12 , 13 nursing, 14 and family medicine. 15 However, there remains confusion and a lack of clarity on the key tenets of case study methodology.

Several common philosophical underpinnings have contributed to the development of case study research 1 which has led to different approaches to planning, data collection, and analysis. This presents challenges in assessing quality and rigour for researchers conducting case studies and stakeholders reading results.

This article discusses the various approaches and philosophical underpinnings to case study methodology. Our goal is to explain it in a way that provides guidance for decision-makers, policy-makers, and health leaders on how to understand, critically appraise, and engage in case study research and design, as such guidance is largely absent in the literature. This article is by no means exhaustive or authoritative. Instead, we aim to provide guidance and encourage dialogue around case study methodology, facilitating critical thinking around the variety of approaches and ways quality and rigour can be bolstered for its use within HSR.

Purpose of case study methodology

Case study methodology is often used to develop an in-depth, holistic understanding of a specific phenomenon within a specified context. 11 It focuses on studying one or multiple cases over time and uses an in-depth analysis of multiple information sources. 16 , 17 It is ideal for situations including, but not limited to, exploring under-researched and real-life phenomena, 18 especially when the contexts are complex and the researcher has little control over the phenomena. 19 , 20 Case studies can be useful when researchers want to understand how interventions are implemented in different contexts, and how context shapes the phenomenon of interest.

In addition to demonstrating coherency with the type of questions case study is suited to answer, there are four key tenets to case study methodologies: (1) be transparent in the paradigmatic and theoretical perspectives influencing study design; (2) clearly define the case and phenomenon of interest; (3) clearly define and justify the type of case study design; and (4) use multiple data collection sources and analysis methods to present the findings in ways that are consistent with the methodology and the study’s paradigmatic base. 9 , 16 The goal is to appropriately match the methods to empirical questions and issues and not to universally advocate any single approach for all problems. 21

Approaches to case study methodology

Three authors propose distinct foundational approaches to case study methodology positioned within different paradigms: Yin, 19 , 22 Stake, 5 , 23 and Merriam 24 , 25 ( Table 1 ). Yin is strongly post-positivist whereas Stake and Merriam are grounded in a constructivist paradigm. Researchers should locate their research within a paradigm that explains the philosophies guiding their research 26 and adhere to the underlying paradigmatic assumptions and key tenets of the appropriate author’s methodology. This will enhance the consistency and coherency of the methods and findings. However, researchers often do not report their paradigmatic position, nor do they adhere to one approach. 9 Although deliberately blending methodologies may be defensible and methodologically appropriate, more often it is done in an ad hoc and haphazard way, without consideration for limitations.

Cross-analysis of three case study approaches, adapted from Yazan 2015

The post-positive paradigm postulates there is one reality that can be objectively described and understood by “bracketing” oneself from the research to remove prejudice or bias. 27 Yin focuses on general explanation and prediction, emphasizing the formulation of propositions, akin to hypothesis testing. This approach is best suited for structured and objective data collection 9 , 11 and is often used for mixed-method studies.

Constructivism assumes that the phenomenon of interest is constructed and influenced by local contexts, including the interaction between researchers, individuals, and their environment. 27 It acknowledges multiple interpretations of reality 24 constructed within the context by the researcher and participants which are unlikely to be replicated, should either change. 5 , 20 Stake and Merriam’s constructivist approaches emphasize a story-like rendering of a problem and an iterative process of constructing the case study. 7 This stance values researcher reflexivity and transparency, 28 acknowledging how researchers’ experiences and disciplinary lenses influence their assumptions and beliefs about the nature of the phenomenon and development of the findings.

Defining a case

A key tenet of case study methodology often underemphasized in literature is the importance of defining the case and phenomenon. Researches should clearly describe the case with sufficient detail to allow readers to fully understand the setting and context and determine applicability. Trying to answer a question that is too broad often leads to an unclear definition of the case and phenomenon. 20 Cases should therefore be bound by time and place to ensure rigor and feasibility. 6

Yin 22 defines a case as “a contemporary phenomenon within its real-life context,” (p13) which may contain a single unit of analysis, including individuals, programs, corporations, or clinics 29 (holistic), or be broken into sub-units of analysis, such as projects, meetings, roles, or locations within the case (embedded). 30 Merriam 24 and Stake 5 similarly define a case as a single unit studied within a bounded system. Stake 5 , 23 suggests bounding cases by contexts and experiences where the phenomenon of interest can be a program, process, or experience. However, the line between the case and phenomenon can become muddy. For guidance, Stake 5 , 23 describes the case as the noun or entity and the phenomenon of interest as the verb, functioning, or activity of the case.

Designing the case study approach

Yin’s approach to a case study is rooted in a formal proposition or theory which guides the case and is used to test the outcome. 1 Stake 5 advocates for a flexible design and explicitly states that data collection and analysis may commence at any point. Merriam’s 24 approach blends both Yin and Stake’s, allowing the necessary flexibility in data collection and analysis to meet the needs.

Yin 30 proposed three types of case study approaches—descriptive, explanatory, and exploratory. Each can be designed around single or multiple cases, creating six basic case study methodologies. Descriptive studies provide a rich description of the phenomenon within its context, which can be helpful in developing theories. To test a theory or determine cause and effect relationships, researchers can use an explanatory design. An exploratory model is typically used in the pilot-test phase to develop propositions (eg, Sibbald et al. 31 used this approach to explore interprofessional network complexity). Despite having distinct characteristics, the boundaries between case study types are flexible with significant overlap. 30 Each has five key components: (1) research question; (2) proposition; (3) unit of analysis; (4) logical linking that connects the theory with proposition; and (5) criteria for analyzing findings.

Contrary to Yin, Stake 5 believes the research process cannot be planned in its entirety because research evolves as it is performed. Consequently, researchers can adjust the design of their methods even after data collection has begun. Stake 5 classifies case studies into three categories: intrinsic, instrumental, and collective/multiple. Intrinsic case studies focus on gaining a better understanding of the case. These are often undertaken when the researcher has an interest in a specific case. Instrumental case study is used when the case itself is not of the utmost importance, and the issue or phenomenon (ie, the research question) being explored becomes the focus instead (eg, Paciocco 32 used an instrumental case study to evaluate the implementation of a chronic disease management program). 5 Collective designs are rooted in an instrumental case study and include multiple cases to gain an in-depth understanding of the complexity and particularity of a phenomenon across diverse contexts. 5 , 23 In collective designs, studying similarities and differences between the cases allows the phenomenon to be understood more intimately (for examples of this in the field, see van Zelm et al. 33 and Burrows et al. 34 In addition, Sibbald et al. 35 present an example where a cross-case analysis method is used to compare instrumental cases).

