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Case Study – Methods, Examples and Guide

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

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Case studies are effective research methods that focus on one specific case over time. This gives a detailed view that's great for learning.

Writing a case study is a very useful form of study in the educational process. With real-life examples, students can learn more effectively. 

A case study also has different types and forms. As a rule of thumb, all of them require a detailed and convincing answer based on a thorough analysis.

In this blog, we are going to discuss the different types of case study research methods in detail.

So, let’s dive right in!

• 1. Understanding Case Studies
• 2. What are the Types of Case Study?
• 3. Types of Subjects of Case Study 
• 4. Benefits of Case Study for Students

Understanding Case Studies

Case studies are a type of research methodology. Case study research designs examine subjects, projects, or organizations to provide an analysis based on the evidence.

It allows you to get insight into what causes any subject’s decisions and actions. This makes case studies a great way for students to develop their research skills.

A case study focuses on a single project for an extended period, which allows students to explore the topic in depth.

What are the Types of Case Study?

Multiple case studies are used for different purposes. The main purpose of case studies is to analyze problems within the boundaries of a specific organization, environment, or situation. 

Many aspects of a case study such as data collection and analysis, qualitative research questions, etc. are dependent on the researcher and what the study is looking to address. 

Case studies can be divided into the following categories:

Illustrative Case Study

Exploratory case study, cumulative case study, critical instance case study, descriptive case study, intrinsic case study, instrumental case study.

Let’s take a look at the detailed description of each type of case study with examples. 

An illustrative case study is used to examine a familiar case to help others understand it. It is one of the main types of case studies in research methodology and is primarily descriptive. 

In this type of case study, usually, one or two instances are used to explain what a situation is like. 

Here is an example to help you understand it better:

Illustrative Case Study Example

An exploratory case study is usually done before a larger-scale research. These types of case studies are very popular in the social sciences like political science and primarily focus on real-life contexts and situations.

This method is useful in identifying research questions and methods for a large and complex study. 

Let’s take a look at this example to help you have a better understanding:

Exploratory Case Study Example

A cumulative case study is one of the main types of case studies in qualitative research. It is used to collect information from different sources at different times.

This case study aims to summarize the past studies without spending additional cost and time on new investigations. 

Let’s take a look at the example below:

Cumulative Case Study Example

Critical instances case studies are used to determine the cause and consequence of an event. 

The main reason for this type of case study is to investigate one or more sources with unique interests and sometimes with no interest in general. 

Take a look at this example below:

Critical Instance Case Study Example

When you have a hypothesis, you can design a descriptive study. It aims to find connections between the subject being studied and a theory.

After making these connections, the study can be concluded. The results of the descriptive case study will usually suggest how to develop a theory further.

This example can help you understand the concept better:

Descriptive Case Study Example

Intrinsic studies are more commonly used in psychology, healthcare, or social work. So, if you were looking for types of case studies in sociology, or types of case studies in social research, this is it.

The focus of intrinsic studies is on the individual. The aim of such studies is not only to understand the subject better but also their history and how they interact with their environment.

Here is an example to help you understand;

Intrinsic Case Study Example

This type of case study is mostly used in qualitative research. In an instrumental case study, the specific case is selected to provide information about the research question.

It offers a lens through which researchers can explore complex concepts, theories, or generalizations.

Take a look at the example below to have a better understanding of the concepts:

Instrumental Case Study Example

Review some case study examples to help you understand how a specific case study is conducted.

Types of Subjects of Case Study 

In general, there are 5 types of subjects that case studies address. Every case study fits into the following subject categories. 

• Person: This type of study focuses on one subject or individual and can use several research methods to determine the outcome. 
• Group: This type of study takes into account a group of individuals. This could be a group of friends, coworkers, or family. 
• Location: The main focus of this type of study is the place. It also takes into account how and why people use the place. 
• Organization: This study focuses on an organization or company. This could also include the company employees or people who work in an event at the organization. 
• Event: This type of study focuses on a specific event. It could be societal or cultural and examines how it affects the surroundings. 

Benefits of Case Study for Students

Here's a closer look at the multitude of benefits students can have with case studies:

Real-world Application

Case studies serve as a crucial link between theory and practice. By immersing themselves in real-world scenarios, students can apply theoretical knowledge to practical situations.

Critical Thinking Skills

Analyzing case studies demands critical thinking and informed decision-making. Students cultivate the ability to evaluate information, identify key factors, and develop well-reasoned solutions – essential skills in both academic and professional contexts.

Enhanced Problem-solving Abilities

Case studies often present complex problems that require creative and strategic solutions. Engaging with these challenges refines students' problem-solving skills, encouraging them to think innovatively and develop effective approaches.

Holistic Understanding

Going beyond theoretical concepts, case studies provide a holistic view of a subject. Students gain insights into the multifaceted aspects of a situation, helping them connect the dots and understand the broader context.

Exposure to Diverse Perspectives

Case studies often encompass a variety of industries, cultures, and situations. This exposure broadens students' perspectives, fostering a more comprehensive understanding of the world and the challenges faced by different entities.

So there you have it!

We have explored different types of case studies and their examples. Case studies act as the tools to understand and deal with the many challenges and opportunities around us.

Case studies are being used more and more in colleges and universities to help students understand how a hypothetical event can influence a person, group, or organization in real life. 

Not everyone can handle the case study writing assignment easily. It is even scary to think that your time and work could be wasted if you don't do the case study paper right. 

Our professional paper writing service is here to make your academic journey easier. 

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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What Is 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."

Cara Lustik is a fact-checker and copywriter.

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."

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

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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Research Writing and Analysis

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Writing a Case Study

What is a case study?

A Case study is: 

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

What are the different types of case studies?

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

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

Who are your case study participants?

What is triangulation ? 

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

How to write a Case Study?

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

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• Last Updated: Apr 18, 2024 1:08 PM
• URL: https://resources.nu.edu/researchtools

What is case study research?

Last updated

8 February 2023

Reviewed by

Cathy Heath

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

Organization

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

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

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

Analyze case study research

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

• What are the different types of case study designs?

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

Here are the common types of case study design:

Explanatory

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

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

Descriptive

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

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

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

Exploratory

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

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

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

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

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

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

Critical instance

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

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

Instrumental

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

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

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

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

• When do you use case studies?

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

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

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

1. Select a case

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

2. Create a theoretical framework

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

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

3. Collect the data

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

4. Analyze your case

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

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

• What are some case study examples?

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

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

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

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

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

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

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Case Studies

This guide examines case studies, a form of qualitative descriptive research that is used to look at individuals, a small group of participants, or a group as a whole. Researchers collect data about participants using participant and direct observations, interviews, protocols, tests, examinations of records, and collections of writing samples. Starting with a definition of the case study, the guide moves to a brief history of this research method. Using several well documented case studies, the guide then looks at applications and methods including data collection and analysis. A discussion of ways to handle validity, reliability, and generalizability follows, with special attention to case studies as they are applied to composition studies. Finally, this guide examines the strengths and weaknesses of case studies.

Definition and Overview

Case study refers to the collection and presentation of detailed information about a particular participant or small group, frequently including the accounts of subjects themselves. A form of qualitative descriptive research, the case study looks intensely at an individual or small participant pool, drawing conclusions only about that participant or group and only in that specific context. Researchers do not focus on the discovery of a universal, generalizable truth, nor do they typically look for cause-effect relationships; instead, emphasis is placed on exploration and description.

Case studies typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located. Thick description also involves interpreting the meaning of demographic and descriptive data such as cultural norms and mores, community values, ingrained attitudes, and motives.

Unlike quantitative methods of research, like the survey, which focus on the questions of who, what, where, how much, and how many, and archival analysis, which often situates the participant in some form of historical context, case studies are the preferred strategy when how or why questions are asked. Likewise, they are the preferred method when the researcher has little control over the events, and when there is a contemporary focus within a real life context. In addition, unlike more specifically directed experiments, case studies require a problem that seeks a holistic understanding of the event or situation in question using inductive logic--reasoning from specific to more general terms.

In scholarly circles, case studies are frequently discussed within the context of qualitative research and naturalistic inquiry. Case studies are often referred to interchangeably with ethnography, field study, and participant observation. The underlying philosophical assumptions in the case are similar to these types of qualitative research because each takes place in a natural setting (such as a classroom, neighborhood, or private home), and strives for a more holistic interpretation of the event or situation under study.

Unlike more statistically-based studies which search for quantifiable data, the goal of a case study is to offer new variables and questions for further research. F.H. Giddings, a sociologist in the early part of the century, compares statistical methods to the case study on the basis that the former are concerned with the distribution of a particular trait, or a small number of traits, in a population, whereas the case study is concerned with the whole variety of traits to be found in a particular instance" (Hammersley 95).

Case studies are not a new form of research; naturalistic inquiry was the primary research tool until the development of the scientific method. The fields of sociology and anthropology are credited with the primary shaping of the concept as we know it today. However, case study research has drawn from a number of other areas as well: the clinical methods of doctors; the casework technique being developed by social workers; the methods of historians and anthropologists, plus the qualitative descriptions provided by quantitative researchers like LePlay; and, in the case of Robert Park, the techniques of newspaper reporters and novelists.

Park was an ex-newspaper reporter and editor who became very influential in developing sociological case studies at the University of Chicago in the 1920s. As a newspaper professional he coined the term "scientific" or "depth" reporting: the description of local events in a way that pointed to major social trends. Park viewed the sociologist as "merely a more accurate, responsible, and scientific reporter." Park stressed the variety and value of human experience. He believed that sociology sought to arrive at natural, but fluid, laws and generalizations in regard to human nature and society. These laws weren't static laws of the kind sought by many positivists and natural law theorists, but rather, they were laws of becoming--with a constant possibility of change. Park encouraged students to get out of the library, to quit looking at papers and books, and to view the constant experiment of human experience. He writes, "Go and sit in the lounges of the luxury hotels and on the doorsteps of the flophouses; sit on the Gold Coast settees and on the slum shakedowns; sit in the Orchestra Hall and in the Star and Garter Burlesque. In short, gentlemen [sic], go get the seats of your pants dirty in real research."

But over the years, case studies have drawn their share of criticism. In fact, the method had its detractors from the start. In the 1920s, the debate between pro-qualitative and pro-quantitative became quite heated. Case studies, when compared to statistics, were considered by many to be unscientific. From the 1930's on, the rise of positivism had a growing influence on quantitative methods in sociology. People wanted static, generalizable laws in science. The sociological positivists were looking for stable laws of social phenomena. They criticized case study research because it failed to provide evidence of inter subjective agreement. Also, they condemned it because of the few number of cases studied and that the under-standardized character of their descriptions made generalization impossible. By the 1950s, quantitative methods, in the form of survey research, had become the dominant sociological approach and case study had become a minority practice.

Educational Applications

The 1950's marked the dawning of a new era in case study research, namely that of the utilization of the case study as a teaching method. "Instituted at Harvard Business School in the 1950s as a primary method of teaching, cases have since been used in classrooms and lecture halls alike, either as part of a course of study or as the main focus of the course to which other teaching material is added" (Armisted 1984). The basic purpose of instituting the case method as a teaching strategy was "to transfer much of the responsibility for learning from the teacher on to the student, whose role, as a result, shifts away from passive absorption toward active construction" (Boehrer 1990). Through careful examination and discussion of various cases, "students learn to identify actual problems, to recognize key players and their agendas, and to become aware of those aspects of the situation that contribute to the problem" (Merseth 1991). In addition, students are encouraged to "generate their own analysis of the problems under consideration, to develop their own solutions, and to practically apply their own knowledge of theory to these problems" (Boyce 1993). Along the way, students also develop "the power to analyze and to master a tangled circumstance by identifying and delineating important factors; the ability to utilize ideas, to test them against facts, and to throw them into fresh combinations" (Merseth 1991).

In addition to the practical application and testing of scholarly knowledge, case discussions can also help students prepare for real-world problems, situations and crises by providing an approximation of various professional environments (i.e. classroom, board room, courtroom, or hospital). Thus, through the examination of specific cases, students are given the opportunity to work out their own professional issues through the trials, tribulations, experiences, and research findings of others. An obvious advantage to this mode of instruction is that it allows students the exposure to settings and contexts that they might not otherwise experience. For example, a student interested in studying the effects of poverty on minority secondary student's grade point averages and S.A.T. scores could access and analyze information from schools as geographically diverse as Los Angeles, New York City, Miami, and New Mexico without ever having to leave the classroom.

The case study method also incorporates the idea that students can learn from one another "by engaging with each other and with each other's ideas, by asserting something and then having it questioned, challenged and thrown back at them so that they can reflect on what they hear, and then refine what they say" (Boehrer 1990). In summary, students can direct their own learning by formulating questions and taking responsibility for the study.

Types and Design Concerns

Researchers use multiple methods and approaches to conduct case studies.

Types of Case Studies

Under the more generalized category of case study exist several subdivisions, each of which is custom selected for use depending upon the goals and/or objectives of the investigator. These types of case study include the following:

Illustrative Case Studies These are primarily descriptive studies. They typically utilize one or two instances of an event to show what a situation is like. Illustrative case studies serve primarily to make the unfamiliar familiar and to give readers a common language about the topic in question.

Exploratory (or pilot) Case Studies These are condensed case studies performed before implementing a large scale investigation. Their basic function is to help identify questions and select types of measurement prior to the main investigation. The primary pitfall of this type of study is that initial findings may seem convincing enough to be released prematurely as conclusions.

Cumulative Case Studies These serve to aggregate information from several sites collected at different times. The idea behind these studies is the collection of past studies will allow for greater generalization without additional cost or time being expended on new, possibly repetitive studies.

Critical Instance Case Studies These examine one or more sites for either the purpose of examining a situation of unique interest with little to no interest in generalizability, or to call into question or challenge a highly generalized or universal assertion. This method is useful for answering cause and effect questions.

Identifying a Theoretical Perspective

Much of the case study's design is inherently determined for researchers, depending on the field from which they are working. In composition studies, researchers are typically working from a qualitative, descriptive standpoint. In contrast, physicists will approach their research from a more quantitative perspective. Still, in designing the study, researchers need to make explicit the questions to be explored and the theoretical perspective from which they will approach the case. The three most commonly adopted theories are listed below:

Individual Theories These focus primarily on the individual development, cognitive behavior, personality, learning and disability, and interpersonal interactions of a particular subject.

Organizational Theories These focus on bureaucracies, institutions, organizational structure and functions, or excellence in organizational performance.

Social Theories These focus on urban development, group behavior, cultural institutions, or marketplace functions.

Two examples of case studies are used consistently throughout this chapter. The first, a study produced by Berkenkotter, Huckin, and Ackerman (1988), looks at a first year graduate student's initiation into an academic writing program. The study uses participant-observer and linguistic data collecting techniques to assess the student's knowledge of appropriate discourse conventions. Using the pseudonym Nate to refer to the subject, the study sought to illuminate the particular experience rather than to generalize about the experience of fledgling academic writers collectively.

For example, in Berkenkotter, Huckin, and Ackerman's (1988) study we are told that the researchers are interested in disciplinary communities. In the first paragraph, they ask what constitutes membership in a disciplinary community and how achieving membership might affect a writer's understanding and production of texts. In the third paragraph they state that researchers must negotiate their claims "within the context of his sub specialty's accepted knowledge and methodology." In the next paragraph they ask, "How is literacy acquired? What is the process through which novices gain community membership? And what factors either aid or hinder students learning the requisite linguistic behaviors?" This introductory section ends with a paragraph in which the study's authors claim that during the course of the study, the subject, Nate, successfully makes the transition from "skilled novice" to become an initiated member of the academic discourse community and that his texts exhibit linguistic changes which indicate this transition. In the next section the authors make explicit the sociolinguistic theoretical and methodological assumptions on which the study is based (1988). Thus the reader has a good understanding of the authors' theoretical background and purpose in conducting the study even before it is explicitly stated on the fourth page of the study. "Our purpose was to examine the effects of the educational context on one graduate student's production of texts as he wrote in different courses and for different faculty members over the academic year 1984-85." The goal of the study then, was to explore the idea that writers must be initiated into a writing community, and that this initiation will change the way one writes.

The second example is Janet Emig's (1971) study of the composing process of a group of twelfth graders. In this study, Emig seeks to answer the question of what happens to the self as a result educational stimuli in terms of academic writing. The case study used methods such as protocol analysis, tape-recorded interviews, and discourse analysis.

In the case of Janet Emig's (1971) study of the composing process of eight twelfth graders, four specific hypotheses were made:

• Twelfth grade writers engage in two modes of composing: reflexive and extensive.
• These differences can be ascertained and characterized through having the writers compose aloud their composition process.
• A set of implied stylistic principles governs the writing process.
• For twelfth grade writers, extensive writing occurs chiefly as a school-sponsored activity, or reflexive, as a self-sponsored activity.

In this study, the chief distinction is between the two dominant modes of composing among older, secondary school students. The distinctions are:

• The reflexive mode, which focuses on the writer's thoughts and feelings.
• The extensive mode, which focuses on conveying a message.

Emig also outlines the specific questions which guided the research in the opening pages of her Review of Literature , preceding the report.

Designing a Case Study

After considering the different sub categories of case study and identifying a theoretical perspective, researchers can begin to design their study. Research design is the string of logic that ultimately links the data to be collected and the conclusions to be drawn to the initial questions of the study. Typically, research designs deal with at least four problems:

• What questions to study
• What data are relevant
• What data to collect
• How to analyze that data

In other words, a research design is basically a blueprint for getting from the beginning to the end of a study. The beginning is an initial set of questions to be answered, and the end is some set of conclusions about those questions.

