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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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Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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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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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

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

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

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

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

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

 Famous Case Studies

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

Clinical Case Studies

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

Child Psychology Case Studies

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

Types of Case Studies

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

Where Do You Find Data for a Case Study?

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

1. Primary sources

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

2. Secondary sources

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

3. Archival records

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

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

4. Organizational records

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

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

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

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

How do I Write a Case Study in Psychology?

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

1. Introduction

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

2. Case Presentation

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

3. Management and Outcome

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

4. Discussion

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

5. Additional Items

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

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

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

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

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

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

Limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

• Open access
• Published: 27 June 2011

The case study approach

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

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

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

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Introduction

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

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

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

What is a case study?

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

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

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

What are case studies used for?

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

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

How are case studies conducted?

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

Defining the case

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Conclusions

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

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Acknowledgements

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

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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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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."

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

11 Case research

Case research—also called case study—is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, pre-recorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualised. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

Case research has several unique strengths over competing research methods such as experiments and survey research. First, case research can be used for either theory building or theory testing, while positivist methods can be used for theory testing only. In interpretive case research, the constructs of interest need not be known in advance, but may emerge from the data as the research progresses. Second, the research questions can be modified during the research process if the original questions are found to be less relevant or salient. This is not possible in any positivist method after the data is collected. Third, case research can help derive richer, more contextualised, and more authentic interpretation of the phenomenon of interest than most other research methods by virtue of its ability to capture a rich array of contextual data. Fourth, the phenomenon of interest can be studied from the perspectives of multiple participants and using multiple levels of analysis (e.g., individual and organisational).

At the same time, case research also has some inherent weaknesses. Because it involves no experimental control, internal validity of inferences remain weak. Of course, this is a common problem for all research methods except experiments. However, as described later, the problem of controls may be addressed in case research using ‘natural controls’. Second, the quality of inferences derived from case research depends heavily on the integrative powers of the researcher. An experienced researcher may see concepts and patterns in case data that a novice researcher may miss. Hence, the findings are sometimes criticised as being subjective. Finally, because the inferences are heavily contextualised, it may be difficult to generalise inferences from case research to other contexts or other organisations.

It is important to recognise that case research is different from case descriptions such as Harvard case studies discussed in business classes. While case descriptions typically describe an organisational problem in rich detail with the goal of stimulating classroom discussion and critical thinking among students, or analysing how well an organisation handled a specific problem, case research is a formal research technique that involves a scientific method to derive explanations of organisational phenomena.

Case research is a difficult research method that requires advanced research skills on the part of the researcher, and is therefore often prone to error. Benbasat, Goldstein and Mead (1987) [1] describe five problems frequently encountered in case research studies. First, many case research studies start without specific research questions, and therefore end up without having any specific answers or insightful inferences. Second, case sites are often chosen based on access and convenience, rather than based on the fit with the research questions, and are therefore cannot adequately address the research questions of interest. Third, researchers often do not validate or triangulate data collected using multiple means, which may lead to biased interpretation based on responses from biased interviewees. Fourth, many studies provide very little details on how data was collected (e.g., what interview questions were used, which documents were examined, the organisational positions of each interviewee, etc.) or analysed, which may raise doubts about the reliability of the inferences. Finally, despite its strength as a longitudinal research method, many case research studies do not follow through a phenomenon in a longitudinal manner, and hence present only a cross-sectional and limited view of organisational processes and phenomena that are temporal in nature.

Key decisions in case research

Several key decisions must be made by a researcher when considering a case research method. First, is this the right method for the research questions being studied? The case research method is particularly appropriate for exploratory studies, for discovering relevant constructs in areas where theory building is in the formative stages, for studies where the experiences of participants and context of actions are critical, and for studies aimed at understanding complex, temporal processes (why and how) rather than factors or causes (what). This method is well-suited for studying complex organisational processes that involve multiple participants and interacting sequences of events, such as organisational change and large-scale technology implementation projects.