Merriam’s approach is flexible (similar to Stake) as well as stepwise and linear (similar to Yin). She advocates for conducting a literature review before designing the study to better understand the theoretical underpinnings. 24 , 25 Unlike Stake or Yin, Merriam proposes a step-by-step guide for researchers to design a case study. These steps include performing a literature review, creating a theoretical framework, identifying the problem, creating and refining the research question(s), and selecting a study sample that fits the question(s). 24 , 25 , 36

Data collection and analysis

Using multiple data collection methods is a key characteristic of all case study methodology; it enhances the credibility of the findings by allowing different facets and views of the phenomenon to be explored. 23 Common methods include interviews, focus groups, observation, and document analysis. 5 , 37 By seeking patterns within and across data sources, a thick description of the case can be generated to support a greater understanding and interpretation of the whole phenomenon. 5 , 17 , 20 , 23 This technique is called triangulation and is used to explore cases with greater accuracy. 5 Although Stake 5 maintains case study is most often used in qualitative research, Yin 17 supports a mix of both quantitative and qualitative methods to triangulate data. This deliberate convergence of data sources (or mixed methods) allows researchers to find greater depth in their analysis and develop converging lines of inquiry. For example, case studies evaluating interventions commonly use qualitative interviews to describe the implementation process, barriers, and facilitators paired with a quantitative survey of comparative outcomes and effectiveness. 33 , 38 , 39

Yin 30 describes analysis as dependent on the chosen approach, whether it be (1) deductive and rely on theoretical propositions; (2) inductive and analyze data from the “ground up”; (3) organized to create a case description; or (4) used to examine plausible rival explanations. According to Yin’s 40 approach to descriptive case studies, carefully considering theory development is an important part of study design. “Theory” refers to field-relevant propositions, commonly agreed upon assumptions, or fully developed theories. 40 Stake 5 advocates for using the researcher’s intuition and impression to guide analysis through a categorical aggregation and direct interpretation. Merriam 24 uses six different methods to guide the “process of making meaning” (p178) : (1) ethnographic analysis; (2) narrative analysis; (3) phenomenological analysis; (4) constant comparative method; (5) content analysis; and (6) analytic induction.

Drawing upon a theoretical or conceptual framework to inform analysis improves the quality of case study and avoids the risk of description without meaning. 18 Using Stake’s 5 approach, researchers rely on protocols and previous knowledge to help make sense of new ideas; theory can guide the research and assist researchers in understanding how new information fits into existing knowledge.

Practical applications of case study research

Columbia University has recently demonstrated how case studies can help train future health leaders. 41 Case studies encompass components of systems thinking—considering connections and interactions between components of a system, alongside the implications and consequences of those relationships—to equip health leaders with tools to tackle global health issues. 41 Greenwood 42 evaluated Indigenous peoples’ relationship with the healthcare system in British Columbia and used a case study to challenge and educate health leaders across the country to enhance culturally sensitive health service environments.

An important but often omitted step in case study research is an assessment of quality and rigour. We recommend using a framework or set of criteria to assess the rigour of the qualitative research. Suitable resources include Caelli et al., 43 Houghten et al., 44 Ravenek and Rudman, 45 and Tracy. 46

New directions in case study

Although “pragmatic” case studies (ie, utilizing practical and applicable methods) have existed within psychotherapy for some time, 47 , 48 only recently has the applicability of pragmatism as an underlying paradigmatic perspective been considered in HSR. 49 This is marked by uptake of pragmatism in Randomized Control Trials, recognizing that “gold standard” testing conditions do not reflect the reality of clinical settings 50 , 51 nor do a handful of epistemologically guided methodologies suit every research inquiry.

Pragmatism positions the research question as the basis for methodological choices, rather than a theory or epistemology, allowing researchers to pursue the most practical approach to understanding a problem or discovering an actionable solution. 52 Mixed methods are commonly used to create a deeper understanding of the case through converging qualitative and quantitative data. 52 Pragmatic case study is suited to HSR because its flexibility throughout the research process accommodates complexity, ever-changing systems, and disruptions to research plans. 49 , 50 Much like case study, pragmatism has been criticized for its flexibility and use when other approaches are seemingly ill-fit. 53 , 54 Similarly, authors argue that this results from a lack of investigation and proper application rather than a reflection of validity, legitimizing the need for more exploration and conversation among researchers and practitioners. 55

Although occasionally misunderstood as a less rigourous research methodology, 8 case study research is highly flexible and allows for contextual nuances. 5 , 6 Its use is valuable when the researcher desires a thorough understanding of a phenomenon or case bound by context. 11 If needed, multiple similar cases can be studied simultaneously, or one case within another. 16 , 17 There are currently three main approaches to case study, 5 , 17 , 24 each with their own definitions of a case, ontological and epistemological paradigms, methodologies, and data collection and analysis procedures. 37

Individuals’ experiences within health systems are influenced heavily by contextual factors, participant experience, and intricate relationships between different organizations and actors. 55 Case study research is well suited for HSR because it can track and examine these complex relationships and systems as they evolve over time. 6 , 7 It is important that researchers and health leaders using this methodology understand its key tenets and how to conduct a proper case study. Although there are many examples of case study in action, they are often under-reported and, when reported, not rigorously conducted. 9 Thus, decision-makers and health leaders should use these examples with caution. The proper reporting of case studies is necessary to bolster their credibility in HSR literature and provide readers sufficient information to critically assess the methodology. We also call on health leaders who frequently use case studies 56 – 58 to report them in the primary research literature.

The purpose of this article is to advocate for the continued and advanced use of case study in HSR and to provide literature-based guidance for decision-makers, policy-makers, and health leaders on how to engage in, read, and interpret findings from case study research. As health systems progress and evolve, the application of case study research will continue to increase as researchers and health leaders aim to capture the inherent complexities, nuances, and contextual factors. 7

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Research Methodologies Guide

  • Action Research
  • Bibliometrics

Case Studies

  • Content Analysis
  • Digital Scholarship This link opens in a new window
  • Documentary
  • Ethnography
  • Focus Groups
  • Grounded Theory
  • Life Histories/Autobiographies
  • Longitudinal
  • Participant Observation
  • Qualitative Research (General)
  • Quasi-Experimental Design
  • Usability Studies
"A case study is an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident" (Yin, 1994).

It refers to a type of research in which a case (an event, issue, population, or other item being studied) is analyzed, often through the use of multiple methods of analysis.