Because case studies are conducted on topics as diverse as Anglo-Saxon Literature (Thrane 1986) and AIDS prevention (Van Vugt 1994), it is virtually impossible to outline any strict or universal method or design for conducting the case study. However, Robert K. Yin (1993) does offer five basic components of a research design:

• A study's questions.
• A study's propositions (if any).
• A study's units of analysis.
• The logic that links the data to the propositions.
• The criteria for interpreting the findings.

In addition to these five basic components, Yin also stresses the importance of clearly articulating one's theoretical perspective, determining the goals of the study, selecting one's subject(s), selecting the appropriate method(s) of collecting data, and providing some considerations to the composition of the final report.

Conducting Case Studies

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of approaches and methods. These approaches, methods, and related issues are discussed in depth in this section.

Method: Single or Multi-modal?

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of methods. Some common methods include interviews , protocol analyses, field studies, and participant-observations. Emig (1971) chose to use several methods of data collection. Her sources included conversations with the students, protocol analysis, discrete observations of actual composition, writing samples from each student, and school records (Lauer and Asher 1988).

Berkenkotter, Huckin, and Ackerman (1988) collected data by observing classrooms, conducting faculty and student interviews, collecting self reports from the subject, and by looking at the subject's written work.

A study that was criticized for using a single method model was done by Flower and Hayes (1984). In this study that explores the ways in which writers use different forms of knowing to create space, the authors used only protocol analysis to gather data. The study came under heavy fire because of their decision to use only one method.

Participant Selection

Case studies can use one participant, or a small group of participants. However, it is important that the participant pool remain relatively small. The participants can represent a diverse cross section of society, but this isn't necessary.

For example, the Berkenkotter, Huckin, and Ackerman (1988) study looked at just one participant, Nate. By contrast, in Janet Emig's (1971) study of the composition process of twelfth graders, eight participants were selected representing a diverse cross section of the community, with volunteers from an all-white upper-middle-class suburban school, an all-black inner-city school, a racially mixed lower-middle-class school, an economically and racially mixed school, and a university school.

Often, a brief "case history" is done on the participants of the study in order to provide researchers with a clearer understanding of their participants, as well as some insight as to how their own personal histories might affect the outcome of the study. For instance, in Emig's study, the investigator had access to the school records of five of the participants, and to standardized test scores for the remaining three. Also made available to the researcher was the information that three of the eight students were selected as NCTE Achievement Award winners. These personal histories can be useful in later stages of the study when data are being analyzed and conclusions drawn.

Data Collection

There are six types of data collected in case studies:

• Archival records.
• Interviews.
• Direct observation.
• Participant observation.

In the field of composition research, these six sources might be:

• A writer's drafts.
• School records of student writers.
• Transcripts of interviews with a writer.
• Transcripts of conversations between writers (and protocols).
• Videotapes and notes from direct field observations.
• Hard copies of a writer's work on computer.

Depending on whether researchers have chosen to use a single or multi-modal approach for the case study, they may choose to collect data from one or any combination of these sources.

Protocols, that is, transcriptions of participants talking aloud about what they are doing as they do it, have been particularly common in composition case studies. For example, in Emig's (1971) study, the students were asked, in four different sessions, to give oral autobiographies of their writing experiences and to compose aloud three themes in the presence of a tape recorder and the investigator.

In some studies, only one method of data collection is conducted. For example, the Flower and Hayes (1981) report on the cognitive process theory of writing depends on protocol analysis alone. However, using multiple sources of evidence to increase the reliability and validity of the data can be advantageous.

Case studies are likely to be much more convincing and accurate if they are based on several different sources of information, following a corroborating mode. This conclusion is echoed among many composition researchers. For example, in her study of predrafting processes of high and low-apprehensive writers, Cynthia Selfe (1985) argues that because "methods of indirect observation provide only an incomplete reflection of the complex set of processes involved in composing, a combination of several such methods should be used to gather data in any one study." Thus, in this study, Selfe collected her data from protocols, observations of students role playing their writing processes, audio taped interviews with the students, and videotaped observations of the students in the process of composing.

It can be said then, that cross checking data from multiple sources can help provide a multidimensional profile of composing activities in a particular setting. Sharan Merriam (1985) suggests "checking, verifying, testing, probing, and confirming collected data as you go, arguing that this process will follow in a funnel-like design resulting in less data gathering in later phases of the study along with a congruent increase in analysis checking, verifying, and confirming."

It is important to note that in case studies, as in any qualitative descriptive research, while researchers begin their studies with one or several questions driving the inquiry (which influence the key factors the researcher will be looking for during data collection), a researcher may find new key factors emerging during data collection. These might be unexpected patterns or linguistic features which become evident only during the course of the research. While not bearing directly on the researcher's guiding questions, these variables may become the basis for new questions asked at the end of the report, thus linking to the possibility of further research.

Data Analysis

As the information is collected, researchers strive to make sense of their data. Generally, researchers interpret their data in one of two ways: holistically or through coding. Holistic analysis does not attempt to break the evidence into parts, but rather to draw conclusions based on the text as a whole. Flower and Hayes (1981), for example, make inferences from entire sections of their students' protocols, rather than searching through the transcripts to look for isolatable characteristics.

However, composition researchers commonly interpret their data by coding, that is by systematically searching data to identify and/or categorize specific observable actions or characteristics. These observable actions then become the key variables in the study. Sharan Merriam (1988) suggests seven analytic frameworks for the organization and presentation of data:

• The role of participants.
• The network analysis of formal and informal exchanges among groups.
• Historical.
• Thematical.
• Ritual and symbolism.
• Critical incidents that challenge or reinforce fundamental beliefs, practices, and values.

There are two purposes of these frameworks: to look for patterns among the data and to look for patterns that give meaning to the case study.

As stated above, while most researchers begin their case studies expecting to look for particular observable characteristics, it is not unusual for key variables to emerge during data collection. Typical variables coded in case studies of writers include pauses writers make in the production of a text, the use of specific linguistic units (such as nouns or verbs), and writing processes (planning, drafting, revising, and editing). In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, researchers coded the participant's texts for use of connectives, discourse demonstratives, average sentence length, off-register words, use of the first person pronoun, and the ratio of definite articles to indefinite articles.

Since coding is inherently subjective, more than one coder is usually employed. In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, three rhetoricians were employed to code the participant's texts for off-register phrases. The researchers established the agreement among the coders before concluding that the participant used fewer off-register words as the graduate program progressed.

Composing the Case Study Report

In the many forms it can take, "a case study is generically a story; it presents the concrete narrative detail of actual, or at least realistic events, it has a plot, exposition, characters, and sometimes even dialogue" (Boehrer 1990). Generally, case study reports are extensively descriptive, with "the most problematic issue often referred to as being the determination of the right combination of description and analysis" (1990). Typically, authors address each step of the research process, and attempt to give the reader as much context as possible for the decisions made in the research design and for the conclusions drawn.

This contextualization usually includes a detailed explanation of the researchers' theoretical positions, of how those theories drove the inquiry or led to the guiding research questions, of the participants' backgrounds, of the processes of data collection, of the training and limitations of the coders, along with a strong attempt to make connections between the data and the conclusions evident.

Although the Berkenkotter, Huckin, and Ackerman (1988) study does not, case study reports often include the reactions of the participants to the study or to the researchers' conclusions. Because case studies tend to be exploratory, most end with implications for further study. Here researchers may identify significant variables that emerged during the research and suggest studies related to these, or the authors may suggest further general questions that their case study generated.

For example, Emig's (1971) study concludes with a section dedicated solely to the topic of implications for further research, in which she suggests several means by which this particular study could have been improved, as well as questions and ideas raised by this study which other researchers might like to address, such as: is there a correlation between a certain personality and a certain composing process profile (e.g. is there a positive correlation between ego strength and persistence in revising)?

Also included in Emig's study is a section dedicated to implications for teaching, which outlines the pedagogical ramifications of the study's findings for teachers currently involved in high school writing programs.

Sharan Merriam (1985) also offers several suggestions for alternative presentations of data:

• Prepare specialized condensations for appropriate groups.
• Replace narrative sections with a series of answers to open-ended questions.
• Present "skimmer's" summaries at beginning of each section.
• Incorporate headlines that encapsulate information from text.
• Prepare analytic summaries with supporting data appendixes.
• Present data in colorful and/or unique graphic representations.

Issues of Validity and Reliability

Once key variables have been identified, they can be analyzed. Reliability becomes a key concern at this stage, and many case study researchers go to great lengths to ensure that their interpretations of the data will be both reliable and valid. Because issues of validity and reliability are an important part of any study in the social sciences, it is important to identify some ways of dealing with results.

Multi-modal case study researchers often balance the results of their coding with data from interviews or writer's reflections upon their own work. Consequently, the researchers' conclusions become highly contextualized. For example, in a case study which looked at the time spent in different stages of the writing process, Berkenkotter concluded that her participant, Donald Murray, spent more time planning his essays than in other writing stages. The report of this case study is followed by Murray's reply, wherein he agrees with some of Berkenkotter's conclusions and disagrees with others.

As is the case with other research methodologies, issues of external validity, construct validity, and reliability need to be carefully considered.

Commentary on Case Studies

Researchers often debate the relative merits of particular methods, among them case study. In this section, we comment on two key issues. To read the commentaries, choose any of the items below:

Strengths and Weaknesses of Case Studies

Most case study advocates point out that case studies produce much more detailed information than what is available through a statistical analysis. Advocates will also hold that while statistical methods might be able to deal with situations where behavior is homogeneous and routine, case studies are needed to deal with creativity, innovation, and context. Detractors argue that case studies are difficult to generalize because of inherent subjectivity and because they are based on qualitative subjective data, generalizable only to a particular context.

Flexibility

The case study approach is a comparatively flexible method of scientific research. Because its project designs seem to emphasize exploration rather than prescription or prediction, researchers are comparatively freer to discover and address issues as they arise in their experiments. In addition, the looser format of case studies allows researchers to begin with broad questions and narrow their focus as their experiment progresses rather than attempt to predict every possible outcome before the experiment is conducted.

Emphasis on Context

By seeking to understand as much as possible about a single subject or small group of subjects, case studies specialize in "deep data," or "thick description"--information based on particular contexts that can give research results a more human face. This emphasis can help bridge the gap between abstract research and concrete practice by allowing researchers to compare their firsthand observations with the quantitative results obtained through other methods of research.

Inherent Subjectivity

"The case study has long been stereotyped as the weak sibling among social science methods," and is often criticized as being too subjective and even pseudo-scientific. Likewise, "investigators who do case studies are often regarded as having deviated from their academic disciplines, and their investigations as having insufficient precision (that is, quantification), objectivity and rigor" (Yin 1989). Opponents cite opportunities for subjectivity in the implementation, presentation, and evaluation of case study research. The approach relies on personal interpretation of data and inferences. Results may not be generalizable, are difficult to test for validity, and rarely offer a problem-solving prescription. Simply put, relying on one or a few subjects as a basis for cognitive extrapolations runs the risk of inferring too much from what might be circumstance.

High Investment

Case studies can involve learning more about the subjects being tested than most researchers would care to know--their educational background, emotional background, perceptions of themselves and their surroundings, their likes, dislikes, and so on. Because of its emphasis on "deep data," the case study is out of reach for many large-scale research projects which look at a subject pool in the tens of thousands. A budget request of $10,000 to examine 200 subjects sounds more efficient than a similar request to examine four subjects.

Ethical Considerations

Researchers conducting case studies should consider certain ethical issues. For example, many educational case studies are often financed by people who have, either directly or indirectly, power over both those being studied and those conducting the investigation (1985). This conflict of interests can hinder the credibility of the study.

The personal integrity, sensitivity, and possible prejudices and/or biases of the investigators need to be taken into consideration as well. Personal biases can creep into how the research is conducted, alternative research methods used, and the preparation of surveys and questionnaires.

A common complaint in case study research is that investigators change direction during the course of the study unaware that their original research design was inadequate for the revised investigation. Thus, the researchers leave unknown gaps and biases in the study. To avoid this, researchers should report preliminary findings so that the likelihood of bias will be reduced.

Concerns about Reliability, Validity, and Generalizability

Merriam (1985) offers several suggestions for how case study researchers might actively combat the popular attacks on the validity, reliability, and generalizability of case studies:

• Prolong the Processes of Data Gathering on Site: This will help to insure the accuracy of the findings by providing the researcher with more concrete information upon which to formulate interpretations.
• Employ the Process of "Triangulation": Use a variety of data sources as opposed to relying solely upon one avenue of observation. One example of such a data check would be what McClintock, Brannon, and Maynard (1985) refer to as a "case cluster method," that is, when a single unit within a larger case is randomly sampled, and that data treated quantitatively." For instance, in Emig's (1971) study, the case cluster method was employed, singling out the productivity of a single student named Lynn. This cluster profile included an advanced case history of the subject, specific examination and analysis of individual compositions and protocols, and extensive interview sessions. The seven remaining students were then compared with the case of Lynn, to ascertain if there are any shared, or unique dimensions to the composing process engaged in by these eight students.
• Conduct Member Checks: Initiate and maintain an active corroboration on the interpretation of data between the researcher and those who provided the data. In other words, talk to your subjects.
• Collect Referential Materials: Complement the file of materials from the actual site with additional document support. For example, Emig (1971) supports her initial propositions with historical accounts by writers such as T.S. Eliot, James Joyce, and D.H. Lawrence. Emig also cites examples of theoretical research done with regards to the creative process, as well as examples of empirical research dealing with the writing of adolescents. Specific attention is then given to the four stages description of the composing process delineated by Helmoltz, Wallas, and Cowley, as it serves as the focal point in this study.
• Engage in Peer Consultation: Prior to composing the final draft of the report, researchers should consult with colleagues in order to establish validity through pooled judgment.

Although little can be done to combat challenges concerning the generalizability of case studies, "most writers suggest that qualitative research should be judged as credible and confirmable as opposed to valid and reliable" (Merriam 1985). Likewise, it has been argued that "rather than transplanting statistical, quantitative notions of generalizability and thus finding qualitative research inadequate, it makes more sense to develop an understanding of generalization that is congruent with the basic characteristics of qualitative inquiry" (1985). After all, criticizing the case study method for being ungeneralizable is comparable to criticizing a washing machine for not being able to tell the correct time. In other words, it is unjust to criticize a method for not being able to do something which it was never originally designed to do in the first place.

Annotated Bibliography

Armisted, C. (1984). How Useful are Case Studies. Training and Development Journal, 38 (2), 75-77.

This article looks at eight types of case studies, offers pros and cons of using case studies in the classroom, and gives suggestions for successfully writing and using case studies.

Bardovi-Harlig, K. (1997). Beyond Methods: Components of Second Language Teacher Education . New York: McGraw-Hill.

A compilation of various research essays which address issues of language teacher education. Essays included are: "Non-native reading research and theory" by Lee, "The case for Psycholinguistics" by VanPatten, and "Assessment and Second Language Teaching" by Gradman and Reed.

Bartlett, L. (1989). A Question of Good Judgment; Interpretation Theory and Qualitative Enquiry Address. 70th Annual Meeting of the American Educational Research Association. San Francisco.

Bartlett selected "quasi-historical" methodology, which focuses on the "truth" found in case records, as one that will provide "good judgments" in educational inquiry. He argues that although the method is not comprehensive, it can try to connect theory with practice.

Baydere, S. et. al. (1993). Multimedia conferencing as a tool for collaborative writing: a case study in Computer Supported Collaborative Writing. New York: Springer-Verlag.

The case study by Baydere et. al. is just one of the many essays in this book found in the series "Computer Supported Cooperative Work." Denley, Witefield and May explore similar issues in their essay, "A case study in task analysis for the design of a collaborative document production system."

Berkenkotter, C., Huckin, T., N., & Ackerman J. (1988). Conventions, Conversations, and the Writer: Case Study of a Student in a Rhetoric Ph.D. Program. Research in the Teaching of English, 22, 9-44.

The authors focused on how the writing of their subject, Nate or Ackerman, changed as he became more acquainted or familiar with his field's discourse community.

Berninger, V., W., and Gans, B., M. (1986). Language Profiles in Nonspeaking Individuals of Normal Intelligence with Severe Cerebral Palsy. Augmentative and Alternative Communication, 2, 45-50.

Argues that generalizations about language abilities in patients with severe cerebral palsy (CP) should be avoided. Standardized tests of different levels of processing oral language, of processing written language, and of producing written language were administered to 3 male participants (aged 9, 16, and 40 yrs).

Bockman, J., R., and Couture, B. (1984). The Case Method in Technical Communication: Theory and Models. Texas: Association of Teachers of Technical Writing.

Examines the study and teaching of technical writing, communication of technical information, and the case method in terms of those applications.

Boehrer, J. (1990). Teaching With Cases: Learning to Question. New Directions for Teaching and Learning, 42 41-57.

This article discusses the origins of the case method, looks at the question of what is a case, gives ideas about learning in case teaching, the purposes it can serve in the classroom, the ground rules for the case discussion, including the role of the question, and new directions for case teaching.

Bowman, W. R. (1993). Evaluating JTPA Programs for Economically Disadvantaged Adults: A Case Study of Utah and General Findings . Washington: National Commission for Employment Policy.

"To encourage state-level evaluations of JTPA, the Commission and the State of Utah co-sponsored this report on the effectiveness of JTPA Title II programs for adults in Utah. The technique used is non-experimental and the comparison group was selected from registrants with Utah's Employment Security. In a step-by-step approach, the report documents how non-experimental techniques can be applied and several specific technical issues can be addressed."

Boyce, A. (1993) The Case Study Approach for Pedagogists. Annual Meeting of the American Alliance for Health, Physical Education, Recreation and Dance. (Address). Washington DC.