Second, what is the appropriate unit of analysis for a case research study? Since case research can simultaneously examine multiple units of analyses, the researcher must decide whether she wishes to study a phenomenon at the individual, group, or organisational level or at multiple levels. For instance, a study of group decision-making or group work may combine individual-level constructs such as individual participation in group activities with group-level constructs, such as group cohesion and group leadership, to derive richer understanding than can be achieved from a single level of analysis.

Third, should the researcher employ a single-case or multiple-case design? The single-case design is more appropriate at the outset of theory generation, if the situation is unique or extreme, if it is revelatory (i.e., the situation was previously inaccessible for scientific investigation), or if it represents a critical or contrary case for testing a well-formulated theory. The multiple-case design is more appropriate for theory testing, for establishing generalisability of inferences, and for developing richer and more nuanced interpretations of a phenomenon. Yin (1984) [2] recommends the use of multiple case sites with replication logic, viewing each case site as similar to one experimental study, and following rules of scientific rigor similar to that used in positivist research.

Fourth, what sites should be chosen for case research? Given the contextualised nature of inferences derived from case research, site selection is a particularly critical issue because selecting the wrong site may lead to the wrong inferences. If the goal of the research is to test theories or examine generalisability of inferences, then dissimilar case sites should be selected to increase variance in observations. For instance, if the goal of the research is to understand the process of technology implementation in firms, a mix of large, mid-sized, and small firms should be selected to examine whether the technology implementation process differs with firm size. Site selection should not be opportunistic or based on convenience, but rather based on the fit with research questions though a process called ‘theoretical sampling’.

Fifth, what techniques of data collection should be used in case research? Although interview (either open-ended/unstructured or focused/structured) is by far the most popular data collection technique for case research, interview data can be supplemented or corroborated with other techniques such as direct observation (e.g., attending executive meetings, briefings, and planning sessions), documentation (e.g., internal reports, presentations, and memoranda, as well as external accounts such as newspaper reports), archival records (e.g., organisational charts, financial records, etc.), and physical artefacts (e.g., devices, outputs, tools). Furthermore, the researcher should triangulate or validate observed data by comparing responses between interviewees.

Conducting case research

Most case research studies tend to be interpretive in nature. Interpretive case research is an inductive technique where evidence collected from one or more case sites is systematically analysed and synthesised to allow concepts and patterns to emerge for the purpose of building new theories or expanding existing ones. Eisenhardt (1989) [3] proposed a ‘roadmap’ for building theories from case research—a slightly modified version of which is described below. For positivist case research, some of the following stages may need to be rearranged or modified, however sampling, data collection, and data analytic techniques should generally remain the same.

Define research questions. Like any other scientific research, case research must also start with defining research questions that are theoretically and practically interesting, and identifying some intuitive expectations about possible answers to those research questions or preliminary constructs to guide initial case design. In positivist case research, the preliminary constructs are based on theory, while no such theories or hypotheses should be considered ex ante in interpretive research. These research questions and constructs may be changed in interpretive case research later on, if needed, but not in positivist case research.

Select case sites. The researcher should use a process of ‘theoretical sampling’—not random sampling—to identify case sites. In this approach, case sites are chosen based on theoretical rather than statistical considerations—for instance, to replicate previous cases, to extend preliminary theories, or to fill theoretical categories or polar types. Care should be taken to ensure that the selected sites fit the nature of research questions, minimise extraneous variance or noise due to firm size, industry effects, and so forth, and maximise variance in the dependent variables of interest. For instance, if the goal of the research is to examine how some firms innovate better than others, the researcher should select firms of similar size within the same industry to reduce industry or size effects, and select some more innovative and some less innovative firms to increase variation in firm innovation. Instead of cold-calling or writing to a potential site, it is better to contact someone at executive level inside each firm who has the authority to approve the project, or someone who can identify a person of authority. During initial conversations, the researcher should describe the nature and purpose of the project, any potential benefits to the case site, how the collected data will be used, the people involved in data collection (other researchers, research assistants, etc.), desired interviewees, and the amount of time, effort, and expense required of the sponsoring organisation. The researcher must also assure confidentiality, privacy, and anonymity of both the firm and the individual respondents.