Tools commonly used in case studies include:

  • Observations

For more information about case studies, review the resources below:

Books and articles

  • Five Misunderstandings About Case Study Research [pdf] An article reflecting on common issues in case study research.
  • Case Study Research and Applications by Robert K. Yin Publication Date: 2017
  • Qualitative Research Through Case Studies by Max Travers Publication Date: 2001
  • Unravelling the Mysteries of Case Study Research by Marilyn L. Taylor; Mikael Søndergaard Publication Date: 2017

Additional Resources

  • Case Studies A tutorial on case study research from Colorado State University.
  • Case Study - Wikipedia, the free encyclopedia. Wikipedia can be a useful place to start your research- check the citations at the bottom of the article for more information.
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  • Last Updated: Dec 19, 2023 2:12 PM
  • URL: https://instr.iastate.libguides.com/researchmethods

Verywell Mind

Descriptive Research in Psychology

Sometimes you need to dig deeper than the pure statistics

Descriptive research is one of the key tools needed in any psychology researcher’s toolbox in order to create and lead a project that is both equitable and effective. Because psychology, as a field, loves definitions, let’s start with one. The University of Minnesota’s Introduction to Psychology defines this type of research as one that is “...designed to provide a snapshot of the current state of affairs.”

That's pretty broad, so what does that mean in practice? Dr. Heather Derry-Vick (PhD) , an assistant professor in psychiatry at Hackensack Meridian School of Medicine, helps us put it into perspective.

"Descriptive research really focuses on defining, understanding, and measuring a phenomenon or an experience," she says. "Not trying to change a person's experience or outcome, or even really looking at the mechanisms for why that might be happening, but more so describing an experience or a process as it unfolds naturally.”

Types of Descriptive Research and the Methods Used

Within the descriptive research methodology there are multiple types, including the following.

Descriptive Survey Research

This involves going beyond a typical tool like a LIkert Scale —where you typically place your response to a prompt on a one to five scale. We already know that scales like this can be ineffective, particularly when studying pain, for example.

When that's the case, using a descriptive methodology can help dig deeper into how a person is thinking, feeling, and acting rather than simply quantifying it in a way that might be unclear or confusing.

Descriptive Observational Research

Think of observational research like an ethically-focused version of people-watching. One example would be watching the patterns of children on a playground—perhaps when looking at a concept like risky play or seeking to observe social behaviors between children of different ages.

Descriptive Case Study Research

A descriptive approach to a case study is akin to a biography of a person, honing in on the experiences of a small group to extrapolate to larger themes. We most commonly see descriptive case studies when those in the psychology field are using past clients as an example to illustrate a point.

Correlational Descriptive Research

While descriptive research is often about the here and now, this form of the methodology allows researchers to make connections between groups of people. As an example from her research, Derry-Vick says she uses this method to identify how gender might play a role in cancer scan anxiety, aka scanxiety.

Dr. Derry-Vick's research uses surveys and interviews to get a sense of how cancer patients are feeling and what they are experiencing both in the course of their treatment and in the lead-up to their next scan, which can be a significant source of stress.

David Marlon, PsyD, MBA , who works as a clinician and as CEO at Vegas Stronger, and whose research focused on leadership styles at community-based clinics, says that using descriptive research allowed him to get beyond the numbers.

In his case, that includes data points like how many unhoused people found stable housing over a certain period or how many people became drug-free—and identify the reasons for those changes.

For the portion of his thesis that was focused on descriptive research, Marlon used semi-structured interviews to look at the how and the why of transformational leadership and its impact on clinics’ clients and staff.

Advantages & Limitations of Descriptive Research

So, if the advantages of using descriptive research include that it centers the research participants, gives us a clear picture of what is happening to a person in a particular moment,  and gives us very nuanced insights into how a particular situation is being perceived by the very person affected, are there drawbacks?

Yes, there are. Dr. Derry-Vick says that it’s important to keep in mind that just because descriptive research tells us something is happening doesn’t mean it necessarily leads us to the resolution of a given problem.

Another limitation she identifies is that it also can’t tell you, on its own, whether a particular treatment pathway is having the desired effect.

“Descriptive research in and of itself can't really tell you whether a specific approach is going to be helpful until you take in a different approach to actually test it.”

Marlon, who believes in a multi-disciplinary approach, says that his subfield—addictions—is one where descriptive research had its limits, but helps readers go beyond preconceived notions of what addictions treatment looks and feels like when it is effective.

“If we talked to and interviewed and got descriptive information from the clinicians and the clients, a much more precise picture would be painted, showing the need for a client's specific multidisciplinary approach augmented with a variety of modalities," he says. "If you tried to look at my discipline in a pure quantitative approach , it wouldn't begin to tell the real story.”

Best Practices for Conducting Descriptive Research

Because you’re controlling far fewer variables than other forms of research, it’s important to identify whether those you are describing, your study participants, should be informed that they are part of a study.

For example, if you’re observing and describing who is buying what in a grocery store to identify patterns, then you might not need to identify yourself.

However, if you’re asking people about their fear of certain treatment, or how their marginalized identities impact their mental health in a particular way, there is far more of a pressure to think deeply about how you, as the researcher, are connected to the people you are researching.

Many descriptive research projects use interviews as a form of research gathering and, as a result, descriptive research that is focused on this type of data gathering also has ethical and practical concerns attached. Thankfully, there are plenty of guides from established researchers about how to best conduct these interviews and/or formulate surveys .

While descriptive research has its limits, it is commonly used by researchers to get a clear vantage point on what is happening in a given situation.

Tools like surveys, interviews, and observation are often employed to dive deeper into a given issue and really highlight the human element in psychological research. At its core, descriptive research is rooted in a collaborative style that allows deeper insights when used effectively.

Read the original article on Verywell Mind .

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StatAnalytica

Top 100 Research Methodology Project Topics

research methodology project topics

Research methodology might sound like a fancy term, but it’s simply the way researchers go about investigating a question or problem. Think of it as a roadmap for your project, guiding you through the steps to find answers. It’s crucial to pick the right methodology because it determines how you collect and analyze data, which affects the reliability of your findings. So, let’s check 100 research methodology project topics below.

Types of Research Methodologies

Table of Contents

There are mainly three types of research methodologies: quantitative, qualitative, and mixed-methods.

Quantitative Research Methodology

Quantitative research focuses on collecting numerical data and analyzing it statistically. It’s great for measuring things objectively.

For instance, if you’re studying how many people prefer coffee over tea, quantitative research can provide concrete numbers.