This paper addresses how case studies 1) bridge the gap between teaching theory and application, 2) enable students to analyze problems and develop solutions for situations that will be encountered in the real world of teaching, and 3) helps students to evaluate the feasibility of alternatives and to understand the ramifications of a particular course of action.

Carson, J. (1993) The Case Study: Ideal Home of WAC Quantitative and Qualitative Data. Annual Meeting of the Conference on College Composition and Communication. (Address). San Diego.

"Increasingly, one of the most pressing questions for WAC advocates is how to keep [WAC] programs going in the face of numerous difficulties. Case histories offer the best chance for fashioning rhetorical arguments to keep WAC programs going because they offer the opportunity to provide a coherent narrative that contextualizes all documents and data, including what is generally considered scientific data. A case study of the WAC program, . . . at Robert Morris College in Pittsburgh demonstrates the advantages of this research method. Such studies are ideal homes for both naturalistic and positivistic data as well as both quantitative and qualitative information."

---. (1991). A Cognitive Process Theory of Writing. College Composition and Communication. 32. 365-87.

No abstract available.

Cromer, R. (1994) A Case Study of Dissociations Between Language and Cognition. Constraints on Language Acquisition: Studies of Atypical Children . Hillsdale: Lawrence Erlbaum Associates, 141-153.

Crossley, M. (1983) Case Study in Comparative and International Education: An Approach to Bridging the Theory-Practice Gap. Proceedings of the 11th Annual Conference of the Australian Comparative and International Education Society. Hamilton, NZ.

Case study research, as presented here, helps bridge the theory-practice gap in comparative and international research studies of education because it focuses on the practical, day-to-day context rather than on the national arena. The paper asserts that the case study method can be valuable at all levels of research, formation, and verification of theories in education.

Daillak, R., H., and Alkin, M., C. (1982). Qualitative Studies in Context: Reflections on the CSE Studies of Evaluation Use . California: EDRS

The report shows how the Center of the Study of Evaluation (CSE) applied qualitative techniques to a study of evaluation information use in local, Los Angeles schools. It critiques the effectiveness and the limitations of using case study, evaluation, field study, and user interview survey methodologies.

Davey, L. (1991). The Application of Case Study Evaluations. ERIC/TM Digest.

This article examines six types of case studies, the type of evaluation questions that can be answered, the functions served, some design features, and some pitfalls of the method.

Deutch, C. E. (1996). A course in research ethics for graduate students. College Teaching, 44, 2, 56-60.

This article describes a one-credit discussion course in research ethics for graduate students in biology. Case studies are focused on within the four parts of the course: 1) major issues, 2 )practical issues in scholarly work, 3) ownership of research results, and 4) training and personal decisions.

DeVoss, G. (1981). Ethics in Fieldwork Research. RIE 27p. (ERIC)

This article examines four of the ethical problems that can happen when conducting case study research: acquiring permission to do research, knowing when to stop digging, the pitfalls of doing collaborative research, and preserving the integrity of the participants.

Driscoll, A. (1985). Case Study of a Research Intervention: the University of Utah’s Collaborative Approach . San Francisco: Far West Library for Educational Research Development.

Paper presented at the annual meeting of the American Association of Colleges of Teacher Education, Denver, CO, March 1985. Offers information of in-service training, specifically case studies application.

Ellram, L. M. (1996). The Use of the Case Study Method in Logistics Research. Journal of Business Logistics, 17, 2, 93.

This article discusses the increased use of case study in business research, and the lack of understanding of when and how to use case study methodology in business.

Emig, J. (1971) The Composing Processes of Twelfth Graders . Urbana: NTCE.

This case study uses observation, tape recordings, writing samples, and school records to show that writing in reflexive and extensive situations caused different lengths of discourse and different clusterings of the components of the writing process.

Feagin, J. R. (1991). A Case For the Case Study . Chapel Hill: The University of North Carolina Press.

This book discusses the nature, characteristics, and basic methodological issues of the case study as a research method.

Feldman, H., Holland, A., & Keefe, K. (1989) Language Abilities after Left Hemisphere Brain Injury: A Case Study of Twins. Topics in Early Childhood Special Education, 9, 32-47.

"Describes the language abilities of 2 twin pairs in which 1 twin (the experimental) suffered brain injury to the left cerebral hemisphere around the time of birth and1 twin (the control) did not. One pair of twins was initially assessed at age 23 mo. and the other at about 30 mo.; they were subsequently evaluated in their homes 3 times at about 6-mo intervals."

Fidel, R. (1984). The Case Study Method: A Case Study. Library and Information Science Research, 6.

The article describes the use of case study methodology to systematically develop a model of online searching behavior in which study design is flexible, subject manner determines data gathering and analyses, and procedures adapt to the study's progressive change.

Flower, L., & Hayes, J. R. (1984). Images, Plans and Prose: The Representation of Meaning in Writing. Written Communication, 1, 120-160.

Explores the ways in which writers actually use different forms of knowing to create prose.

Frey, L. R. (1992). Interpreting Communication Research: A Case Study Approach Englewood Cliffs, N.J.: Prentice Hall.

The book discusses research methodologies in the Communication field. It focuses on how case studies bridge the gap between communication research, theory, and practice.

Gilbert, V. K. (1981). The Case Study as a Research Methodology: Difficulties and Advantages of Integrating the Positivistic, Phenomenological and Grounded Theory Approaches . The Annual Meeting of the Canadian Association for the Study of Educational Administration. (Address) Halifax, NS, Can.

This study on an innovative secondary school in England shows how a "low-profile" participant-observer case study was crucial to the initial observation, the testing of hypotheses, the interpretive approach, and the grounded theory.

Gilgun, J. F. (1994). A Case for Case Studies in Social Work Research. Social Work, 39, 4, 371-381.

This article defines case study research, presents guidelines for evaluation of case studies, and shows the relevance of case studies to social work research. It also looks at issues such as evaluation and interpretations of case studies.

Glennan, S. L., Sharp-Bittner, M. A. & Tullos, D. C. (1991). Augmentative and Alternative Communication Training with a Nonspeaking Adult: Lessons from MH. Augmentative and Alternative Communication, 7, 240-7.

"A response-guided case study documented changes in a nonspeaking 36-yr-old man's ability to communicate using 3 trained augmentative communication modes. . . . Data were collected in videotaped interaction sessions between the nonspeaking adult and a series of adult speaking."

Graves, D. (1981). An Examination of the Writing Processes of Seven Year Old Children. Research in the Teaching of English, 15, 113-134.

Hamel, J. (1993). Case Study Methods . Newbury Park: Sage. .

"In a most economical fashion, Hamel provides a practical guide for producing theoretically sharp and empirically sound sociological case studies. A central idea put forth by Hamel is that case studies must "locate the global in the local" thus making the careful selection of the research site the most critical decision in the analytic process."

Karthigesu, R. (1986, July). Television as a Tool for Nation-Building in the Third World: A Post-Colonial Pattern, Using Malaysia as a Case-Study. International Television Studies Conference. (Address). London, 10-12.

"The extent to which Television Malaysia, as a national mass media organization, has been able to play a role in nation building in the post-colonial period is . . . studied in two parts: how the choice of a model of nation building determines the character of the organization; and how the character of the organization influences the output of the organization."

Kenny, R. (1984). Making the Case for the Case Study. Journal of Curriculum Studies, 16, (1), 37-51.

The article looks at how and why the case study is justified as a viable and valuable approach to educational research and program evaluation.

Knirk, F. (1991). Case Materials: Research and Practice. Performance Improvement Quarterly, 4 (1 ), 73-81.

The article addresses the effectiveness of case studies, subject areas where case studies are commonly used, recent examples of their use, and case study design considerations.

Klos, D. (1976). Students as Case Writers. Teaching of Psychology, 3.2, 63-66.

This article reviews a course in which students gather data for an original case study of another person. The task requires the students to design the study, collect the data, write the narrative, and interpret the findings.

Leftwich, A. (1981). The Politics of Case Study: Problems of Innovation in University Education. Higher Education Review, 13.2, 38-64.

The article discusses the use of case studies as a teaching method. Emphasis is on the instructional materials, interdisciplinarity, and the complex relationships within the university that help or hinder the method.

Mabrito, M. (1991, Oct.). Electronic Mail as a Vehicle for Peer Response: Conversations of High and Low Apprehensive Writers. Written Communication, 509-32.

McCarthy, S., J. (1955). The Influence of Classroom Discourse on Student Texts: The Case of Ella . East Lansing: Institute for Research on Teaching.

A look at how students of color become marginalized within traditional classroom discourse. The essay follows the struggles of one black student: Ella.

Matsuhashi, A., ed. (1987). Writing in Real Time: Modeling Production Processes Norwood, NJ: Ablex Publishing Corporation.

Investigates how writers plan to produce discourse for different purposes to report, to generalize, and to persuade, as well as how writers plan for sentence level units of language. To learn about planning, an observational measure of pause time was used" (ERIC).

Merriam, S. B. (1985). The Case Study in Educational Research: A Review of Selected Literature. Journal of Educational Thought, 19.3, 204-17.

The article examines the characteristics of, philosophical assumptions underlying the case study, the mechanics of conducting a case study, and the concerns about the reliability, validity, and generalizability of the method.

---. (1988). Case Study Research in Education: A Qualitative Approach San Francisco: Jossey Bass.

Merry, S. E., & Milner, N. eds. (1993). The Possibility of Popular Justice: A Case Study of Community Mediation in the United States . Ann Arbor: U of Michigan.

". . . this volume presents a case study of one experiment in popular justice, the San Francisco Community Boards. This program has made an explicit claim to create an alternative justice, or new justice, in the midst of a society ordered by state law. The contributors to this volume explore the history and experience of the program and compare it to other versions of popular justice in the United States, Europe, and the Third World."

Merseth, K. K. (1991). The Case for Cases in Teacher Education. RIE. 42p. (ERIC).

This monograph argues that the case method of instruction offers unique potential for revitalizing the field of teacher education.

Michaels, S. (1987). Text and Context: A New Approach to the Study of Classroom Writing. Discourse Processes, 10, 321-346.

"This paper argues for and illustrates an approach to the study of writing that integrates ethnographic analysis of classroom interaction with linguistic analysis of written texts and teacher/student conversational exchanges. The approach is illustrated through a case study of writing in a single sixth grade classroom during a single writing assignment."

Milburn, G. (1995). Deciphering a Code or Unraveling a Riddle: A Case Study in the Application of a Humanistic Metaphor to the Reporting of Social Studies Teaching. Theory and Research in Education, 13.

This citation serves as an example of how case studies document learning procedures in a senior-level economics course.

Milley, J. E. (1979). An Investigation of Case Study as an Approach to Program Evaluation. 19th Annual Forum of the Association for Institutional Research. (Address). San Diego.

The case study method merged a narrative report focusing on the evaluator as participant-observer with document review, interview, content analysis, attitude questionnaire survey, and sociogram analysis. Milley argues that case study program evaluation has great potential for widespread use.

Minnis, J. R. (1985, Sept.). Ethnography, Case Study, Grounded Theory, and Distance Education Research. Distance Education, 6.2.

This article describes and defines the strengths and weaknesses of ethnography, case study, and grounded theory.

Nunan, D. (1992). Collaborative language learning and teaching . New York: Cambridge University Press.

Included in this series of essays is Peter Sturman’s "Team Teaching: a case study from Japan" and David Nunan’s own "Toward a collaborative approach to curriculum development: a case study."

Nystrand, M., ed. (1982). What Writers Know: The Language, Process, and Structure of Written Discourse . New York: Academic Press.

Owenby, P. H. (1992). Making Case Studies Come Alive. Training, 29, (1), 43-46. (ERIC)

This article provides tips for writing more effective case studies.

---. (1981). Pausing and Planning: The Tempo of Writer Discourse Production. Research in the Teaching of English, 15 (2),113-34.

Perl, S. (1979). The Composing Processes of Unskilled College Writers. Research in the Teaching of English, 13, 317-336.

"Summarizes a study of five unskilled college writers, focusing especially on one of the five, and discusses the findings in light of current pedagogical practice and research design."

Pilcher J. and A. Coffey. eds. (1996). Gender and Qualitative Research . Brookfield: Aldershot, Hants, England.

This book provides a series of essays which look at gender identity research, qualitative research and applications of case study to questions of gendered pedagogy.

Pirie, B. S. (1993). The Case of Morty: A Four Year Study. Gifted Education International, 9 (2), 105-109.

This case study describes a boy from kindergarten through third grade with above average intelligence but difficulty in learning to read, write, and spell.

Popkewitz, T. (1993). Changing Patterns of Power: Social Regulation and Teacher Education Reform. Albany: SUNY Press.

Popkewitz edits this series of essays that address case studies on educational change and the training of teachers. The essays vary in terms of discipline and scope. Also, several authors include case studies of educational practices in countries other than the United States.

---. (1984). The Predrafting Processes of Four High- and Four Low Apprehensive Writers. Research in the Teaching of English, 18, (1), 45-64.

Rasmussen, P. (1985, March) A Case Study on the Evaluation of Research at the Technical University of Denmark. International Journal of Institutional Management in Higher Education, 9 (1).

This is an example of a case study methodology used to evaluate the chemistry and chemical engineering departments at the University of Denmark.

Roth, K. J. (1986). Curriculum Materials, Teacher Talk, and Student Learning: Case Studies in Fifth-Grade Science Teaching . East Lansing: Institute for Research on Teaching.

Roth offers case studies on elementary teachers, elementary school teaching, science studies and teaching, and verbal learning.

Selfe, C. L. (1985). An Apprehensive Writer Composes. When a Writer Can't Write: Studies in Writer's Block and Other Composing-Process Problems . (pp. 83-95). Ed. Mike Rose. NMY: Guilford.

Smith-Lewis, M., R. and Ford, A. (1987). A User's Perspective on Augmentative Communication. Augmentative and Alternative Communication, 3, 12-7.

"During a series of in-depth interviews, a 25-yr-old woman with cerebral palsy who utilized augmentative communication reflected on the effectiveness of the devices designed for her during her school career."

St. Pierre, R., G. (1980, April). Follow Through: A Case Study in Metaevaluation Research . 64th Annual Meeting of the American Educational Research Association. (Address).

The three approaches to metaevaluation are evaluation of primary evaluations, integrative meta-analysis with combined primary evaluation results, and re-analysis of the raw data from a primary evaluation.

Stahler, T., M. (1996, Feb.) Early Field Experiences: A Model That Worked. ERIC.

"This case study of a field and theory class examines a model designed to provide meaningful field experiences for preservice teachers while remaining consistent with the instructor's beliefs about the role of teacher education in preparing teachers for the classroom."

Stake, R. E. (1995). The Art of Case Study Research. Thousand Oaks: Sage Publications.

This book examines case study research in education and case study methodology.

Stiegelbauer, S. (1984) Community, Context, and Co-curriculum: Situational Factors Influencing School Improvements in a Study of High Schools. Presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Discussion of several case studies: one looking at high school environments, another examining educational innovations.

Stolovitch, H. (1990). Case Study Method. Performance And Instruction, 29, (9), 35-37.

This article describes the case study method as a form of simulation and presents guidelines for their use in professional training situations.

Thaller, E. (1994). Bibliography for the Case Method: Using Case Studies in Teacher Education. RIE. 37 p.

This bibliography presents approximately 450 citations on the use of case studies in teacher education from 1921-1993.

Thrane, T. (1986). On Delimiting the Senses of Near-Synonyms in Historical Semantics: A Case Study of Adjectives of 'Moral Sufficiency' in the Old English Andreas. Linguistics Across Historical and Geographical Boundaries: In Honor of Jacek Fisiak on the Occasion of his Fiftieth Birthday . Berlin: Mouton de Gruyter.

United Nations. (1975). Food and Agriculture Organization. Report on the FAO/UNFPA Seminar on Methodology, Research and Country: Case Studies on Population, Employment and Productivity . Rome: United Nations.

This example case study shows how the methodology can be used in a demographic and psychographic evaluation. At the same time, it discusses the formation and instigation of the case study methodology itself.

Van Vugt, J. P., ed. (1994). Aids Prevention and Services: Community Based Research . Westport: Bergin and Garvey.

"This volume has been five years in the making. In the process, some of the policy applications called for have met with limited success, such as free needle exchange programs in a limited number of American cities, providing condoms to prison inmates, and advertisements that depict same-sex couples. Rather than dating our chapters that deal with such subjects, such policy applications are verifications of the type of research demonstrated here. Furthermore, they indicate the critical need to continue community based research in the various communities threatened by acquired immuno-deficiency syndrome (AIDS) . . . "

Welch, W., ed. (1981, May). Case Study Methodology in Educational Evaluation. Proceedings of the Minnesota Evaluation Conference. Minnesota. (Address).

The four papers in these proceedings provide a comprehensive picture of the rationale, methodology, strengths, and limitations of case studies.

Williams, G. (1987). The Case Method: An Approach to Teaching and Learning in Educational Administration. RIE, 31p.

This paper examines the viability of the case method as a teaching and learning strategy in instructional systems geared toward the training of personnel of the administration of various aspects of educational systems.

Yin, R. K. (1993). Advancing Rigorous Methodologies: A Review of 'Towards Rigor in Reviews of Multivocal Literatures.' Review of Educational Research, 61, (3).

"R. T. Ogawa and B. Malen's article does not meet its own recommended standards for rigorous testing and presentation of its own conclusions. Use of the exploratory case study to analyze multivocal literatures is not supported, and the claim of grounded theory to analyze multivocal literatures may be stronger."

---. (1989). Case Study Research: Design and Methods. London: Sage Publications Inc.

This book discusses in great detail, the entire design process of the case study, including entire chapters on collecting evidence, analyzing evidence, composing the case study report, and designing single and multiple case studies.