Qualitative Research Methodology

Qualitative research, on the other hand, dives deep into understanding people’s experiences, feelings, and behaviors. It’s like peeling an onion layer by layer to reveal the underlying emotions and motivations.

For example, if you want to explore why some students struggle with math, qualitative research can uncover personal stories and perspectives.

Mixed-Methods Research

Sometimes, researchers use a combination of quantitative and qualitative methods, known as mixed-methods research.

This approach offers a more comprehensive understanding of a topic by blending numerical data with rich narratives. It’s like having the best of both worlds.

Factors Influencing Choice of Research Methodology

Several factors influence the choice of research methodology:

  • Nature of the research question: Is it about measuring something objectively or understanding complex human behaviors?
  • Availability of resources: Do you have access to the tools and expertise needed for a particular methodology?
  • Time constraints: How much time do you have to conduct the research?
  • Ethical considerations: Are there any ethical concerns related to your research methods?

Steps Involved in Research Methodology for Project Topics

Regardless of the chosen methodology, research typically follows these steps:

  • Problem Definition: Clearly define the research question or problem you want to address.
  • Literature Review: Explore existing research and theories related to your topic to build a solid foundation.
  • Selection of Research Design: Choose the appropriate methodology based on your research question and objectives.
  • Data Collection: Gather relevant data using surveys, interviews, observations, or experiments.
  • Data Analysis: Analyze the collected data using statistical tools (for quantitative research) or thematic analysis (for qualitative research).
  • Interpretation of Results: Draw conclusions based on your analysis and discuss their implications.

Best Practices in Research Methodology for Project Topics

To ensure the quality and integrity of your research, follow these best practices:

  • Ensuring validity and reliability of data: Use reliable measurement tools and sampling techniques to minimize errors.
  • Ethical considerations in research: Obtain informed consent from participants, protect their privacy, and avoid any form of deception.
  • Proper documentation and citation: Keep detailed records of your research process and cite all sources properly to avoid plagiarism.
  • Peer review and feedback: Seek feedback from peers and experts in your field to improve the quality of your research.
  • The impact of online surveys on response rates and data quality.
  • Comparing the effectiveness of focus groups and individual interviews in marketing research.
  • Analyzing the ethical considerations of using social media data for research.
  • Exploring the potential of big data analytics in social science research.
  • Evaluating the reliability and validity of mixed-methods research approaches.
  • Examining the role of cultural sensitivity in international research projects.
  • Investigating the challenges and opportunities of conducting research in conflict zones.
  • Analyzing the effectiveness of different strategies for recruiting research participants.
  • Exploring the use of action research methodologies in addressing real-world problems.
  • Evaluating the impact of researcher bias on the research process and outcomes.
  • Investigating the potential of citizen science for collecting and analyzing data.
  • Exploring the use of virtual reality in conducting research studies.
  • Analyzing the ethical considerations of conducting research with vulnerable populations.
  • Evaluating the effectiveness of different strategies for disseminating research findings.
  • Examining the role of storytelling in qualitative research.
  • Investigating the use of visual methods in research, such as photography and video.
  • Analyzing the challenges and opportunities of conducting longitudinal research studies.
  • Exploring the use of case studies in research projects.
  • Evaluating the effectiveness of different strategies for coding and analyzing qualitative data.
  • Examining the role of theory in research design and analysis.
  • Investigating the use of discourse analysis methodologies in research.
  • Analyzing the strengths and limitations of quantitative research methods.
  • Exploring the use of experimental research designs in social science research.
  • Evaluating the effectiveness of different sampling techniques in research.
  • Examining the role of research ethics committees in ensuring the ethical conduct of research.
  • Investigating the challenges and opportunities of conducting research online.
  • Analyzing the impact of social media on public perceptions of research.
  • Exploring the use of gamification in research to increase participant engagement.
  • Evaluating the effectiveness of different strategies for data visualization.
  • Examining the role of open access in making research findings available to a wider audience.
  • Investigating the challenges and opportunities of interdisciplinary research collaborations.
  • Analyzing the impact of political and economic factors on research funding.
  • Exploring the use of participatory action research methodologies to empower communities.
  • Evaluating the effectiveness of different strategies for knowledge mobilization.
  • Examining the role of research in informing policy and practice.
  • Investigating the use of artificial intelligence in research methodologies.
  • Analyzing the ethical considerations of using facial recognition technology in research.
  • Exploring the potential of blockchain technology to improve data security and transparency in research.
  • Evaluating the effectiveness of different strategies for engaging with stakeholders in research projects.
  • Examining the role of reflexivity in qualitative research.
  • Investigating the use of narrative inquiry methodologies in research.
  • Analyzing the strengths and limitations of case studies as a research method.
  • Exploring the use of secondary data analysis in research projects.
  • Evaluating the effectiveness of different strategies for managing and storing research data.
  • Examining the role of research assistants in the research process.
  • Investigating the challenges and opportunities of conducting research in developing countries.
  • Analyzing the impact of climate change on research methodologies.
  • Exploring the use of citizen science for environmental monitoring.
  • Evaluating the effectiveness of different strategies for conducting research with indigenous communities.
  • Examining the role of research in promoting social justice.
  • Investigating the historical development of research methodologies.
  • Analyzing the impact of technological advancements on research practices.
  • Exploring the use of mixed methods research approaches in different disciplines.
  • Evaluating the effectiveness of different strategies for managing research projects.
  • Examining the role of research funders in shaping research agendas.
  • Investigating the challenges and opportunities of conducting research across different cultures.
  • Analyzing the impact of language barriers on research communication.
  • Exploring the use of collaborative online platforms for conducting research.
  • Evaluating the effectiveness of different strategies for promoting research skills development.
  • Examining the role of research misconduct in undermining public trust in research.
  • Investigating the challenges and opportunities of conducting research with children.
  • Analyzing the impact of research on mental health and well-being.
  • Exploring the use of arts-based research methodologies.
  • Evaluating the effectiveness of different strategies for recruiting and retaining research participants.
  • Examining the role of research networks in supporting researchers.
  • Investigating the challenges and opportunities of conducting research in the private sector.
  • Exploring the use of open science practices to promote research transparency and reproducibility.
  • Evaluating the effectiveness of different strategies for mentoring and supporting early-career researchers.
  • Examining the role of research misconduct in retracting scientific articles.
  • Investigating the challenges and opportunities of data sharing in research.
  • Analyzing the impact of open data initiatives on scientific progress.
  • Exploring the use of crowdsourcing in research to gather data and solve problems.
  • Evaluating the effectiveness of different strategies for promoting research impact.
  • Examining the role of alternative research metrics in evaluating the quality of research.
  • Investigating the use of bibliometrics to analyze research trends and identify emerging areas.
  • Analyzing the impact of research on public policy and decision-making.
  • Exploring the use of participatory research methodologies to empower communities.
  • Evaluating the effectiveness of different strategies for communicating research findings to the public.
  • Examining the role of social media in disseminating research findings.
  • Analyzing the impact of humanitarian aid on research practices in developing countries.
  • Exploring the use of research methodologies to address global challenges, such as climate change and poverty.
  • Evaluating the effectiveness of different strategies for building research capacity in developing countries.
  • Examining the role of international research collaborations in promoting global research excellence.
  • Investigating the challenges and opportunities of conducting research in the field of artificial intelligence.
  • Analyzing the ethical considerations of using autonomous robots in research.
  • Exploring the potential of artificial intelligence to automate research tasks.
  • Evaluating the effectiveness of different strategies for mitigating the risks of bias in artificial intelligence-powered research.
  • Examining the role of research in shaping the future of work.
  • Investigating the impact of automation on research jobs.
  • Exploring the use of new technologies to improve research efficiency and productivity.
  • Evaluating the effectiveness of different strategies for developing transferable skills for researchers.
  • Examining the role of lifelong learning in maintaining research expertise.
  • Investigating the impact of research funding cuts on research quality and innovation.
  • Exploring the use of alternative funding models, such as crowdfunding and philanthropy, to support research.
  • Evaluating the effectiveness of different strategies for advocating for increased research funding.
  • Examining the role of research universities in driving innovation and economic growth.
  • Investigating the impact of research on social and cultural change.
  • Exploring the future of research methodologies in an ever-changing world.