Related Links

Consider the following list of related Web sites for more information on the topic of case study research. Note: although many of the links cover the general category of qualitative research, all have sections that address issues of case studies.

• Sage Publications on Qualitative Methodology: Search here for a comprehensive list of new books being published about "Qualitative Methodology" http://www.sagepub.co.uk/
• The International Journal of Qualitative Studies in Education: An on-line journal "to enhance the theory and practice of qualitative research in education." On-line submissions are welcome. http://www.tandf.co.uk/journals/tf/09518398.html
• Qualitative Research Resources on the Internet: From syllabi to home pages to bibliographies. All links relate somehow to qualitative research. http://www.nova.edu/ssss/QR/qualres.html

Citation Information

Bronwyn Becker, Patrick Dawson, Karen Devine, Carla Hannum, Steve Hill, Jon Leydens, Debbie Matuskevich, Carol Traver, and Mike Palmquist. (1994-2024). Case Studies. The WAC Clearinghouse. Colorado State University. Available at https://wac.colostate.edu/repository/writing/guides/.

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What is a Case Study? [+6 Types of Case Studies]

By Ronita Mohan , Sep 20, 2021

Case studies have become powerful business tools. But what is a case study? What are the benefits of creating one? Are there limitations to the format?

If you’ve asked yourself these questions, our helpful guide will clear things up. Learn how to use a case study for business. Find out how cases analysis works in psychology and research.

We’ve also got examples of case studies to inspire you.

Haven’t made a case study before? You can easily  create a case study  with Venngage’s customizable templates.

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Click to jump ahead:

What is a case study, what is the case study method, benefits of case studies, limitations of case studies, types of case studies, faqs about case studies.

Case studies are research methodologies. They examine subjects, projects, or organizations to tell a story.

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Numerous sectors use case analyses. The social sciences, social work, and psychology create studies regularly.

Healthcare industries write reports on patients and diagnoses. Marketing case study examples , like the one below, highlight the benefits of a business product.

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Now that you know what a case study is, we explain how case reports are used in three different industries.

What is a business case study?

A business or marketing case study aims at showcasing a successful partnership. This can be between a brand and a client. Or the case study can examine a brand’s project.

There is a perception that case studies are used to advertise a brand. But effective reports, like the one below, can show clients how a brand can support them.

Hubspot created a case study on a customer that successfully scaled its business. The report outlines the various Hubspot tools used to achieve these results.

Hubspot also added a video with testimonials from the client company’s employees.

So, what is the purpose of a case study for businesses? There is a lot of competition in the corporate world. Companies are run by people. They can be on the fence about which brand to work with.

Business reports  stand out aesthetically, as well. They use  brand colors  and brand fonts . Usually, a combination of the client’s and the brand’s.

With the Venngage  My Brand Kit  feature, businesses can automatically apply their brand to designs.

A business case study, like the one below, acts as social proof. This helps customers decide between your brand and your competitors.

Don’t know how to design a report? You can learn  how to write a case study  with Venngage’s guide. We also share design tips and examples that will help you convert.

Related: 55+ Annual Report Design Templates, Inspirational Examples & Tips [Updated]

What is a case study in psychology?

In the field of psychology, case studies focus on a particular subject. Psychology case histories also examine human behaviors.

Case reports search for commonalities between humans. They are also used to prescribe further research. Or these studies can elaborate on a solution for a behavioral ailment.

The American Psychology Association  has a number of case studies on real-life clients. Note how the reports are more text-heavy than a business case study.

Famous psychologists such as Sigmund Freud and Anna O popularised the use of case studies in the field. They did so by regularly interviewing subjects. Their detailed observations build the field of psychology.

It is important to note that psychological studies must be conducted by professionals. Psychologists, psychiatrists and therapists should be the researchers in these cases.

Related: What Netflix’s Top 50 Shows Can Teach Us About Font Psychology [Infographic]

What is a case study in research?

Research is a necessary part of every case study. But specific research fields are required to create studies. These fields include user research, healthcare, education, or social work.

For example, this UX Design  report examined the public perception of a client. The brand researched and implemented new visuals to improve it. The study breaks down this research through lessons learned.

Clinical reports are a necessity in the medical field. These documents are used to share knowledge with other professionals. They also help examine new or unusual diseases or symptoms.

The pandemic has led to a significant increase in research. For example,  Spectrum Health  studied the value of health systems in the pandemic. They created the study by examining community outreach.

The pandemic has significantly impacted the field of education. This has led to numerous examinations on remote studying. There have also been studies on how students react to decreased peer communication.

Social work case reports often have a community focus. They can also examine public health responses. In certain regions, social workers study disaster responses.

You now know what case studies in various fields are. In the next step of our guide, we explain the case study method.

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A case analysis is a deep dive into a subject. To facilitate this case studies are built on interviews and observations. The below example would have been created after numerous interviews.

Case studies are largely qualitative. They analyze and describe phenomena. While some data is included, a case analysis is not quantitative.

There are a few steps in the case method. You have to start by identifying the subject of your study. Then determine what kind of research is required.

In natural sciences, case studies can take years to complete. Business reports, like this one, don’t take that long. A few weeks of interviews should be enough.

The case method will vary depending on the industry. Reports will also look different once produced.

As you will have seen, business reports are more colorful. The design is also more accessible . Healthcare and psychology reports are more text-heavy.

Designing case reports takes time and energy. So, is it worth taking the time to write them? Here are the benefits of creating case studies.

• Collects large amounts of information
• Helps formulate hypotheses
• Builds the case for further research
• Discovers new insights into a subject
• Builds brand trust and loyalty
• Engages customers through stories

For example, the business study below creates a story around a brand partnership. It makes for engaging reading. The study also shows evidence backing up the information.

We’ve shared the benefits of why studies are needed. We will also look at the limitations of creating them.

Related: How to Present a Case Study like a Pro (With Examples)

There are a few disadvantages to conducting a case analysis. The limitations will vary according to the industry.

• Responses from interviews are subjective
• Subjects may tailor responses to the researcher
• Studies can’t always be replicated
• In certain industries, analyses can take time and be expensive
• Risk of generalizing the results among a larger population

These are some of the common weaknesses of creating case reports. If you’re on the fence, look at the competition in your industry.

Other brands or professionals are building reports, like this example. In that case, you may want to do the same.

There are six common types of case reports. Depending on your industry, you might use one of these types.

Descriptive case studies

Explanatory case studies, exploratory case reports, intrinsic case studies, instrumental case studies, collective case reports.

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We go into more detail about each type of study in the guide below.

Related:  15+ Professional Case Study Examples [Design Tips + Templates]

When you have an existing hypothesis, you can design a descriptive study. This type of report starts with a description. The aim is to find connections between the subject being studied and a theory.

Once these connections are found, the study can conclude. The results of this type of study will usually suggest how to develop a theory further.

A study like the one below has concrete results. A descriptive report would use the quantitative data as a suggestion for researching the subject deeply.

When an incident occurs in a field, an explanation is required. An explanatory report investigates the cause of the event. It will include explanations for that cause.

The study will also share details about the impact of the event. In most cases, this report will use evidence to predict future occurrences. The results of explanatory reports are definitive.

Note that there is no room for interpretation here. The results are absolute.

The study below is a good example. It explains how one brand used the services of another. It concludes by showing definitive proof that the collaboration was successful.

Another example of this study would be in the automotive industry. If a vehicle fails a test, an explanatory study will examine why. The results could show that the failure was because of a particular part.

Related: How to Write a Case Study [+ Design Tips]

An explanatory report is a self-contained document. An exploratory one is only the beginning of an investigation.

Exploratory cases act as the starting point of studies. This is usually conducted as a precursor to large-scale investigations. The research is used to suggest why further investigations are needed.

An exploratory study can also be used to suggest methods for further examination.

For example, the below analysis could have found inconclusive results. In that situation, it would be the basis for an in-depth study.

Intrinsic studies are more common in the field of psychology. These reports can also be conducted in healthcare or social work.

These types of studies focus on a unique subject, such as a patient. They can sometimes study groups close to the researcher.

The aim of such studies is to understand the subject better. This requires learning their history. The researcher will also examine how they interact with their environment.

For instance, if the case study below was about a unique brand, it could be an intrinsic study.

Once the study is complete, the researcher will have developed a better understanding of a phenomenon. This phenomenon will likely not have been studied or theorized about before.

Examples of intrinsic case analysis can be found across psychology. For example, Jean Piaget’s theories on cognitive development. He established the theory from intrinsic studies into his own children.

Related: What Disney Villains Can Tell Us About Color Psychology [Infographic]

This is another type of study seen in medical and psychology fields. Instrumental reports are created to examine more than just the primary subject.

When research is conducted for an instrumental study, it is to provide the basis for a larger phenomenon. The subject matter is usually the best example of the phenomenon. This is why it is being studied.

Assume it’s examining lead generation strategies. It may want to show that visual marketing is the definitive lead generation tool. The brand can conduct an instrumental case study to examine this phenomenon.

Collective studies are based on instrumental case reports. These types of studies examine multiple reports.

There are a number of reasons why collective reports are created:

• To provide evidence for starting a new study
• To find pattens between multiple instrumental reports
• To find differences in similar types of cases
• Gain a deeper understanding of a complex phenomenon
• Understand a phenomenon from diverse contexts

A researcher could use multiple reports, like the one below, to build a collective case report.

Related: 10+ Case Study Infographic Templates That Convert

What makes a case study a case study?

A case study has a very particular research methodology. They are an in-depth study of a person or a group of individuals. They can also study a community or an organization. Case reports examine real-world phenomena within a set context.

How long should a case study be?

The length of studies depends on the industry. It also depends on the story you’re telling. Most case studies should be at least 500-1500 words long. But you can increase the length if you have more details to share.

What should you ask in a case study?

The one thing you shouldn’t ask is ‘yes’ or ‘no’ questions. Case studies are qualitative. These questions won’t give you the information you need.

Ask your client about the problems they faced. Ask them about solutions they found. Or what they think is the ideal solution. Leave room to ask them follow-up questions. This will help build out the study.

How to present a case study?

When you’re ready to present a case study, begin by providing a summary of the problem or challenge you were addressing. Follow this with an outline of the solution you implemented, and support this with the results you achieved, backed by relevant data. Incorporate visual aids like slides, graphs, and images to make your case study presentation more engaging and impactful.

Now you know what a case study means, you can begin creating one. These reports are a great tool for analyzing brands. They are also useful in a variety of other fields.

Use a visual communication platform like Venngage to design case studies. With Venngage’s templates, you can design easily. Create branded, engaging reports, all without design experience.

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What is a case study?

A case study is a type of research method. In case studies, the unit of analysis is a case . The case typically provides a detailed account of a situation that usually focuses on a conflict or complexity that one might encounter in the workplace.

• Case studies help explain the process by which a unit (a person, department, business, organization, industry, country, etc.) deals with the issue or problem confronting it, and offers possible solutions that can be applied to other units facing similar situations.
• The information presented in case studies is usually qualitative in nature - gathered through methods such as interview, observation, and document collection.
• There are different types of case study, including  intrinsic, instrumental, naturalistic,  and  pragmatic.

This research guide will assist you in finding individual case studies, as well as providing information on designing case studies. If you need assistance locating information, please Ask a Librarian .

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A Quick Guide to Case Study with Examples

Published by Alvin Nicolas at August 14th, 2021 , Revised On August 29, 2023

A case study is a documented history and detailed analysis of a situation concerning organisations, industries, and markets.

A case study:

• Focuses on discovering new facts of the situation under observation.
• Includes data collection from multiple sources over time.
• Widely used in social sciences to study the underlying information, organisation, community, or event.
• It does not provide any solution to the problem .

When to Use Case Study? 

You can use a case study in your research when:

• The focus of your study is to find answers to how and why questions .
• You don’t have enough time to conduct extensive research; case studies are convenient for completing your project successfully.
• You want to analyse real-world problems in-depth, then you can use the method of the case study.

You can consider a single case to gain in-depth knowledge about the subject, or you can choose multiple cases to know about various aspects of your  research problem .

What are the Aims of the Case Study?

• The case study aims at identifying weak areas that can be improved.
• This method is often used for idiographic research (focuses on individual cases or events).
• Another aim of the case study is nomothetic research (aims to discover new theories through data analysis of multiple cases).

Types of Case Studies

There are different types of case studies that can be categorised based on the purpose of the investigation.

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How to Conduct a Case Study?

• Select the Case to Investigate
• Formulate the Research Question
• Review of Literature
• Choose the Precise Case to Use in your Study
• Select Data Collection and Analysis Techniques
• Collect the Data
• Analyse the Data
• Prepare the Report

Step1: Select the Case to Investigate

The first step is to select a case to conduct your investigation. You should remember the following points.

• Make sure that you perform the study in the available timeframe.
• There should not be too much information available about the organisation.
• You should be able to get access to the organisation.
• There should be enough information available about the subject to conduct further research.

Step2: Formulate the Research Question

It’s necessary to  formulate a research question  to proceed with your case study. Most of the research questions begin with  how, why, what, or what can . 

You can also use a research statement instead of a research question to conduct your research which can be conditional or non-conditional. 

Step 3: Review of Literature

Once you formulate your research statement or question, you need to extensively  review the documentation about the existing discoveries related to your research question or statement.

Step 4: Choose the Precise Case to Use in your Study

You need to select a specific case or multiple cases related to your research. It would help if you treated each case individually while using multiple cases. The outcomes of each case can be used as contributors to the outcomes of the entire study.  You can select the following cases. 

• Representing various geographic regions
• Cases with various size parameters
• Explaining the existing theories or assumptions
• Leading to discoveries
• Providing a base for future research.

Step 5: Select Data Collection and Analysis Techniques

You can choose both  qualitative or quantitative approaches  for  collecting the data . You can use  interviews ,  surveys , artifacts, documentation, newspapers, and photographs, etc. To avoid biased observation, you can triangulate  your research to provide different views of your case. Even if you are focusing on a single case, you need to observe various case angles. It would help if you constructed validity, internal and external validity, as well as reliability.

Example: Identifying the impacts of contaminated water on people’s health and the factors responsible for it. You need to gather the data using qualitative and quantitative approaches to understand the case in such cases.

Construct validity:  You should select the most suitable measurement tool for your research. 

Internal validity:   You should use various methodological tools to  triangulate  the data. Try different methods to study the same hypothesis.

External validity:  You need to effectively apply the data beyond the case’s circumstances to more general issues.

Reliability:   You need to be confident enough to formulate the new direction for future studies based on your findings.

Also Read:  Reliability and Validity

Step 6: Collect the Data

Beware of the following when collecting data:

• Information should be gathered systematically, and the collected evidence from various sources should contribute to your research objectives.
• Don’t collect your data randomly.
• Recheck your research questions to avoid mistakes.
• You should save the collected data in any popular format for clear understanding.
• While making any changes to collecting information, make sure to record the changes in a document.
• You should maintain a case diary and note your opinions and thoughts evolved throughout the study.

Step 7: Analyse the Data

The research data identifies the relationship between the objects of study and the research questions or statements. You need to reconfirm the collected information and tabulate it correctly for better understanding. 

Step 8: Prepare the Report

It’s essential to prepare a report for your case study. You can write your case study in the form of a scientific paper or thesis discussing its detail with supporting evidence. 

A case study can be represented by incorporating  quotations,  stories, anecdotes,  interview transcripts , etc., with empirical data in the result section. 

You can also write it in narrative styles using  textual analysis  or   discourse analysis . Your report should also include evidence from published literature, and you can put it in the discussion section.

Advantages and Disadvantages of Case Study

Frequently asked questions, what is the case study.

A case study is a research method where a specific instance, event, or situation is deeply examined to gain insights into real-world complexities. It involves detailed analysis of context, data, and variables to understand patterns, causes, and effects, often used in various disciplines for in-depth exploration.

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Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Case study research and causal inference

Judith green.

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

Benjamin Hanckel

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

Mark Petticrew

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

Sara Paparini

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

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

Associated Data

Not applicable; no new data generated in this study.

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

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

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

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

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

Cases and causes

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

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

What kinds of causal evidence do case studies provide?

Actors’ accounts of causality.

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

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

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

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

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

Demonstrations of causal relationships

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

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

Mechanisms: how causal relationships operate

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

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

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

Conditions under which causal mechanisms operate

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

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

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

Complex causality

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

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

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

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

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

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

Conclusions

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

Acknowledgements

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

Abbreviations

Authors’ contributions.

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

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

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

Availability of data and materials

Declarations.

Not applicable.

The authors declare that they have no competing interests.

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Research Article

Laughter and effective presidential leadership: A case study of Ronald Reagan as the ‘great communicator’

Roles Conceptualization, Data curation, Formal analysis, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (PAS); [email protected] (CS)

Affiliation Department of Political Science, University of Arkansas, Fayetteville, AR, United States of America

Roles Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Political Science, George Washington University, Washington, DC, United States of America

Roles Conceptualization, Formal analysis, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Psychology, Aston University, Birmingham, United Kingdom

• Patrick A. Stewart, 
• Reagan G. Dye, 
• Carl Senior

• Published: April 17, 2024
• https://doi.org/10.1371/journal.pone.0301324
• Reader Comments

Former United States President Ronald Reagan’s use of media and his charismatic connection with viewers earned him the moniker “the great communicator”. One aspect of his charisma, the influence of elicited laughter, during a highly critical 5-minute news story by CBS reporter Leslie Stahl during the 1984 US presidential election is examined here. Two experiments examining the effects of audience laughter on perceptions of charismatic leadership are reported. In the first experiment the effects of audience laughter in response to Reagan’s comments were investigated. Here, Reagan’s perceived warmth as an effective leader significantly diminished when strong laughter is removed, whereas perceptions of competence remained unaffected. The second study carried out on an older cohort replicated and extended the first in a pre-registered design by considering the perception of trait charisma. Here, the presence or absence of audience laughter did not affect judgements of charisma. Additionally, the affective response before, and then after, the presentation of the news story was measured. Emotions associated with a positive appraisal all decreased after being shown the news story while emotions associated negative appraisal all increased. However, only participant anger was significantly increased when audience laughter was removed. Taken together the findings of both studies converge on the fact that subtle changes in media presentation of political leaders can have a significant effect on viewers. The findings show that even after 40 years in office the social psychological effects of presidential charisma can still influence observers.

Citation: Stewart PA, Dye RG, Senior C (2024) Laughter and effective presidential leadership: A case study of Ronald Reagan as the ‘great communicator’. PLoS ONE 19(4): e0301324. https://doi.org/10.1371/journal.pone.0301324

Editor: Hans H. Tung, National Taiwan University, TAIWAN

Received: April 24, 2023; Accepted: March 14, 2024; Published: April 17, 2024

Copyright: © 2024 Stewart et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data for study 1 are publicly accessible by contacting the authors. Study 2 is a preregistered replication and is available here ( https://osf.io/cq5d8/?view_only=fdab9c5c07f94ea0b9c6d01f706121f5 ).

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

One of the most important areas of study regarding politics and social psychology considers how social behaviours affect political interaction and, more specifically, how nonverbal signals influence perceptions of political leaders, especially as presented in television news. With the introduction of high-definition portrayals and ubiquitous hand-held devices, the role of the visual media in the portrayal of political leaders has grown. Experimental research on the visual primacy effect has also demonstrated that when there is conflicting information between the verbal and nonverbal channels in an audio-visual presentation, viewers have difficulty processing the verbal attributes of television news reports and remember the visuals with far more fidelity [ 1 ]. More attention is also paid to affectively important nonverbal communication when the nonverbal attributes of televised leader displays appear inappropriately matched to the rhetorical context [ 2 – 4 ].

The majority of research concerning the influence of nonverbal leader communication on social perception focuses on how visual attributes of posture, body movements, and facial display behaviours affect viewer perceptions and trait attributions [ 5 ]. At the same time, there is a small but growing literature that considers the role played by audible signals. Specifically, research considering the influence of the observable audience response to political figures suggests there is a significant intra-audience effect of emotional and evaluative signalling on other audience members while watching mediated events [ 6 ]. Applause-cheering, laughter, booing, chanting and combinations of these audible signals significantly influence how audience members view the televised political event or news coverage [ 6 ]. Viewers may unknowingly monitor and respond to the expressed intensity and type of follower utterance in support or opposition to the speaker and their stated political positions [ 7 ]. Media audiences, whether streaming debates, watching on television, or viewing through other media platforms, and perhaps more crucially, journalists who may be reporting on the event, may likewise be influenced by information conveyed via this audible channel. In other words, the social influence asserted through a specific audience response is similar to emotional contagion effects [ 6 ] and can affect viewer and listener perceptions with or without express awareness. However, the specific influence of different audience responses such as applause-cheering, laughter, and booing has yet to be studied in depth.

Despite the influence that observable audience responses may have on perceptions of leaders, systematic evaluation of these behaviours to political figures and how they affect the efficacy of politician narratives is limited. The few studies providing insight into the social influence of audience behaviour on political figures and policy issues tend to incorporate both audible and visually observable responses. Wiegman’s field experiment involved a videotaped studio audience either reacting positively, negatively, or neutrally to a well-known Dutch politician through audible utterances and different visible nonverbal behaviours [ 8 ]. Likewise, Fein and colleagues’ experiments considering Ronald Reagan’s second 1984 presidential debate performance did not differentiate between applause and laughter nor the moderator’s verbal and nonverbal response [ 9 ]. A study by Axsom and colleagues considering the verbal channel alone with regards to specific policy issues (e.g., imprisonment/probation) provided for comparison of “enthusiastic applause-cheering” to unenthusiastic and polite applause with occasional derisive cries [ 10 ] and found a tendency towards a simple consensus heuristic to make social judgments. Thus, while the limited research regarding political candidates and issues provides useful insights, it does not differentiate between different observable audience response types and often conflates visual and audible stimuli. In the work that follows we focus on one form of observable audience response–laughter–considering first its evolutionary roots and social influence briefly before focusing on its presence in politics and the types of humour that might elicit this type of behavioural response. We then evaluate group laughter’s role in providing a heuristic by which individuals may evaluate a political figure in ambiguous situations.

Laughter has been studied extensively across a broad range of social contexts with a wide range of approaches and techniques. Indeed, it is one of the few positive emotions considered in great detail, likely due to the social and survival benefits it confers. Across such species as canines [ 11 ], rats [ 12 ], and multiple primate species [ 13 ]–including humans–laughter signals playfulness, and with it benign intent [ 14 ]. In other words, social animals are more likely to cooperate and learn when in a playful state of mind as signalled by laughter.

Within humans, laughter emerges early and is seen across different cultures. Spontaneous laughter is observed in infants as young as 17 to 26 days [ 15 ], well before socially stimulated laughter occurs at three-to-four months of age [ 16 ]. Laughter is also observed within individuals who are blind since birth suggesting a possible adaptive function in social bonding [ 17 ].

The study of laughter is thus rightfully situated as a social phenomenon and would benefit from application of multiple different types of inquiry techniques; despite this, the primary method for analysing laughter has been naturalistic observational studies. Here, the effects of laughter tend to be studied in its social ecology. For instance, the ground-breaking work of Provine and colleagues took a “side-walk scientist” approach to laughter, finding that its role as a social lubricant by which mutual conversational grooming occurred was underappreciated, whereas its role as response to humorous comments was over-stated [ 16 ].

Laughter can be seen as socially influential due in large part to it being reliably identified through audible and visual characteristics. When nonverbal signals are easily and accurately identified, the more they are likely to affect perceptions and behaviour by being part of a highly learned (near automatic) repertoire of behaviours and responses that are likely to have been evolutionarily selected for survival purposes [ 18 ]. Thus, accurate recognition of the emotional state and behavioural intent of communicators provides relevant social information that influences perceptions and evaluations of others [ 19 ].

Reliable indicators of emotion may be defined as first, leading to an accurate recognition of the emotional state of the communicator, along with their resultant behavioural intent (e.g., bonding), and second, providing an index of the sender’s underlying state as one that is costly to produce [ 20 ]. Such signals are emotionally costly to produce due to their communicating underlying physiological states potentiating specific behaviour; furthermore, even when such signals are faked, physiological change can and does occur through the posing or acting out of these display behaviours.

Laughter may be classified as a costly, and hence reliable, signal when evoked or when it is difficult to control; even when faked, the initially emitted laughter leads to physiological change [ 21 ]. Individual laughter likewise serves as a social emollient by affecting perceiver mood states by dampening negative affect, increasing positive affect (and pain tolerance), while increasing social cooperation and group identity [ 22 ]. Laughter thus serves as a highly reliable social signal regarding behavioural intent [ 14 ].

Laughter across differing social contexts

Because laughter provides a mechanism for the facilitation of affiliative social interactions that go beyond physical contact and is inclusive of large numbers of individuals, it should be easily and accurately recognized to indicate the underlying behavioural intent of the senders. Socially important utterances, such as laughter, can be seen as stereotyped activities by having coherent and identifiable vocalic, facial and even postural displays reliably associated with them. As pointed out by Gaspar and colleagues, the multimodal nature of this affiliative display behaviour, together with its early emergence in ontogenetic development and its stability throughout an individual’s lifespan, make it a predictable and reliable signal even as context changes [ 13 ]. Thus, due to the important role it plays in facilitating extended social interaction laughter may be one of the most reliable of nonverbal signals [ 23 ].

That is not to say that laughter cannot function in varying contexts, or convey differing or nuanced information, rather, that it is reliably recognized across cultures. Research regarding laughter at the individual level focuses on the role of such expressiveness being a pervasive social signal during interpersonal interactions. Here, laughter may serve to punctuate speech and indicate turn-taking and transitions within conversations [ 24 , 25 ].

Laughter by individuals indicates social intent through the conveyance of vocalic qualities. Voiced laughter, with its sing-song characteristics, can communicate the experience of amusement, contempt, and even schadenfreude [ 26 , 27 ]. Unvoiced laughter on the other hand, with its gruntlike characteristics [ 28 ], can be seen as signalling more competitive intent by being connected with aggressive statements [ 29 ]. This is perhaps due to the interrelationship between vocalic and facial movements seen with laughter and the amusement smile; facial display behaviour immediately after laughter-eliciting comments help convey social intent by punctuating the preceding statement [ 30 ]. In summary, laughter at the individual level serves a multitude of social functions based upon reliable multi-modal nonverbal signalling that is easily recognized. At the same time, the nuanced expression of individual laughter allows for subtle differences in to be conveyed in its meaning.

Intra-audience effects of audience laughter

Laughter, as an important communicative signal, should also be socially contagious, or at least mimicked, to allow for the cohesion, broadening, and building of groups. Hatfield and colleagues [ 31 ] define social contagion as the “tendency to automatically mimic and synchronize facial expressions, vocalizations, postures, and movements with those of another person’s and, consequently, to converge emotionally” (p. 169). Thus, laughter would meet the definition of a socially contagious behaviour and indeed might provide the modal behaviour by meeting each of the above criteria in the convergence of mimicry and emotional response [ 13 ]. While group laughter is readily identifiable and distinct from other types of audience responses, it does not appear to have distinguishable characteristics that allow for the differentiation of members of different social groups from each other [ 32 ], nor in identifying nuanced social intent, as is the case with individual laughter.

Experimental research considering how individuals respond to group laughter tends to focus mainly on perceptions of how funny a stimulus is, whether visually with cartoons and written jokes [ 33 – 37 ], audio tapes of jokes, funny stories and stand-up routines [ 38 – 46 ], bloopers [ 47 , 48 ], or scenes from television shows and movies [ 49 – 52 ]. When the source of the humour is taken into account, findings show that group laughter leads to individuals within the group being perceived more favourably across multiple dimensions relevant to leadership, including potential for success [ 45 ], authoritativeness, character, dynamism and interestingness [ 41 ], and credibility, likability, and lowered aggressiveness [ 53 ].

While each of the above studies were influenced by multiple factors, Vraga and colleagues [ 53 ] incisively comment that, “a humorous cue might be more important when faced with a more ambiguous context… as people have substantially less information on which to rely” (p. 145). Much of this research focuses on entertainment figures in which preconceptions either do not play a role due to low awareness, or by being so heterogeneous as to be randomly distributed. Political figures are different. Not only does their humour play a role in audience response, their group membership and social status predisposes perceptions [ 45 ]. Politicians, through their leadership role in society, belong to a clearly demarcated social group that is defined by a more restrictive set of social rules. Thus, the effects of receiving and perceiving laughter within a political context could be manifestly greater than in a non-political context, where it is expected and therefore part of the routine dialogue.

Political laughter

When one considers group level behaviour in political contexts, research regarding observable audience responses tends to focus on the target and intent of verbal statements rather than the social influence process that laughter facilitates [ 30 , 54 , 55 ]. Current analyses describe audience response to political figures, by considering the length, strength, and intensity of audience laughter during political events [ 6 , 56 , 57 ]; however, the results reflect descriptive and correlational findings regarding response to individual speakers rather than group-related outcomes.

In group interactions, laughter is arguably more stereotyped and easily identified than other types of observable audience responses. The vocalic utterances that constitute laughter are much shorter in duration than applause-cheering, for instance. Analysis shows that group laughter in political contexts lasts on average 1–3 seconds in comparison with 2–8 seconds for applause-cheering [ 56 – 59 ]. Booing, another form of observable audience response, is surprisingly rare in political contexts. Interestingly, when an audience shows their appreciation for a humourous comment, applause-cheering prolongs the laughing response [ 30 , 59 ]. This points to high levels of social mimicry in the case of group laughter, and then likely social contagiousness through its continuation via applause.

Studies regarding the use of humour during US presidential primary debates in 2008 [ 30 ] and the 2016 general election presidential debates [ 6 , 56 , 57 ] suggest that the main targets of humour during electoral campaigns tend to be out-group members. Here, ridicule and other forms of disparagement humour are used as a form of political rhetoric. In addition, self-deprecatory humour, where speakers poke fun at themselves or other in-group members, also occurs with regularity. The use of these different types of humour, ridicule and self-deprecation, likely holds strategic value, as ridicule can be used to derogate the competition or set normative boundaries on behaviour. On the other hand, self-deprecatory humour is useful for making a candidate more likable [ 60 ].

While there is an emerging body of research examining the type of humour employed by political candidates and the strength and duration of the laughter response, the correlational nature of this work limits the kinds of inferences that may be drawn. Furthermore, failed humour–which may be defined by the absence of laughter, its muted presence, or even booing–is rarely studied due to the difficulty of identifying enough occurrences for analysis [ 61 ].

The experimental research discussed in the previous section suggests that audience laughter certainly affects perceptions of humorousness and trait evaluations of the speaker. A number of scholars have shown that audience responses affect perceptions of political candidates [ 6 , 8 , 9 ]; however, the question remains as to how robust a role laughter, and the eliciting humour, plays in perceptions of political figures.

This question may be elaborated by considering what leadership traits are influenced by group laughter—and in what direction. The perception of competence and warmth are considered central to the identification and choice of leaders [ 62 – 66 ]. At the same time, these traits may be moderated or mediated by perceptions of leader charisma [ 67 – 72 ]. In the present study the effects of the observable audience response of laughter on the perception of trait charisma is examined by considering an individual considered to be amongst the most charismatic of presidents in United States history, Ronald Reagan.

Humour types and political laughter

When humour in conjunction with laughter has been experimentally studied, the stimuli has tended to have been presented to the participants in the written form [ 73 ]. In other words, vignette studies varying the type of humour used, in combination with the asserted presence of laughter (or its absence), indicates success or lack thereof. As observed by Bitterly, Brooks, and Schweitzer in their extensive analysis of the effect of humour on interpersonal status [ 73 ]:

Though humor can boost status , using humor is risky . Humor attempts can fail in several ways : by being too boring (i . e ., not funny) , too bold (i . e ., inappropriate) , or failing to elicit laughter from the audience . How the audience reacts profoundly influences perceptions . If the audience does not laugh , observers are less likely to view the humor attempt as appropriate or funny , and the joke teller may lose status . (p. 17)

While the work of Bitterly and colleagues’ is indeed informative, their use of written vignettes as experimental treatments limits generalizability. Likewise, their focus on inappropriate humour relied upon sexually-charged quips; while important for the workplace with mixed sexes and fluid power dynamics, this type of humour is not used much by politicians in our technologically mediated era [ 30 , 74 ]. Indeed, the use of sexualized humour in today’s political climate would probably be unsuccessful in eliciting laughter but would also likely alienate a substantial proportion of the electorate.

Regardless, their focus on perceptions of competence and status in response to humour–and the laughter that it elicits–is applicable to contests for leadership within politics. This is especially the case in viewer observations regarding leader competence, which in addition to perceptions of prestige is key to understanding why followers defer to, and confer status on, potential leaders.

Existing research in the use of humour by political figures suggest that it is used to either attack opponents, often through ridicule, or make light of oneself or allies [ 30 , 56 , 75 , 76 ]. Smith and Powell found in the case of other- and self-disparaging humour by group leaders that those making ridicule attempts directed downwards at lower status group members were perceived as less effective, less encouraging, less helpful, and less socially attractive than those using self-directed humour [ 77 ]. However, this investigation also showed that not using humour was perceived as leading to better outcomes; in almost all leadership-based attributions that were considered save for tension relief and opinion offering, leaders who did not attempt any humorous remarks were perceived in a more positive light.

Arguably, the key factor here is the presence or absence of the laughter that is recognized to be an observable audience response to the politician. In the case of other-deprecatory humour, ridicule may increase perceived competence by virtue of martialling an audience together in their response to a tangible target; likewise, failure would see its reduction, negatively affecting the joke-teller. On the other hand, self-deprecatory humour successfully eliciting audience laughter would presumably lead to greater perceptions of warmth and communication effectiveness for the joke-teller [ 77 ]. Ultimately, observable (here audible) support for specific leader comments helps followers to identify leadership potential and other related traits.

Ronald Reagan’s leadership style

Former US President Ronald Reagan’s moniker as “The Great Communicator” inspired a large body of literature assessing his communication style and its effects on public perceptions and the expectations of the American presidency [ 78 ]. As the first “celebrity” politician, Reagan provides insight into the role of media notoriety in politics. Consequently, re-examining Reagan’s relationship with the press and his ability to manipulate public perception is relevant in the current American political climate. The return of the celebrity presidency with the ascension of Donald Trump further warrants an historical examination of Reagan to glean insight into his unique communication style and public perception of populist leaders.

Reagan’s leadership style developed from his natural ability to connect with audiences and years of experience as a recognized film actor and television personality [ 79 ]. Upon entering national politics, Reagan was successful in enjoining his conservative agenda with the Republican Party establishment, garnering successful victories in the 1980 and 1984 presidential elections, and passing supply side economic policies. Although he suffered from periods of public scrutiny during his time in office, he was known as the “Teflon president” for his ability to rebound from criticism and controversy and gainfully employed rhetorical strategies to develop a reputation as humorous, charismatic, and likeable [ 75 ]. Now some 30 years since the Reagan era, the study of Reagan’s communication and leadership style has much to offer our current understanding of the normative behaviour of presidents and candidates operating under conditions of constant media scrutiny. Whereas Reagan was adept at connecting with Americans through television, contemporary office holders (and presidential hopefuls) must be able to compete with the flood of media choices now available across numerous platforms [ 80 ] and the fast pace of the issue-attention cycle.