Examples of Research Methodology Project Topics

Here are some examples of project topics suited for different research methodologies:

Quantitative Research Topics

  • The impact of social media usage on mental health among teenagers.
  • Factors influencing customer satisfaction in the hospitality industry.

Qualitative Research Topics

  • Exploring the experiences of first-generation college students.
  • Understanding the challenges faced by small business owners during the COVID-19 pandemic.

Mixed-Methods Research Topics

  • Assessing the effectiveness of a school bullying prevention program .
  • Investigating the relationship between exercise habits and stress levels among working adults.

Research methodology is like a compass that guides you through the journey of inquiry. By understanding the different types of methodologies, factors influencing their choice, and best practices, you can embark on your research methodology project topics journey with confidence.

Remember, the key to successful research lies in asking the right questions and choosing the appropriate methodology to find the answers.

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  • Volume 12, Issue 1
  • Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case–control study
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  • http://orcid.org/0000-0002-9905-4855 Nikki L B Freeman 1 ,
  • Rashmi Muthukkumar 2 ,
  • Ruth S Weinstock 3 ,
  • M Victor Wickerhauser 4 ,
  • http://orcid.org/0000-0003-2701-101X Anna R Kahkoska 5 , 6
  • 1 Department of Surgery , University of North Carolina at Chapel Hill School of Medicine , Chapel Hill , North Carolina , USA
  • 2 Department of Medicine , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA
  • 3 Department of Medicine , SUNY Upstate Medical University , Syracuse , New York , USA
  • 4 Department of Mathematics , Washington University in St Louis , St Louis , Missouri , USA
  • 5 Department of Nutrition , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA
  • 6 Division of Endocrinology and Metabolism , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA
  • Correspondence to Dr Nikki L B Freeman; nlbf{at}live.unc.edu

Introduction Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures.

Research design and methods Data from a case–control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics.

Results Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score.

Conclusions Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.

  • Severe Hypoglycemia
  • Diabetes Mellitus, Type 1
  • Case-Control Studies

Data availability statement

The de-identified study datasets are available in a public, open-access repository.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjdrc-2023-003748

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Severe hypoglycemia in older adults with type 1 diabetes is associated with significant morbidity and mortality, and previous work by Weinstock et al identified the characteristics that distinguish older adults with a recent history of severe hypoglycemia from those without across a wide range of potentially important variables.

WHAT THIS STUDY ADDS

This study aimed to harness machine learning methods to uncover the relative importance of those variables, including demographic, clinical, lifestyle, and neurocognitive characteristics, and continuous glucose monitoring (CGM) measures associated with a history of severe hypoglycemia among older adults with type 1 diabetes.

The individual-level characteristics associated with a history of severe hypoglycemia were hypoglycemia unawareness, hypoglycemia fear, glycemic variability as measured by CGM (coefficient of variation), % time blood glucose below 70 mg/dL, and trail making test B score.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study shows how machine learning models can be applied to prioritize risk characteristics for severe hypoglycemia.

The results may inform future risk stratification tools for older adults designed to aid care providers in providing data-driven, individualized counseling related to severe hypoglycemia.

Introduction

Older adults with type 1 diabetes are a growing population within the USA. 1 Although hypoglycemia is a concern at any age for people with type 1 diabetes, older adults are at substantially higher risk of hypoglycemia compared with younger adults. It has previously been reported that the incidence of one or more episodes of severe hypoglycemia (SH) in patients over age 65 within a 12-month period is 16.1%. 2 For older adults who have had type 1 diabetes for 40 years or more, the incidence of SH can be as high as 18.6% within 1 year. 2 Another study has shown that older adults over age 60 with type 1 diabetes have double the risk for SH compared with their younger counterparts. 3

Among older adults, episodes of SH are associated with significant morbidity, including hospitalization, falls, fractures, altered mentation, and seizures. 1 4 In addition to the acute effects of hypoglycemia, in older adults the risk of other long-term side effects is also increased, including decreases in cognitive function. 5 SH episodes may affect cognition in older adults with type 1 diabetes related to language, executive function, and episodic memory, potentially because the brains of older adults are more susceptible to harm from SH compared with younger adults. 6 Avoiding these episodes is thus a priority of care. 7

The increasing risk of SH with age may be attributed to changes to cognitive status, 8 metabolism and insulin sensitivity, higher prevalence rates of hypoglycemia unawareness, frailty, and functional impairments, as well as polypharmacy. 7 However, a challenge for mitigating the risk of SH on an individual level is that its etiology among older adults is both complex and multifactorial. Previous work by Weinstock et al 4 collected comprehensive data from older adults with type 1 diabetes in a case–control design and found that SH events were associated with increased hypoglycemia unawareness and glucose variability, with cases and controls having similar mean hemoglobin A1c (HbA1c) and mean continuous glucose monitoring (CGM)-measured glucose levels. Given that there are potentially many characteristics which may impact the risk for SH, spanning demographic and clinical characteristics, behavioral and lifestyle characteristics, neurocognitive characteristics, and CGM measures, there is a need to identify singular and sets of individual-level characteristics which may serve to identify older adults at high risk of SH.