Now more than ever, Converse’s assertion that the public pays more attention to, and takes cues from individuals in politics, rather than to politics and policy making itself is apparent in the individual-centred nature of the contemporary political environment [ 81 ]. If politicians possess the capacity to control the political agenda and how they are perceived by voters, then they have the ability to “go public” without relying on the mass media to set the agenda [ 82 ]. The specific case of Leslie Stahl’s mini-documentary on Reagan from the 1984 campaign is exemplary of this ability to skirt around the media narrative and control perceptions simply through imagery and audience response. Reagan’s mastery of image management in relation to television, including the use of self-deprecatory humour and direct appeals to supporters, provides a blueprint for understanding how presidential contenders must operate to maximize effectiveness in today’s hybrid media era [ 83 ].

The case study approach employed provides a historically relevant example that is recognized by many political communication scholars as a turning point in how nonverbal behaviour and social signals are considered [ 84 , 85 ], it also presents an emotionally evocative stimuli that better reflects the “real world” of media consumption. Here, we test specific hypotheses concerning the influence of the observable audience response of laughter, leadership traits, and also perceived charisma. Reagan’s ability to elicit audience laughter sets up following hypothesis that are addressed in two studies:

• H1: Laughter in response to Reagan’s humorous comments will increase perceptions of his competence, warmth, and charisma.

Furthermore, due to Ronald Reagan’s effective and prolific use of a range of humour types with strategic intent, we can further test the effect of successful and unsuccessful humour, as marked by the presence of absence of laughter. Specifically, the literature reviewed suggests differential impact of Reagan’s use of self-deprecatory and ridicule humour.

• H2: Laughter in response to Reagan’s ridicule of audience members will increase perceptions of his competence.
• H3: Laughter in response to Reagan’s self-deprecatory comments will increase perceptions of his warmth.
• H4: Laughter in response to Reagan’s humour will increase perceptions of his charisma.

The perception of audience laughter to Reagan’s humour will increase judgments of his leadership competence and approachability. However, this will be dependent on whether the humour is self-depreciatory or directed to other parties. Thus, there will be a main effect of humour on judgments of leadership traits and an interaction between the different types of humour that Reagan displays.

Content coding of the Reagan-Stahl News Story (1984)

The key news story was shown on Thursday, October 4, 1984 via a CBS network primetime television news broadcast, one month before Reagan’s landslide election victory. The news story as analysed had a video clip length of five minutes and forty-five seconds (5:44.85/100s; 345 seconds) with the story length after the introduction by Dan Rather being five minutes and twelve seconds. In the five-minute (306 seconds) news story, Leslie Stahl narrated for just over three minutes (194 seconds), while Reagan had twenty-seven seconds of speaking time dispersed throughout five sound bites. These sound bites all took place during the second half of the news story.

Throughout the news story, two minutes and five seconds of audience applause cheering, laughter, and mixed response could be heard. Applause-cheering can be heard throughout almost two minutes of the story (111 seconds). This is notable because support from partisans in the form of audible responses took place in over one-third of a purportedly critical news-story. While Stahl talked over much of the applause-cheering and mixed applause-cheering and booing, laughter was presented without interruption. Indeed, of Reagan’s five sound bites, three were presented with elicited laughter uninterrupted. The first of these laughter events started at two minutes and forty-five seconds into Stahl’s story, whereas the last occurred just under 2 minutes (114 s) from the end. This news story contained a range of examples of Reagan’s performative style and is thus an ideal means to study the effects of the different types of humour used and the interaction between observable audience responses.

While the placement of the humorous comments did not give Reagan the first or final word in the story, these three laughter-eliciting comments provided him with punctuated support from the audience when he did talk. ANVIL content coding software was used to characterize and analyse the news story [ 86 ]. ANVIL allows for frame-by-frame analysis of speaking time and the ability to disambiguate the observable audience responses by considering both audible response by the audience [ 59 ], and camera shots of the audience [ 85 ]. Adobe Premier Pro software was then used to edit the video and develop the various experimental conditions.

A content analytic approach was applied to the visual coding of the key news report [ 85 ]. Specifically, the presence of large (16 of 59 camera shots; 80s and 26.2% of camera time) and approving audiences (15/59 shots; 62.12s and 20.3% of camera time) were coded. When the audible response by the audience is considered in tandem with these types of camera shots, it is found that large, yet non-responsive, audiences were presented in three shots (19.72s), whereas thirteen shots and just over a minute of applause-cheering (60.28s) was heard from large audiences. Audiences seen as approving were evident in fourteen shots for just under one minute (55.52s) where applause-cheering occurred, while laughter was seen in one nearly seven second shot (6.60s). It was expected that the applause-cheering would be most likely observed in media coverage of group settings such as political speeches [ 75 , 76 ] and intra-party debates [ 87 ]. This is due to such observable audience responses predominating in political discourse because of the ease with which candidates can evoke it among supporters in partisan settings. As a result, applause-cheering plays a role as an important barometer of a politicians’ individual appeal during speeches [ 76 , 88 ] or when in direct competition with other candidates during debates [ 59 ]. However, the production decision to incorporate applause-cheering as a major part of a critical news story may be seen as at odds with the perceived intent. So too was the decision to incorporate laughter in response to humorous comments by the then presidential candidate Ronald Reagan.

The objective of the first study is to examine the effects of the observable audience response of laughter and how it moderated the perception of Reagan as an effective presidential leader. It can be expected from the literature reviewed that audience laughter in response to Reagan’s humorous comments will affect perceptions of the leadership traits he holds, whether warmth or competence. The question is, to what extent will the presence or absence of laughter, indicating success or failure of Reagan’s humorous comments differentially affect perceptions of these traits.

While Stahl spoke over the great majority of applause, the first three of Reagan’s sound bites led to observable audience laughter in response to his quips. These occurrences were not spoken over and ensued during a middle portion of the story where Stahl commented on Reagan by stating, “This tight control has baffled those who think that Mr. Reagan is at his very best when he is spontaneous….” With this in mind, three edits totalling just under six seconds (5. 46/100s = 2. 93 +. 83 +1. 7 ) were made. The video was presented in a between subjects design with three different conditions. The first, presented unedited video as the control condition, with participants seeing what viewers of the CBS news story viewed in 1984. The second two treatments involved either the audience laughter being removed completely, with no noise from the video during the edits, or the audience laughter being faded-out to a level at just under fifty percent of that presented during the original news story. As a result, the treatment effect being considered equals 0.016% of audio-visual time (5.46s/344.83) for the total video.

The first edit took place at 3:19:02 (until 3:21:25) of the video clip after Reagan was shown commenting “I’ll raise his taxes” in response to audience members shown as heckling him. The audience, presumably at a campaign speech held during the Missouri State Fair (based upon the scene prior) responded with loud laughter followed by mixed cheering and applause. At the same time Reagan, shown with his suit jacket off in front of hay bales, displayed a smile of amusement after delivering his punchline and during the audience’s laughter and applause-cheering.

The second edit, of less than a second (3:26:29–3:27:21), took place after Stahl commented positively on Reagan’s ability for “tossing off one-liners,” Here Reagan, dressed in a suit and tie and presumably sitting down for an interview, quipped “I never get good reviews from TASS” after shaking his head, presumably to a difficult question. As a small group of individuals laughed at his response Reagan smiled in amusement.

The third edit (3:41:26–3:43:03) was set up by Stahl as Reagan being “masterful at deflecting a hostile question” when he responded to a reporter at a press conference commenting on his keeping Republican Party representatives in line. Here Reagan responded with a self-deprecatory comment, “How can you say that about a sweet fellow like me?” and laughed while displaying a smile of amusement.

Participants

Participants were recruited from introductory-level political science classes and were provided extra course credit for taking part in the study. Written consent to participate was obtained prior to taking part. A total of 317 participants took part in the study that lasted from March 2 to April 28, 2018. So as to ensure task compliance, those individuals who stopped engaging within the first 7 minutes and who did not respond to the open-ended prompt “(P)lease list some of the thoughts you had while watching the video clip” were removed from subsequent analysis, which resulted in a final sample of 283 participants. All procedures were approved by the University of Arkansas IRB.

Of these participants, 61.8% identified as female, 81.3% identified as Caucasian (with 5.3% African-American, 2.1% Asian, 8.5% Hispanic, .4% Native American, and 2.5% other ethnicity), and the average age was twenty-one years old (range 18–71, SD = 4.55). The majority of participants identified themselves as identifying with the Republican Party (40.3%), followed by Democratic Party identifiers (35.3%), as independent (15.2%), Libertarian Party (6.7%), Green Party (.7%) and other (1.8%). Random assignment of participants to the different treatments was balanced (unedited video/laughter-in/control [ n = 96], laughter faded out [ n = 95], and laughter removed [ n = 92]) across the three conditions. When tested for randomness in assignment to the treatment condition, we found no statistical bias (all p-values = ns) for sex, ethnicity, age, party identification, and political ideology (social, economic, overall conservative-liberal).

Prior to the taking part in the protocol, participants were asked to provide basic demographic information (age, sex, ethnicity), whether they were registered to vote, the political party they best identify with and their attitudes towards the main US political parties, as well as their own political ideology. At this point, participants were randomly assigned to one of the three different treatment categories (i.e., control condition, laughter faded out or no laughter).

Immediately after the video clips were viewed, participants were first asked to describe their thoughts on the video, how strongly they felt in reference to different emotions at that moment (anxious, proud, angry, reassured, fearful, irritated, disgusted, sad, and happy) on a 0–10 (not at all to extremely). They were then asked their evaluation of the reporter, Leslie Stahl, in terms of their overall impressions of her, as well as how credible, appropriate, and likable she was on a seven-point scale. These items were then combined into an additive index (Cronbach’s a = .873). A final measure, that of how aggressive Stahl was perceived to be, due to weak correlations with the other measures, was analysed separately.

Participants were then asked to evaluate Ronald Reagan’s leadership traits in terms of his competence , which was based upon measures of how sincere, aggressive, strong, active, competent he appeared to be (Cronbach’s a = .779); additional measures considered a scale of his warmth with questions regarding how intelligent, caring, trustworthy, agreeable, and warm (Cronbach’s a = .928) he appeared during the news story. Responses regarding evaluation of both Leslie Stahl and Ronald Reagan ranged from “Not at all” to “Extremely” on a seven-point (0–6) scale. Finally, to evaluate whether participants noticed the treatment, we asked “How believable did you find the video clip to be?” on the same seven-point scale. Throughout the reported statistical tests an alpha level of >0.05 is designated as n/s.

Emotional response to the video showed that, how anxious ( F = .283, p = ns), proud ( F = .465, p = ns), angry ( F = 1.448, p = ns), reassured ( F = .644, p = ns), fearful ( F = 1.848, p = ns), disgusted ( F = .632, p = ns), sad ( F = .192, p = ns), and happy ( F = .119, p = ns) participants felt was unaffected by the laughter. However, when least significant differences are considered, participants felt significantly less irritated ( F = 4.124, p = .017, partial η 2 = .029) when watching the original video ( M = 3.646) when compared with the treatment videos with laughter faded out ( M = 2.611, p = .008) and laughter completely removed ( M = 2.793, p = .029).

Participant ratings of Leslie Stahl in a similar manner suggested the treatment had little effect. Specifically, the index considering overall performance, perceived credibility, appropriateness, and likability, exhibited no significant violations of homogeneity ( F [2, 280] = 1.298, p = ns) according to the Levene’s test. Analysis of the index shows participants were largely unaffected by whether there was laughter present, faded, or removed entirely ( F [2, 280] = 0.480, p = ns). Likewise, Stahl’s perceived aggressiveness failed to reach statistical significance ( F [2, 280] = 2.722, p = ns).

Similarly, participants did not seem to notice a difference between the different videos. When asked “how believable did you find the video clip to be,” there was no significant difference between the treatments ( F = 1.005, p = ns). In combination with the preceding findings, there was not apparently a cognitively perceived effect from the video as participants were not aware of the treatment.

Analysis of the effect of laughter on evaluation of Ronald Reagan’s leadership traits tells a more nuanced story. Tests for homogeneity of variance regarding the competence index finds no significant violations ( F [2, 280] = 1.536, p = ns) as does the between-subjects ANOVA between the three groups: F [2, 280] = 2.677, p = ns. Although the patterns of response mirror those of perceived warmth (Laughter in M = 23.80; Laughter faded out M = 22.31; Laughter removed M = 22.20).

When Levene’s test for homogeneity of variance regarding the index of Reagan’s warmth is considered, no significant violations were found ( F [2, 280] = 1.699, p = ns). However a highly significant between-subjects effect across the three humour groups was revealed: F [2, 280] = 4.078, p = .018, partial η 2 = .028. Here, Reagan’s trait evaluations were enhanced by the presence of the loud laughter evident in the original news story, with post-hoc comparisons showing that the laughter remaining condition (M = 25.94, p = 0.05) was significantly greater than the faded-out condition (M = 23.39) and the complete removal of the laughter (M = 23.60). Thus, while perceptions of his warmth are all relatively high, they are significantly reduced by the laughter either being faded out or removed entirely (p = ns).

Finally, as a control item a single measure of how humorous participants thought Reagan to be was included. No significant violations of homogeneity were found ( F [2, 280] = 1.497, p = ns) and the pattern revealed was similar to that regarding Reagan’s warmth index. Namely, a significant between-subjects effect between the three humor groups, F [2, 280] = 3.411, p = .034, partial η 2 = .024. When post-hoc least significant differences are considered, there were no significant differences between the laughter faded out (M = 5.13) and laughter completely removed (M = 4.96) groups, as was the case with perceptions of Reagan’s warmth . However, Reagan was considered significantly more humorous with the laughter in (M = 5.52) at the .05-level.

The finding that observers’ emotion was largely unaffected by the treatment, with the exception of feeling irritated, is perhaps not unexpected. The treatment, which is comprised of less than five seconds of laughter across the three Reagan excerpts, is a subtle and unobtrusive stimulus that potentially would not have an observable effect on self-reports of introspective evaluation of emotional response. Furthermore, by only considering between-subjects effects we cannot tease out whether the news story had a greater influence on participant emotional response and how this might have differed across the treatments. Even though the sole finding concerning irritation was aligned too the expectations both in the pattern of response, with greater irritation felt by those either not hearing laughter or diminished laughter, suggesting a failed humour attempt, and the comparatively weak effect of the treatment, future studies should consider change in self-reported emotional state through within-subjects design.

The lack of significant effect on evaluation of the reporter, Leslie Stahl, is likely due to the average age of the participants, which may have rendered her work as a nationally known figure unknown. While Ronald Reagan is recognized as a Republican Party icon and is mentioned in both glowing and critical terms by participants, Stahl is not so well recognized. As noted by Vraga and colleagues [ 53 ] when comparing participant response to a famous U.S. talk show host and an unfamiliar moderator “… a laugh track has very different effects when a host is a well-known comedian versus an unknown talk show host." (p.143) In other words, the perceptions of newscaster Stahl and the presentation of Reagan’s (un)successful humour may be premised upon the humour being interpreted as a benign violation of expectations [ 89 ], as opposed to ridicule that is received as more aggressive and less socially acceptable.

Type of humour likely play a role in perceptions of Ronald Reagan and how he is portrayed in this news story. As noted by Baumgartner, not only does “prior knowledge of the target of the humour affects susceptibility to attitude change” but also the context of political humour plays a role [ 90 ]. Whether the humour is other-deprecatory and ridicule-oriented or self-deprecatory plays a role in its perceptions especially upon considering the audience [ 60 ] when Reagan ridicules an audience member. Because of Reagan’s standing as a Republican Party icon, the effect of the audience’s response to his rejoinder to the dissenter within the audience might be accentuated if participants perceive his response being received in a less than flattering manner. This finding is consistent with considerable prior research considering the target of the humour, especially political figures [ 60 , 90 – 94 ].

The first experimental study is extended here by including control and full treatment levels, with all three laughter events present or removed; this allows replication of the first hypotheses regarding responses to laughter in the evaluation of leadership competence and warmth. This study will also examine the presence of laughter in response to Ronald Reagan’s humour and the effect that it will have on his perceived charismatic traits. The influence of specific laughter-eliciting comments removing concomitant laughter to consider the influence of different types of (un)successful humour will also be examined here. As a result, the second experimental study will have five different levels.

Additionally, the charisma of presidents is driven in part by perceived leadership traits of competence and warmth [ 68 ]. Even with participants not knowledgeable about Reagan, the positive visuals as well as the extensive applause-cheering throughout the news report, whether included inadvertently or not, does convey his charismatic presence. However, whether charisma plays a moderating or mediating role in conjunction with the observable audience response of laughter is still in question.

The second study utilizes three edits that totalled just under six seconds (5.46/100s = 2.93+.83+1.7) with a five condition between-subjects design. The first condition presented unedited video as a control with participants seeing what the 1984 CBS news viewers saw. The second replication treatment removed all three observable audience responses of laughter completely, with no noise from the video during the edits leading to the treatment effect 5.46 seconds of the total video (344.83s).

The next three conditions involved the removal of laughter from the three specific humorous comments. The third treatment, taking place from 3:19:02 until 3:21:25 of the video, showed Reagan responding to a heckler with the comment “I’ll raise his taxes” eliciting loud laughter followed by mixed cheering and applause. The fourth treatment involved the removal of less than a second of laughter from a small group of individuals and occurred from 3:26:29–3:27:21 of the video when a seated Reagan quipped “I never get good reviews from (the Russian news agency) TASS” after shaking his head. The final treatment condition saw Reagan use self-deprecatory humour to deflect an aggressive journalist’s question, leading to brief laughter at 3:41:26–3:43:03 of the video.