Machine learning methods can “mine” high-dimensional data to uncover complex relationships between multiple potential risk characteristics and outcomes with fewer assumptions than traditional methods. We hypothesized that these methods could complement findings from traditional statistical analyses such as those by Weinstock et al 4 to provide insight into how different risk characteristics may be prioritized in a clinical setting to identify older adults at highest risk for SH; this is an important step toward risk stratification to tailor efforts to reduce significant morbidity and possible mortality associated with SH in this population. Building on the work of Weinstock et al that identified factors to distinguish older adults with a recent history of SH from those without from a wide range of potentially important variables, this study aimed to understand the relative importance of those characteristics. The long-term objective of this study is to generate new insights that may improve clinical tools for enhanced risk stratification to identify older adults at risk of SH.

Research design and methods

Study design.

This study used a data set from a prior case–control study to identify risk characteristics for SH in older adults with type 1 diabetes. 4 The random forest algorithm was used to classify (ie, identify) cases versus controls based on individual-level characteristics (ie, covariates). Participants in the original case–control study consented to taking part in the study. 4

Data source

The data set for this study was initially used by Weinstock et al to identify risk characteristics for SH in older adults age 60 and older with diabetes duration of at least 20 years. 4 9 The original study was a case–control study with 201 participants from 18 T1D Exchange Clinical Network centers. 10 Cases were participants who reported an SH event within the past 12 months of study participation and controls did not have SH in the past 3 years. An SH event was defined as a hypoglycemic event leading to altered mentation or loss of consciousness and requiring the assistance of another individual to provide resuscitative assistance through carbohydrates, glucagon, or other means. Potential participants were excluded if they were current CGM users, recipients of pancreatic transplants, with life expectancy of less than 1 year, moderate or advanced dementia, or chronic kidney disease with a glomerular filtration rate of less than 30 mL/min/1.73 m 2 . 4

All data collection procedures are described in detail in Weinstock et al . 4 Demographic variables of interest included sex, race/ethnicity, education level, insurance type, and household size. Race/ethnicity was included as a social construct rather than to reflect differences in biology. Clinical variables of interest included body mass index, exercise, frequency of blood glucose monitoring, mean daytime and nocturnal blood glucose from CGM and blood glucose variability, HbA1c, insulin delivery system and dosing, medications, C peptide levels, creatinine, and hospitalization for diabetic ketoacidosis. Information on cognition, psychomotor skills, frailty, fear of hypoglycemia, hypoglycemia unawareness, and social support was also collected using a variety of survey and physical testing methods, described in the following.

Hypoglycemia unawareness was measured using the Clarke Hypoglycemia Awareness Questionnaire. 11 As noted in Weinstock et al , 4 the Clarke questionnaire includes questions about recent hypoglycemic events, which invalidates the use of the total score for this analysis. More recently, the Clarke score has been deconstructed into two subscales: SH experience and hypoglycemia awareness status. 12 To proxy hypoglycemia unawareness, removed from history of SH, we generated a raw score for the questionnaire elements that measures hypoglycemia awareness status and excluded the items that measure SH experience. Fear of hypoglycemia was assessed using the Hypoglycemia Fear Survey. 13 Neurocognitive testing was completed twice, 2 weeks apart. Mental status testing included the Montreal Cognitive Assessment. 14 Psychomotor testing was completed using the Symbol Digit Modalities Test. 15 Executive functioning was done using two trail making tests (trail making tests A and B). 16 17 Verbal memory was tested using the Hopkins Verbal Learning Test. 18 The grooved pegboard test was used to assess fine motor dexterity and speed. 19 Social support was assessed using the Duke Social Support Index. 20 Frailty was assessed using the timed 10-foot walk test. CGM data were blinded in the original study with SEVEN PLUS CGM devices worn by participants for 14 days with calibration daily. CGM was worn on average for 277 hours by case participants and 294 hours by control participants. The specific way in which each of these variables was operationalized in the model is shown in online supplemental table S1 .

Supplemental material

Statistical analysis.

Successively complex (ie, richer) models were examined to incorporate demographic and clinical characteristics, behavioral and lifestyle characteristics, neurocognitive characteristics, and CGM measures ( figure 1 ). The rationale for this approach was to use clinically accessible measures for model 1, exclusively. Subsequent models, models 2–4, include measures that require more time, tools, or resources to collect.

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Models. We tested four models that were successively more complex, incorporating more individual-level characteristics that may be associated with severe hypoglycemia. BMI, body mass index; CGM, continuous glucose monitoring; CV, coefficient of variation; HbA1c, hemoglobin A1c.

Stratified by case–control status and for the overall study population, we used descriptive statistics to summarize the characteristics of the study population. Binary and categorical characteristics were described using counts and percentages; numerical characteristics were described using the median, minimum, and maximum.

Because of missingness in the variables, we implemented multiple imputation 21 on the full analytic data set. Multiple imputation models the missing values conditional on the observed values. The model, in turn, is used to impute multiple likely values for the missing values and thus yields multiple imputed data sets. For our analysis, we generated 10 imputation data sets.

After multiple imputation, we split the observations into those to be included in the testing data set (test set) and those to be included in the training data set (training set). Observations included in the test set were chosen by randomly selecting 40% of the observations with complete data. Observations not in the test set were included in the training set. Overall, the train–test split was 78%/22%. Because observations included in the test set were complete cases, that is, information across the imputation data sets for the test cases was identical, the test set was exactly the single data set consisting of data for the test cases. In contrast, the training set consisted of the ten multiple imputation data sets subsetted on the observations selected as training cases.

To mitigate overfitting, we used feature selection techniques before fitting the machine learning models. First, we used correlation matrices to identify redundant characteristics and considered characteristics with an absolute correlation greater than 0.75 as redundant. Second, we used recursive feature elimination to identify the characteristics to include in our models to optimize accuracy.

Random forests 22 were trained on the training set and assessed on classification performance. For each of the four models considered, we fit a random forest on each of the imputation data sets in the training set. A random forest is a machine learning method that can be characterized as an ensemble of weak learners. 23 In the case of random forests, the weak learners are simple trees—the trees are the “learners,” or models, and they are called weak because individual trees on average have poor predictive power and performance. Ensembling, in the context of random forests, means that many trees are constructed and each tree “votes” to contribute to the final prediction yielded from the forest of trees. The “random” part of random forests refers to the injection of randomness in tree construction, for example, which characteristics are included in the tree and which ensures that the forest of trees has some heterogeneity and in turn improves performance. In our analysis, for each model, after training (ie, fitting) a random forest to each imputation set, we use the fitted model to classify observations in the test set as cases or controls. To assess model performance, sensitivity, specificity, and precision were calculated and averaged across the imputation sets.

Random forests naturally generate variable importance. In our analysis, we used the mean decrease in the Gini index to identify the importance of each variable. This metric is based on the idea of node purity. A node in a decision tree is a split point and each split is based on a variable. Node purity is a measure of the homogeneity of the labels at a particular node; the more homogeneous the labels the purer the node. The mean decrease in the Gini index captures the extent to which a particular variable, on average, decreases the impurity of a split among the constituent trees, or equivalently, the information gain from the use of that particular variable. The larger the mean decrease in the Gini index, the more important the variable across trees in the random forest.

Data and resource availability

The data set analyzed in the current study is publicly available from the Jaeb Center for Health Research database at https://public.jaeb.org/datasets/diabetes . 9 Analyses were conducted using the R statistical programming language. 24 The mice package 25 was used for multiple imputation, the caret package 26 was used to construct the test and training sets, and the randomForest package 27 was used to train the random forests. Git was used for version control; the code repository is stored on GitHub ( https://github.com/nikkifreeman/T1D_SH_key_predictors ).

Participant characteristics

This study used data for 191 participants from the Weinstock et al data set. Eight cases and four control participants were excluded due to missing demographics (two cases), having less than 7 days of CGM data (three cases), having less than 24 hours of night-time CGM (three cases, three controls), and not having CGM data (one control). The final analytic data set included 95 case participants and 96 controls; their characteristics are described in table 1 .

  • View inline

Study participants, by case and control status

The groups were similar in demographic characteristics based on sex, race/ethnicity, education, insurance status, annual income, and household size, with the majority being non-Hispanic white participants between 60 and 75 years old with at least some college education. There were differences between groups related to a variety of clinical characteristics. On average, the case participants monitored their blood glucose more frequently than the control group. Those in the case group had a greater percentage of time with hypoglycemic range blood glucose and had greater variability in their blood glucose measurements throughout the day and night. The case group also scored higher on frailty testing. Conversely, those in the control group scored lower on measures of hypoglycemia unawareness compared with controls. The testing for various functional modalities also showed differences among groups, with those in the control group demonstrating higher cognition, psychomotor skills, and dexterity.

Feature selection and model evaluation

Feature selection procedures revealed redundancy between two variables, the symbol digit modalities written test and the symbol digit modalities oral test. The controlled univariate analysis of the two tests in Weinstock et al 4 indicated a stronger signal for the written test than the oral test (p=0.001 vs p=0.01), so we dropped the oral test score as a covariate in our analysis. Recursive feature elimination did not provide compelling evidence for dropping additional variables from any of our models; thus, no additional variables were eliminated from our analyses (full results in online supplemental figure S2 ). Performance metrics for the fitted random forests, across all four models, are shown in table 2 . The richer models, that is, models 2, 3, and 4, which had more variables as inputs, were more sensitive than model 1, and model 1 was more specific than the richer models. Precision was similar across models 1, 2, 3, and 4.

Random forest model classification performance*

Modeling results

Figure 2 depicts the top five individual-level characteristics associated with having experienced an episode of SH from models 1–4 based on the mean decrease in the Gini index (full results in online supplemental figure S3 ). In model 1, which examined demographic and clinical characteristics, hypoglycemia awareness, HbA1c, glucose monitoring frequency, frailty, and insurance emerged as the most important for discerning between older adults with and without a history of SH. In model 2, where behavioral and lifestyle characteristics were added, hypoglycemia fear and the Duke Social Support Index additionally emerged as key characteristics, displacing frailty and insurance. In model 3, in which neurocognitive characteristics were added, the top five characteristics were hypoglycemia unawareness, hypoglycemia fear, the results of the Symbol Digit Modalities Test (written), the results of the trail making test - test A, and the results of trail making - test B. Finally, in model 4, which additionally included CGM measures, glucose variability as measured by % coefficient of variation and the per cent of time blood glucose below 70 mg/dL emerged as key variables associated with SH history.

Top five characteristics from each model. These are the individual-level characteristics that emerged as most important for discerning between older adults with and without a history of severe hypoglycemia. CGM, continuous glucose monitoring; CV, coefficient of variation; HbA1c, hemoglobin A1c.

We used a machine learning method and data from 191 older adults with type 1 diabetes to identify the individual-level characteristics that were most strongly associated with having experienced an episode of SH, exploring a series of successively complex models using rich and diverse data. We found that when taking into account all possible demographic, clinical, neurocognitive characteristics, and CGM measures, the characteristics associated with a history of SH compared with those who have not had SH were hypoglycemia unawareness, hypoglycemia fear, glycemic variability as measured by CGM (coefficient of variation), the percent of time with blood glucose below 70 mg/dL, and trail making test B score. These results add to the limited literature for older adults with type 1 diabetes and provide a glimpse into the interactions and relative importance of the range of characteristics that are known to contribute to the risk of SH in this age group. Our results point to the important role of hypoglycemia unawareness in the cycle of SH, as well as how shorter-term measures of glycemia and glucose dynamics can be prioritized as part of the set of characteristics associated with long-term risk for SH. Our analysis also underscores the potential utility of incorporating more comprehensive information, including behavioral, neurocognitive, and CGM data, to discern older adult individuals who are at risk for hypoglycemia.

It has been shown that older adults with type 1 diabetes have double the risk of SH compared with their younger counterparts. 3 This is especially true for older adults who have had diabetes for many decades, as the incidence of SH in 1 year increases with longer diabetes duration. 2 As a result, understanding what characteristics put this particularly vulnerable population at increased risk in may help to guide interventions to prevent the potentially devastating impact that SH can have on the health and quality of life of this population. Yet it remains unclear how to make use of diverse input information as part of stratifying older adults with type 1 diabetes based on their risk for SH. 1

To address this gap, our study used the rich demographic and clinical risk characteristics investigated in the study by Weinstock et al 4 to explore potentially complex relationships between those risk characteristics and SH through machine learning modeling. This type of modeling allows for not only the identification of risk characteristics in a more flexible manner than traditional regression style approaches but also to identify the relative importance of those characteristics for SH compared with each other, effectively allowing for prioritization of risk characteristics. The rich data set allowed for the inclusion of characteristics beyond demographic and clinical data to explore behavioral, lifestyle, and neurocognitive risk characteristics associated with SH. Examination of the relative importance of variables in each of the successively rich models illustrates that SH risk is the interplay of characteristics across a multiplicity of domains. Rather than observing characteristics from a single domain, such as clinical characteristics, dominating in importance across models, figure 2 shows that consistently across models the most important characteristics came from a mix of domains. Model sensitivity increased as more characteristic types were included, and the best-performing model in terms of sensitivity was model 4, which incorporated demographic, clinical, behavioral and lifestyle, neurocognitive, and lifestyle characteristics, along with CGM measures, thereby providing a more holistic and detailed view of which characteristics can contribute to SH. Model 4 is important to consider since the complexity of older adults is incompletely captured by their demographic information and basic clinical and laboratory information.