A power analysis using G*Power was carried out to determine sample size. Here, the traditional power estimation parameters for the least explained variable, the trait of competence , (1 –β err probability = 80%; α error probability = .05; effect size of f = .136). Findings based upon the effect likely, given the means and standard deviations uncovered in experimental study one, suggests a sample size of 650 participants would be required.

Participants were recruited using a snowball sampling approach in which upper-division undergraduate students received course credit for taking part in and recruiting participants. To better reflect the general population, older participants were systematically recruited, leading to a more age diverse sample. A total of 1041 individuals entered the study that lasted from November 16, 2020 to November 11, 2021; of those 315 did not spend at least seven minutes (420 seconds) in the study and were removed as per the previous study parameters. An additional 60 participants were removed due to their not responding to the open-ended prompt and a further 15 for not answering any post-treatment questions, leaving a total of 651 participants in the study. All ethical considerations, including consenting of the participants were identical to that reported for study 1.

Of those taking part, 61.4% identified as female, 83.3% identified as Caucasian (with 3.7% African-American, 0.5% Asian, 7.5% Hispanic, 2.6% Native American, and 2.3% other ethnicity); the average year of birth was 1982 old (range 1934–2005, SD = 16.3). The majority of participants identified themselves as identifying with the Democratic Party (38.9%), followed by Republican Party identifiers (33.3%), as independent (16.7%), Libertarian Party (3.9%), Green Party (1.4%) and other (5.6%). Random assignment of participants to the different treatments was balanced. We first replicated study 1 by having the unedited video control condition [ n = 119] and the treatment condition with all laughter removed [ n = 139]. The other three conditions considered the effect of removing individual laughter events, with the first removing laughter from Reagan’s response to a heckler [ n = 126], the second removing small group laughter [ n = 131], and the third removing group laughter in Reagan’s response to journalistic aggression [ n = 136]. When tested for randomness in assignment to across the five treatment conditions, we found no statistical bias (p = ns in all cases) for sex, ethnicity, age, party identification, and political ideology (social, economic, overall conservative-liberal).

As was the case with the first experimental study, participants were asked basic demographic questions (age, sex, ethnicity), as well as questions about whether they were registered to vote, the political party they identify with and self-reported political ideology. Additionally, they were asked to state how familiar they were with President Ronald Reagan, especially as this more externally valid sample had a greater distribution of ages and experience with Reagan, potentially influencing response. The distribution of participants was therefore examined as a separate, exploratory, and hypothesis-generating model with this variable as a moderator.

However, as the first experiment suggested differences in response to the video treatments, participants were asked to state their feelings both prior to and immediately after the presentation of the stimuli in terms of their emotions at that moment (anxious, proud, angry, reassured, fearful, irritated, disgusted, sad, and happy) on a 0–10 (not at all to extremely) scale. The evaluation of perceived charisma was based upon whether “This leader…” “moves people toward a goal,” “has a vision,” “inspire dares to take risks,” and “elicits a feeling of involvement in me.” [ 69 ]. The resulting scale showed strong reliability (Cronbach’s a = .865).

In line with the first experimental study, participants were asked to evaluate the reporter, Leslie Stahl, based upon their overall impressions of her, as well as how credible, appropriate, and likable she appeared in this video (Cronbach’s a = .919). Participants were also asked to evaluate Ronald Reagan’s leadership traits in terms of his competence , based upon measures of how sincere, aggressive, strong, active, competent he appeared to be (Cronbach’s a = .826). We also consider perceptions of his warmth with questions regarding how intelligent, caring, trustworthy, agreeable, and warm he appeared to be during the news story (Cronbach’s a = .908). All these were measured on a seven-point (0–6) scale ranging from “Not at all” to “Extremely”.

Change in emotional response from immediately before watching the video to immediately afterwards using repeated-measures ANOVA suggests that the video had a significant effect on how participants felt across all emotions (see Table 1 ). There was a small effect with a slight increase in fear (pre M = 1.567, se = .092; post M = 1.775, se = .097); sadness likewise showed a slight increase (pre M = 1.600, se = .088; post M = 2.059, se = .101) with a small-to-medium effect size, whereas felt anxiety decreased (pre M = 3.177, se = .115; post M = 2.719, se = .112) to a small-to-medium extent due to the video.

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Emotions associated with pleasantness and positive appraisal all decreased as a result of the video, showing either medium-to-large (proud pre-M = 4.002, se = .129) or large (reassured pre-M = 3.412, se = .122; post-M = 2.174, se = .105; happy pre-M = 5.843, se = .108; post-M = 4.272, se = .122). For their part, the negative appraisal emotions of irritated (pre-M = 1.786, se = .093; post-M = 3.101, se = .114), disgusted (pre-M = 1.023, se = 077; post-M = 2.540, se = .116) and anger (pre-M = 1.070, se = .076; post-M = 2.173, se = .116) all increase with the video having a large effect size.

While all emotional state measures changed because of the video, only anger was affected by the treatment condition. As can be expected, the least amount of increased anger came in the treatment with all three laughter elements present; while the four other treatments between the video with laughter and with it removed failed to reach statistical significance.(M = 1.626, se = .172, p =.ns; vs M = 1.567, se = .170, p = ns), significant differences only occurred when anger in the laughter-present video (M = 1.223, se = .181) was compared with all laughter absent (M = 1.745, se = 167, p = .034) and with the first treatment condition in which the first laughter utterance was removed (M = 1.948, se = .167, p = .004).

Participant ratings of Leslie Stahl in a similar manner suggested the treatment had little effect. Specifically, the index considering overall performance, perceived credibility, appropriateness, and likability, exhibited no significant violations of homogeneity ( F [4, 686] = 1.070, p = ns) according to the Levene’s test. Analysis of the index shows participants were largely unaffected by whether there was laughter present, faded, or removed entirely ( F [4, 686] = 0.387, p = ns). Likewise, Stahl’s perceived aggressiveness ( F [2, 280] = .174, p = ns) failed to reach statistical significance.

Similarly, participants did not seem to notice a difference between the different videos. When asked “how believable did you find the video clip to be,” there was no significant difference between the treatments ( F = .242, p = ns). In combination with the preceding findings, there was not apparently a cognitively perceived effect from the video as participants were not aware of the treatment.

Analysis of the effect of laughter and its removal from the video treatment on evaluation of Ronald Reagan’s leadership traits does not replicate the first experiment. Tests for homogeneity of variance regarding the competence index finds no significant violations ( F [4, 686] = 1.682, p = ns). The between-subjects ANOVA between the five groups does not reveal significant differences: F [4, 686] = 1.313, p = ns.

Levene’s test for homogeneity of variance revealed a significant violation ( F [4, 686] = 2.480, p = .043) when considering the index of Reagan’s warmth . When the Brown-Forsythe robust test was used, no significant between-subjects effects across the five humour groups was revealed: F [4, 686] = 1.299, p = ns. Likewise, while with perceptions of Reagan’s charisma no significant violations of homogeneity of variance were found using Levene’s test ( F [4, 686] = 1.919, p = ns); no significant between-subjects effects across the five humour groups was revealed; F [4, 686] = .516, p = ns.

Finally, as a control item a single measure of how humorous participants thought Reagan to be was included. Significant violations of homogeneity were found with the Levene test ( F [4, 686] = 5.377, p < .001), yet no significant differences between the groups were seen, F [4, 686] = .589, p = ns, when the Brown-Forsythe robust test was used.

The second study provides insight regarding the importance of the population used and methods employed. First, by using a more representative sample in terms of age, with the first study’s average age being twenty-one years old, and the second study’s average age of thirty-nine years old, we can expect that perceptions of President Ronald Reagan, to be well-established for good or bad. While a historical figure, allowing us to carry out an experiment over a long period of time without worries over external validity, Reagan remains a powerful political symbol in terms of social identity. Indeed, when considering the distributions on the constructs of charisma and warmth, eight percent of participants held a ceiling perception of him on both measures. Thus, even though age, gender, and party identity were randomly distributed through the different treatments, the likelihood of such a weak treatment—between less than a second of laughter to six seconds of laughter—embedded within a roughly five-minute video having an effect was diminished.

Second, the use of trait measures may not be sensitive enough to capture contemporaneous stimuli, especially regarding well known figures (and even those not so well known as in the case of Leslie Stahl). That we found significant and predictable change in all the participant emotional state self-report measures prior to and after watching the video, and that anger was most affected by the absence of laughter, both overall and in Reagan’s response to the heckler, suggests that the presence of laughter does have an effect on participants–even ones with strongly held opinions.

General discussion

Our findings cohere with the expectations of Vraga and colleagues [ 53 ] that when people have limited information to deal with ambiguous situations, they will rely upon subtle signals–especially those socially influential and reliable indicators of positive regard as audience laughter. In this paper, we find two substantially different groups of study participants responding in line with Vraga and colleagues’ results, as the much younger–and likely less politically knowledgeable–study 1 participants used audience laughter, or its absence, as a factor in their evaluating Ronald Reagan’s warmth and, to a lesser extent, competence. The older and more politically experience and involved experimental study 2 participants were not affected by audience laughter’s presence or absence in their evaluation of Reagan’s leadership traits. This was likely due to either experiencing Reagan as an active and polarizing political figure or as seeing him as a historically relevant political figure.

The second, subtle, and perhaps more compelling indicator that audience laughter does have an effect on participants lies with the indicators of appraised emotion. In the first experiment, there were between-subject treatment differences in felt irritation, with participants feeling less irritated when viewing the video with the laughter in than with the video with the laughter removed or faded out. While experimental study 2 participants felt irritation was not significantly affected, their felt anger was. In other words, the older and more politically experienced participants had a response in the same emotion family that replicated that of irritation, with those not hearing audience laughter more angry than those who did hear audience laughter, and both studies having similar effect sizes. Furthermore, the experimental extension in the second study, which teased out the effects of the success–as measured by audience laughter or its absence–of humorous statements found that Reagan’s aggressive quip in response to protesters (“I’ll raise his taxes”) had the strongest treatment effect when post-hoc comparisons were made, stronger even than all laughter removed. This suggests, in line with Stewart’s [ 60 ] finding that other-deprecating and aggressive humour, including ridicule, can be dangerous for a leader if supporters are not there to respond to a quip or joke with laughter.

Taken together, these findings point to a greater awareness of how even very subtle stimuli might affect various measures differently, especially given distinct populations. Having multiple measures thus not only makes sense in assessing discriminant validity of treatment effects it also provides for greater comprehension of how individual differences exhibit themselves. Because the traits of warmth, competence, and charisma can be seen as the crystallization of emotional appraisals in response to individuals over a period of time—albeit one that is more malleable in the absence of pre-existing information–choosing and paying attention to distinct measures based upon population characteristics makes eminent sense when planning a study. It also points towards the more extensive use of highly responsive measures of affect, such as provided by psychophysiology, when crafting an experiment and viewing appraisal and response as a continuum affected by multiple internal and external factors.

Conclusions

Perhaps the most pertinent finding from this paper pertains to the use of an externally valid stimulus that, while nearly forty years old, still resonates today both in experimental effects and lessons imparted. First, historically relevant stimuli remain impactful, as can be seen by the cornerstone work by Fein, Goethals, and Kugler [ 9 ] upon which this paper builds, as the fresh eyes (and brains) of undergraduates in our first experiment had their perceptions significantly affected nearly three decades after Ronald Reagan left the presidency. Perhaps more important is that such a minor treatment in our study–up to 5 ¾ seconds removed from a five-minute+ video–had a small-to-moderate effect size suggests that even perceived minor production choices can have subtle, yet impactful implications for the perceptions and choices of low-information voters reliant on the social influence of others. Despite the fact that the key news story was produced decades ago the use of humour is often seen in contemporary political settings. Future work exploring the social psychological effects of different types of humour that is displayed by politicians should focus on the interactions between humour types and the strength of the observable audience response. As we have shown here it is the interaction between the two that impacts audience perceptions, in turn likely shaping attitudes and, potentially, behaviour.

• View Article
• Google Scholar
• 4. Bucy EP. Nonverbal communication, emotion, and political evaluation. In: Doveling K, von Scheve C, Konijn EA, editors. The Routledge handbook of emotions and mass media. New York, NY: Taylor & Francis; 2011. p. 195–220.
• 5. Bucy EP, Stewart PA. The Personalization of Campaigns: Non-Verbal Cues in Presidential Debates. In: Thompson WR, editor. Oxford Research Encyclopedia of Politics. New York: Oxford University Press; 2018.
• PubMed/NCBI
• 17. Eibl-Eibesfeldt I. Human ethology. New York: Aldine De Gruyter; 1989. 848 p.
• 29. Foster BJ, Stewart PA. Observational research methods and politics. In: Peterson SA, Somit A, editors. Handbook of Biology and Politics. Northampton, MA: Edward Elgar Publishing; 2017. p. 324–44.
• 30. Stewart PA. Debatable Humor: Laughing Matters on the 2008 Presidential Primary Campaign. Lanham, MD: Lexington Books; 2012.
• 56. Eubanks AD, Stewart PA, Dye RG. Nobody Saw This Coming? Support for Hillary Clinton and Donald Trump Through Audience Reactions During the 2016 Presidential Debates. In: Browning RX, editor. Trump’s Twitter Presidency, Collaborative Learning, and Framing Mental Health: C-SPAN Insights―Volume 4. W. Lafayette, IN: Purdue University Press; 2018. p. 199–218.
• 57. Stewart PA. The Audience Decides: Applause-cheering, Laughter, and Booing During Debates in the Trump Era: Rowman & Littlefield; 2023.
• 72. Keating CF. About Face! Facial Status Cues and Perceptions of Charismatic Leadership. The Facial Displays of Leaders: Springer; 2018. p. 145–70.
• 74. Udall MK. Too funny to be President. Tucson, AZ: The University of Arizona Press; 1988. 249 p.
• 75. Dye RG. Applause, laughter, chants, and cheers: An analysis of the rhetorical skill of the "Great Communicator.". Fayetteville, AR: University of Arkansas, Fayetteville; 2018. Available: https://scholarworks.uark.edu/etd/2662/
• 76. Bull P, Waddle M. The Psychology of Political Communication: Politicians Under the Microscope: Taylor & Francis; 2023.
• 79. Ritter KW, Henry DR. Ronald Reagan: The Great Communicator: Greenwood Publishing Group; 1992.
• 83. Chadwick A. The hybrid media system: Politics and power: Oxford University Press; 2017.
• 84. Bucy EP. Media biopolitics: the emergence of a subfield. In: Peterson SA, Somit A, editors. Handbook of Biology and Politics. Northampton, MA: Edward Elgar Publishing; 2017. p. 284–303.
• 85. Grabe ME, Bucy EP. Image bite politics: News and the visual framing of elections. New York, NY: Oxford University Press; 2009. 316 p.
• 86. Kipp M. Multimedia Annotation, Querying and Analysis in ANVIL, chapter 19, Multimedia Information Extraction. In: Maybury MT, editor. Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring: Wiley, IEEE Computer Society Press; 2012. p. 351–86.
• 87. Stewart PA, Hall SC. Microanalysis of the appropriateness of facial displays during presidential debates: C-SPAN coverage of the first and third 2012 debates. In: Browning RX, editor. Exploring the C-SPAN Archives: Advancing the Research Agenda. West Lafayette, IN: Purdue University Press; 2016. p. 103–29.

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• Published: 18 April 2024

A method for identifying different types of university research teams

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

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

Metrics details

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

Introduction

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

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

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

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

How can we identify different types of university research teams?

What are the characteristics of research groups within universities?

Literature review

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

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

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

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

Team identification method

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

Classification of research team members

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

The original idea of team member classification

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

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

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

Establishment of team member classification criteria

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

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

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

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

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

Classification of research teams

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

Project-based research teams: identification based on research projects

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

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

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

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

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

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

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

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

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

Identification of university discipline backbone members

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

Team identification with discipline backbone members as the Core

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

Merging based on similarity perspective

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

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

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

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

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

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

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

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

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

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

Representative team: consolidation via backbone-based research group

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

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

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

Research team identification process and its pros and cons

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

Different university research teams are identified at different stage.