As expected and consistent with Weinstock et al , hypoglycemia unawareness was an important risk characteristic for SH in the random forest modeling. 4 Based on recent studies of the Clark questionnaire, 11 12 we intentionally calculated a score to reflect the construct of hypoglycemia unawareness rather than history of SH. Interestingly, this characteristic remained the most important characteristic associated with SH across all four models and was thus robust to the addition of other information. Physiological changes related to aging such as hormonal response to hypoglycemia can make older adults particularly vulnerable to hypoglycemia unawareness. 28 Weinstock et al 4 also found that fear of hypoglycemia was increased in those with recent SH; this characteristic emerged as a significant characteristic that remained robust across models 2–4, although the temporality of the relationship between this variable and the outcome of SH remains unclear in the case–control design. It is probably that older adults who recently experienced SH reported higher fear as a result of their event.

The vast majority of evidence detailing cognitive function in older adults with diabetes primarily involves those with type 2 diabetes. 8 29 One aspect of the original data set that is particularly interesting was the use of multiple neurocognitive assessments given the significant impact that SH can have on cognition in this population. 5 6 Weinstock et al 4 used the Montreal Cognitive Assessment, the Symbol Digit Modalities Test, the trail making test, and the grooved pegboard test to assess cognition and functioning. There were significant differences among case and control participants related to certain cognitive and functional tests, but it was not possible to elucidate in that study which tests are most predictive in differentiating those at higher risk for SH. The trail making test for executive functioning was a significant characteristic in the original study, and our modeling similarly indicated that trail B was a more significant characteristic compared with the trail A test for executive functioning for case participants. This test for executive functioning has been used in other studies of older adults with type 1 diabetes and those with recent SH did perform worse on that test. 6 Our results advance an understanding of the relative importance of this measure of executive functioning in the context of other potential risk characteristics, underscoring that these characteristics are likely informative in this age group.

Machine learning methods have been used in other studies for a variety of applications for people with type 1 diabetes including for predicting hypoglycemia. Those studies often used CGM data to predict the risk of hypoglycemia in the shorter term. 30–32 Additionally, these usually involved individuals who were younger with shorter diabetes duration and aimed to understand the risk of hypoglycemia in the immediate future based on CGM data. Since the risk of SH increases with increased diabetes duration, it is important to apply these methods to this group as well. While a number of machine learning methods were available for this analysis, we preferred the random forest algorithm because of its ability to naturally select important variables over a method like support vector machines and its ability to handle categorical features better than a method like L1-regularized logistic regression.

Given that the data from this study came from a case–control study, where the case status was based on a retrospective hypoglycemic event, the results provide insight into the characteristics that are robustly associated with SH, rather than true “risk factors” that are associated with acute events in the future. Prospective studies are needed to empirically test the predictive performance of these characteristics, including combinations thereof. A further limitation of this analysis is that participants who regularly use CGM were excluded, which limits generalizability to contemporary populations as CGM or closed-loop systems are becoming more common in older adults. Moreover, those who use CGM may have a different relationship with SH and other risk characteristics that cannot be accurately predicted using this model. In addition, the study used data from 191 participants from the T1D Exchange Clinical Network, a relatively small cohort consisting of a majority of non-Hispanic white participants. 4 As a result, models based on a more diverse population may have different risk characteristics that have contributed more to past SH or show differences in future SH events as in a prospective study. Because of the modest sample size, the number of CGM metrics included in the analysis were limited to those known to be associated with SH risk. Moreover, the variables of age, diabetes duration, or diabetes-related complications were not available in the data set. Including these variables may change the top five key characteristics across all models. Assessments of the social determinants of health were also not included despite the known importance of these variables in diabetes outcomes. 33

The strengths of the study include the use of novel machine learning methods and the ability to compare our findings with prior work to assess for clinical validity of the machine learning models and elucidate how these methods complement traditional regression approaches. There are many possible risk characteristics for SH, and traditional statistical methods, which can help identify whether a characteristic is a risk characteristic or not, may be complemented by machine learning methods that can, for example, provide perspective on the relative importance of those characteristics. The models in this analysis allow for prioritization of potential risk characteristics so clinicians can more efficiently use their appointments to provide more personalized, yet data-driven advice for patients who have similar characteristics to those who have experienced SH. Future work in this space can be used to create risk stratification tools for clinician use. The data set that was used was also an important strength of this analysis because we were able to go beyond simple demographic or clinical measures and explore neurocognitive functioning in addition to CGM measures, thus providing a more holistic picture of the participants involved. Together, these results provide a glimpse into how varying levels of individual-level data can be prioritized in clinical settings to inform discussions with their older adult patients.

Ethics statements

Patient consent for publication.

Not required.

Ethics approval

This study involves human participants. This study analyzed publicly available, deidentified data from a research study that was previously completed. Participants gave informed consent to participate before taking part in the original study. The University of North Carolina at Chapel Hill Institutional Review Board reviewed and approved the study (IRB 23-3227). It was deemed exempt due to being a category 4 study (secondary data/specimens).

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

  • Data supplement 1

Contributors NLBF and ARK conceived the study. NLBF, MVW, and ARK designed the analysis plan. NLBF conducted the statistical analyses. All authors contributed to the interpretation of the results. RM, NLBF, and ARK prepared the manuscript with contributions from MVW and RW. NLBF is the guarantor of this work.

Funding ARK is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR002490. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. ARK also reports receiving research grants from the Diabetes Research Connection and the American Diabetes Association, and a prize from the National Academy of Medicine, outside the submitted work.

Competing interests RW participated in multicenter clinical trials through her institution, sponsored by Insulet, Medtronic, Eli Lilly, Novo Nordisk, and Boehringer Ingelheim, and has used donated DexCom CGMs and Tandem insulin pumps in projects sponsored by the NIH and the Leona M and Harry B Helmsley Charitable Trust.

Provenance and peer review Not commissioned; externally peer reviewed.

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

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