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

Validation of identification results

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

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

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

Data collection and preprocessing

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

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

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

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

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

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

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

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

Identification results of university research teams

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

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

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

Analysis of the characteristics of Bbrg in universities

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

Group scale

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

Group structure

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

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

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

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

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

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

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

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

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

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

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

Group output

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

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

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

Typical cases

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

Basic Information of the Bbrgs

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

Characteristics of team member network structure

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

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

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

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

Discussion and conclusion

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

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

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

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

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

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

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

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

Data availability

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

Aagaard K, Kladakis A, Nielsen MW (2020) Concentration or dispersal of research funding? Quant Sci Stud 1(1):117–149. https://doi.org/10.1162/qss_a_00002

Article   Google Scholar  

Abramo G, D’Angelo CA, Di Costa F (2017) Do interdisciplinary research teams deliver higher gains to science? Scientometrics 111:317–336. https://doi.org/10.1007/s11192-017-2253-x

Barjak F, Robinson S (2008) International collaboration, mobility and team diversity in the life sciences: impact on research performance. Soc Geogr 3(1):23–36. https://doi.org/10.5194/sg-3-23-2008

Boardman C, Ponomariov B (2014) Management knowledge and the organization of team science in university research centers. J Technol Transf 39:75–92. https://doi.org/10.1007/s10961-012-9271-x

Boyack KW, Klavans R (2014) 12 Identifying and Quantifying Research Strengths Using Market Segmentation. In: Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact, 225, MIT Press, Cambridge

Bozeman B, Youtie J (2018) The strength in numbers: The new science of team science. Princeton University Press. https://doi.org/10.1515/9781400888610

Bloch C, Sørensen MP (2015) The size of research funding: Trends and implications. Sci Public Policy 42(1):30–43. https://doi.org/10.1093/scipol/scu019

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

Coles NA, Hamlin JK, Sullivan LL, Parker TH, Altschul D (2022) Build up big-team science. Nature 601(7894):505–507. https://doi.org/10.1038/d41586-022-00150-2

Article   ADS   CAS   PubMed   Google Scholar  

Dino H, Yu S, Wan L, Wang M, Zhang K, Guo H, Hussain I (2020) Detecting leaders and key members of scientific teams in co-authorship networks. Comput Electr Eng 85:106703. https://doi.org/10.1016/j.compeleceng.2020.106703

Deng H, Breunig H, Apte J, Qin Y (2022) An early career perspective on the opportunities and challenges of team science. Environ Sci Technol 56(3):1478–1481. https://doi.org/10.1021/acs.est.1c08322

Everett M (2002) Social network analysis. In: Textbook at Essex Summer School in SSDA, 102, Essex Summer School in Social Science Data Analysis, United Kingdom

Forscher PS, Wagenmakers EJ, Coles NA, Silan MA, Dutra N, Basnight-Brown D, IJzerman H (2023) The benefits, barriers, and risks of big-team science. Perspect Psychological Sci 18(3):607–623. https://doi.org/10.1177/17456916221082970

Guo K, Huang X, Wu L, Chen Y (2022) Local community detection algorithm based on local modularity density. Appl Intell 52(2):1238–1253. https://doi.org/10.1007/s10489-020-02052-0

Hu Z, Lin A, Willett P (2019) Identification of research communities in cited and uncited publications using a co-authorship network. Scientometrics 118:1–19. https://doi.org/10.1007/s11192-018-2954-9

Imran F, Abbasi RA, Sindhu MA, Khattak AS, Daud A, Amjad T (2018) Finding research areas of academicians using clique percolation. In 2018 14th International Conference on Emerging Technologies (ICET). IEEE, pp 1–6. https://doi.org/10.1109/ICET.2018.8603549

Lee HJ, Kim JW, Koh J, Lee Y (2008) Relative Importance of Knowledge Portal Functionalities: A Contingent Approach on Knowledge Portal Design for R&D Teams. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008). IEEE, pp 331–331, https://doi.org/10.1109/HICSS.2008.373

Liao Q (2018) Research Team Identification and Influence Factors Analysis of Team Performance. M. A. Thesis. Beijing Institute of Technology, Beijing

Google Scholar  

Li Y, Tan S (2012) Research on identification and network analysis of university research team. Sci Technol Prog policy 29(11):147–150

Li G, Liu M, Wu Q, Mao J (2017) A Research of Characters and Identifications of Roles Among Research Groups Based on the Bow-Tie Model. Libr Inf Serv 61(5):87–94

Liu Y, Wu Y, Rousseau S, Rousseau R (2020) Reflections on and a short review of the science of team science. Scientometrics 125:937–950. https://doi.org/10.1007/s11192-020-03513-6

Lungeanu A, Huang Y, Contractor NS (2014) Understanding the assembly of interdisciplinary teams and its impact on performance. J Informetr 8(1):59–70. https://doi.org/10.1016/j.joi.2013.10.006

Article   PubMed   PubMed Central   Google Scholar  

Lv L, Zhao Y, Wang X, Zhao P (2016) Core R&D Team Recognition Method Based on Association Rules Mining. Sci Technol Manag Res 36(17):148–152

Ma A, Mondragón RJ, Latora V (2015) Anatomy of funded research in science. Proc Natl Acad Sci 112(48):14760–14765. https://doi.org/10.1073/pnas.1513651112

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Merton RK (1973) The sociology of science: Theoretical and empirical investigations. University of Chicago Press, Chicago

Mongeon P, Brodeur C, Beaudry C, Larivière V (2016) Concentration of research funding leads to decreasing marginal returns. Res Eval 25(4):396–404. https://doi.org/10.1093/reseval/rvw007

National Research Council (2015) Enhancing the effectiveness of team science. The National Academies Press, Washington, DC

Okamoto J, Centers for Population Health and Health Disparities Evaluation Working Group (2015) Scientific collaboration and team science: a social network analysis of the centers for population health and health disparities. Transl Behav Med 5(1):12–23. https://doi.org/10.1007/s13142-014-0280-1

Article   PubMed   Google Scholar  

Peterson JB (2018) 12 rules for life: An antidote to chaos. Random House, Canada

Scott J (2017) Social network analysis. Sage Publications Ltd, London

Seidman SB, Foster BL (1978) A graph‐theoretic generalization of the clique concept. J Math Sociol 6(1):139–154. https://doi.org/10.1080/0022250X.1978.9989883

Article   MathSciNet   Google Scholar  

Sun Y, Livan G, Ma A, Latora V (2021) Interdisciplinary researchers attain better long-term funding performance. Commun Phys 4(1):263. https://doi.org/10.1038/s42005-021-00769-z

Stokols D, Hall KL, Taylor BK, Moser RP (2008) The science of team science: overview of the field and introduction to the supplement. Am J Prev Med 35(2):S77–S89. https://doi.org/10.1016/j.amepre.2008.05.002

Wang C, Cheng Z, Huang Z (2017) Analysis on the co-authoring in the field of management in China: based on social network analysis. Int J Emerg Technol Learn 12(6):149. https://doi.org/10.3991/ijet.v12i06.7091

Wang T, Chen S, Wang X, Wang J (2020) Label propagation algorithm based on node importance. Phys A Stat Mech Appl. 551:124137. https://doi.org/10.1016/j.physa.2020.124137

Wu L, Wang D, Evans JA (2019) Large teams develop and small teams disrupt science and technology. Nature 566(7744):378–382. https://doi.org/10.1038/s41586-019-0941-9

Xu F, Wu L, Evans J (2022) Flat teams drive scientific innovation. Proc. Natl Acad. Sci 119(23):e2200927119. https://doi.org/10.1073/pnas.2200927119

Article   CAS   PubMed   PubMed Central   Google Scholar  

Yu H, Bai K, Zou B, Wang Y (2020) Identification and Extraction of Research Team in the Artificial Intelligence Field. Libr Inf Serv 64(20):4–13

Yu Y, Dong C, Han H, Li Z (2018) The Method of Research Teams Identification Based on Social Network Analysis:Identifying Research Team Leaders Based on Iterative Betweenness Centrality Rank Method. Inf Stud Theory Appl 41(7):105–110

Zhao L, Zhang Q, Wang L (2014) Benefit distribution mechanism in the team members’ scientific research collaboration network. Scientometrics 100:363–389. https://doi.org/10.1007/s11192-014-1322-7

Zhang M, Jia Y, Wang N, Ge S (2019) Using Relative Tie Strength to Identify Core Teams of Scientific Research. Int J Emerg Technol Learn 14(23):33–54. https://www.learntechlib.org/p/217243/

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

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

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6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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Consider this scenario: you’ve been told to clean up a giant room full of Things Your Agency Has Made in the Past and Now Maintains for Public Use . This means disposing of the Things that no longer add value, and sprucing up the Things that are still useful. How do you determine which Things belong in which category, especially when all the Things in that giant room have been used by the public, and available for all to see?

When the “things” we’re talking about are websites, this determination is often much more complicated than it might appear on the surface. This scenario is one facing web teams across the government, including at the U.S. General Services Administration (GSA), every single day. If you’re in this situation, consider all the ways you might begin to tackle this cleanup job.

Evaluating by visits

You decide to start by determining how many people visit each website each month. Delighted, you pull those numbers together and produce a chart that looks something like this:

The chart states that the 10 least-visited GSA websites had only about 66 visits in the past 30 days, whereas the top 10 websites averaged over 629,000 visits, and the agency average websites averaged over 244,000 monthly visits. So there you have it: clearly, it appears the websites with only 66 visits are the least useful and should be decommissioned. (Note that the low-traffic websites all show 66 visits because of the analytics tool’s statistical sampling methodology.)

However, you stop to examine one of the low-traffic sites. In studying it, you realize that it was never designed to have many visitors. Instead, it was designed to support a very small audience that only appears at random, unpredictable intervals; say, when a natural disaster strikes. Clearly, you don’t want to get rid of that website, since it’s meeting a specific need of a small but well-defined and important audience.

Through this consideration, you realize that using the number of visitors to determine the usefulness of a website incorrectly assumes:

• Each visit across all your websites is of the same value.
• Each audience, whether 66 people, or 629,000, have the same level of urgency and need for each website, even if one website is intended to serve a large, continuous audience, while another is designed to serve a small, irregular audience.

Since both of these assumptions are false, visitor numbers are not enough to determine the usefulness of a website. You need another evaluation tactic.

Evaluating by accessibility

After some consideration, you realize that all the websites have to be fully accessible to everyone, regardless of ability. You also have the tools and processes to help determine whether that standard has been reached. Excited, you start by assembling and running your automated accessibility tests.

Five websites stand out as having the worst accessibility errors, according to your tests. Clearly, these websites must go. As you prepare to get rid of them, however, you notice that the vast majority of the errors in the worst website are identical and all seem to originate from the same part of the website. You look closer and realize that the problem causing all those errors is actually quite basic and can be fixed easily, taking the worst website out of the bottom ranking. Looking at the other websites in your list, you realize that other errors that have surfaced are only errors in an automatic test, not a human one. Many of them aren’t on critical paths for the website’s use, so while they should be addressed, they are not meaningfully blocking access to the website.

That throws your entire evaluation into question: how can you possibly batch and judge the usefulness of a website by accessibility, if the severity and impact of each accessibility error varies so much? Instead, you must pair automated accessibility tests with manual testing to reach conclusions on the least accessible websites. That won’t help you quickly get rid of the lowest value websites, so yet another evaluation tactic is needed.

Evaluating by speed and performance

After considering the number of visits and the accessibility, you realize that an evaluation of usefulness needs to consider a basic question: is the performance and speed of the website reasonable? If a product is so frustratingly slow that people don’t use it, then nothing else matters.

To figure out which websites are so slow as to be essentially non-functional, you find a free online tool that tests website performance. Additionally, you get smart based on your previous experiments: this tool tests for a few different parameters, not just one element of performance. It then compiles these parameters into a single index score, so its results are compelling.

This performance metric shows you that, on average, your websites perform at 84% of a perfect 100% score, and there are a few low-performing websites at 26% performance or lower. This works for you; you know you need to get rid of your agency’s low-performing websites. As you’re planning to decommission these sites, however, a user visits one of them to complete a task and provides some feedback.

Evaluating by customer research

The user waits while the website slowly loads. Then, they interact with the website and exit the page. To gauge their satisfaction, you prompt them to give you feedback on the page by asking, “Was this page helpful?” The user shares:

“This website does work; it just works slowly. I’m willing to wait, though, because I need the information. There’s nowhere else to get this information, so please don’t get rid of this website; I have to come back and get information from it every month.”

After taking this customer research into account, you realize that visits, accessibility, performance, and speed do not, on their own, fully reflect the website’s value, so you still don’t know which websites to decommission.

At this point, you’ve discovered that evaluating websites is a multidimensional problem — one that cannot be determined by a single, simple metric. Indeed, even when you consider several metrics, your conclusions lack a customer’s perspective.

Determining the value of agency websites therefore must use an index that is not just composed of similar metrics (like the performance index) but is in fact a composite index of different datasets of different data types. This approach will allow you to evaluate the website’s purpose, function, and ultimately, value, to your agency and your customers. This aggregation of dataset types is known as a composite indicator.

Methodology: The Enterprise Digital Experience composite indicator

This is the story of evaluating websites in GSA. Websites seem simple to evaluate: do they work or not? But in truth, they are a multidimensional problem. In taking on the definition and evaluation of GSA public-facing websites, the Service Design team in GSA’s Office of Customer Experience researched and designed a composite indicator of multiple data sets of different types to evaluate the value of websites in GSA. Since 2021, we’ve been doing this by examining six things:

Accessibility , scored by our agency standard accessibility tool ( quantitative data, 21st Century IDEA Section 3A.1 )

Customer-centricity , scored by a human-centered design interview ( qualitative data, 21st Century IDEA Section 3A.6 and OMB Circular A-11 280.1 and 280.8 )

• Stated audience : Can the website team succinctly and precisely name their website’s primary audience?
• Stated purpose : Can the website team succinctly and precisely name their website’s primary purpose?
• Measurement of purpose : Does the website have a replicable means to measure if the website’s purpose is being achieved?
• Repeatable customer feedback mechanism : Does the website team have a repeatable customer feedback mechanism in place, such as an embedded survey, or recurring, well-promoted and attended meetings, or focus groups with customers? (Receiving ad hoc feedback from customer call centers or email submissions does not meet this mark.)
• Ability to action : Does the website team have a skillset that can contribute to rapidly improving the website based on feedback and need, such as human-centered design research, user experience, writing, or programming skills?
• Ability to measure impact : Does the website team have the ability to measure the impact of the improvements they implement? Have they devised and implemented a measurement methodology specifically for their changes (an ability to measure impact) or do they rely solely on blanket measures such as Digital Analytics Program data (no ability to measure impact)?

Performance and search engine optimization , scored by Google Lighthouse ( quantitative data, 21st Century IDEA Section 3A.8 )

Required links , scored by the Site Scanning Program ’s website scan ( quantitative data, 21st Century IDEA Section 3A.1 & 3E )

User behavior, non-duplication , scored by Google Analytics with related sites ( qualitative + quantitative data, 21st Century IDEA Section 3A.3 )

U.S. Web Design System implementation , scored by Site Scanning Program’s website scan ( qualitative + quantitative data, 21st Century IDEA Section 3A.1 & 3E )

View all sections of the law and the circular mentioned above:

• 21st Century IDEA (Public Law No. 115-336)
• OMB Circular A-11 (PDF, 385 KB, 14 pages, 2023)

We visualize this evaluation in website maps, rendered as charts that are available internally to GSA employees. This helps us see examples of good performers, such as Website A (on the left), and not-so-good performers, like Website B (on the right.)

In addition, these charts, like all maps [1] , contains some decisions that prioritize how the information is rendered. They include:

• An equal weight to all datasets and data types, regardless of fidelity . In the charts above, the slices spread out from 0 along even increments. Our measurement of customer-centricity gives equal weight to whether a site proactively listens to their customers, as well as to whether it has the resources to implement change.
• A direct comparison by slice . For example, our customer-centricity slice gives the same amount of distance from the center for listening to its customers as our required links slice gives for including information about privacy, regardless of the fact that customer listening is foundationally different (and more complicated) as an activity than including required links.

We made these decisions because to weight all of the metrics would be to travel down the coastline paradox [2] , meaning: we had to identify a stopping point for measurement and comparison that is somewhat arbitrary because, paradoxically, the more closely we measure and compare, the less clear the GSA digital ecosystem would become. These measures are the baseline because, broadly, they are fair in their unfairness: some things are easier to do, and some things are harder, but what is “easier” and what is “harder” differs depending on the resources available to each website team.

But even in comparing websites using charts and maps containing multiple dataset types, we’re missing some nuance. “Website A” is a simple, informational site, whereas “Website B” contains a pricing feature, which introduces additional complexities that are more difficult to manage than simple textual information. To give visibility to this nuance, the Service Design team uses these maps as part of a broader website evaluation package, which includes qualitative research interviews and subsequent evaluation write ups. These are sent to every website team within three weeks after we conduct the research interview. Taken together, the quantitative and qualitative data in the website evaluation packages allow GSA staff to consistently measure how digital properties are functioning, and what their impact is on customers.

Concluding which websites should exist

The reality is: value exists in dimensions, not in single data points, or even in single datasets. To further complicate things, the closer you look at single datasets, the more your decision-making process is complicated, rather than clarified. This is because each data type and each data point in complex systems can be broken down into infinitely smaller pieces, rendering decisions made based on these pieces more accurate, but also of smaller and smaller impact. [3]

None of the measures in the Enterprise Digital Experience composite indicator or their use as a whole pie results in an affirmation or denial of the value of a digital property to the agency or to the public; value will always exist as an interpretation of these datasets. The indicator can tell us how existing sites are doing, but not whether we should continue supporting them.

To understand whether a website is worth supporting and how to evolve it, the Service Design team pairs qualitative and quantitative data with mission and strategic priorities to evaluate which websites to improve, and which to stop supporting. To achieve this pairing, three elements must come together:

• Technical evaluations
• Regular dialogue with each website’s customers, including internal stakeholders and leadership
• Enterprise-level meta-analysis of a digital property’s functions in comparison to other digital properties

Customer dialogue is the responsibility of each team, and technical evaluations are readily available, thanks to tools like the Digital Analytics Program (DAP), but enterprise-level meta-analyses require a cross-functional view. This view can be attained through matrixed initiatives like GSA’s Service Design program, or cross-functional groups like GSA’s Digital Council, in collaboration with program teams and leadership.

From an enterprise perspective, the next phase in our evaluation of GSA properties is to apply service categories to each website, to better understand how GSA is working along categorical lines, instead of businesses or brands. Taxonomical work like this is the domain of enterprise architecture. Our service category taxonomy was compiled by using the Federal Enterprise Architecture Framework (FEAF) [4] as a starting point, and crosswalks a website’s designed function with its practical function, evaluated through general and agency use.

We’re starting to leverage service categories, and working with teams to create a more coalesced view of website value as we do so.

What can I do next?

Review an introduction to analytics to learn how metrics and data can improve understanding of how people use your website.

If you work at a U.S. federal government agency, and would like to learn more about this work, reach out to GSA’s Service Design team at [email protected] .

Disclaimer : All references to specific brands, products, and/or companies are used only for illustrative purposes and do not imply endorsement by the U.S. federal government or any federal government agency.

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