Create instruments and protocols. Since the primary mode of data collection in case research is interviews, an interview protocol should be designed to guide the interview process. This is essentially a list of questions to be asked. Questions may be open-ended (unstructured) or closed-ended (structured) or a combination of both. The interview protocol must be strictly followed, and the interviewer must not change the order of questions or skip any question during the interview process, although some deviations are allowed to probe further into a respondent’s comments if they are ambiguous or interesting. The interviewer must maintain a neutral tone, and not lead respondents in any specific direction—for example, by agreeing or disagreeing with any response. More detailed interviewing techniques are discussed in the chapter on surveys. In addition, additional sources of data—such as internal documents and memorandums, annual reports, financial statements, newspaper articles, and direct observations—should be sought to supplement and validate interview data.

Select respondents. Select interview respondents at different organisational levels, departments, and positions to obtain divergent perspectives on the phenomenon of interest. A random sampling of interviewees is most preferable, however a snowball sample is acceptable, as long as a diversity of perspectives is represented in the sample. Interviewees must be selected based on their personal involvement with the phenomenon under investigation and their ability and willingness to answer the researcher’s questions accurately and adequately, and not based on convenience or access.

Start data collection . It is usually a good idea to electronically record interviews for future reference. However, such recording must only be done with the interviewee’s consent. Even when interviews are being recorded, the interviewer should take notes to capture important comments or critical observations, behavioural responses (e.g., the respondent’s body language), and the researcher’s personal impressions about the respondent and his/her comments. After each interview is completed, the entire interview should be transcribed verbatim into a text document for analysis.

Conduct within-case data analysis. Data analysis may follow or overlap with data collection. Overlapping data collection and analysis has the advantage of adjusting the data collection process based on themes emerging from data analysis, or to further probe into these themes. Data analysis is done in two stages. In the first stage (within-case analysis), the researcher should examine emergent concepts separately at each case site and patterns between these concepts to generate an initial theory of the problem of interest. The researcher can use interview data subjectively to ‘make sense’ of the research problem in conjunction with using his/her personal observations or experience at the case site. Alternatively, a coding strategy such as Glaser and Strauss’ (1967) [4] grounded theory approach, using techniques such as open coding, axial coding, and selective coding, may be used to derive a chain of evidence and inferences. These techniques are discussed in detail in a later chapter. Homegrown techniques, such as graphical representation of data (e.g., network diagram) or sequence analysis (for longitudinal data) may also be used. Note that there is no predefined way of analysing the various types of case data, and the data analytic techniques can be modified to fit the nature of the research project.

Conduct cross-case analysis. Multi-site case research requires cross-case analysis as the second stage of data analysis. In such analysis, the researcher should look for similar concepts and patterns between different case sites, ignoring contextual differences that may lead to idiosyncratic conclusions. Such patterns may be used for validating the initial theory, or for refining it—by adding or dropping concepts and relationships—to develop a more inclusive and generalisable theory. This analysis may take several forms. For instance, the researcher may select categories (e.g., firm size, industry, etc.) and look for within-group similarities and between-group differences (e.g., high versus low performers, innovators versus laggards). Alternatively, they can compare firms in a pairwise manner listing similarities and differences across pairs of firms.

Build and test hypotheses. Tenative hypotheses are constructed based on emergent concepts and themes that are generalisable across case sites. These hypotheses should be compared iteratively with observed evidence to see if they fit the observed data, and if not, the constructs or relationships should be refined. Also the researcher should compare the emergent constructs and hypotheses with those reported in the prior literature to make a case for their internal validity and generalisability. Conflicting findings must not be rejected, but rather reconciled using creative thinking to generate greater insight into the emergent theory. When further iterations between theory and data yield no new insights or changes in the existing theory, ‘theoretical saturation’ is reached and the theory building process is complete.

Write case research report. In writing the report, the researcher should describe very clearly the detailed process used for sampling, data collection, data analysis, and hypotheses development, so that readers can independently assess the reasonableness, strength, and consistency of the reported inferences. A high level of clarity in research methods is needed to ensure that the findings are not biased by the researcher’s preconceptions.

Interpretive case research exemplar

Perhaps the best way to learn about interpretive case research is to examine an illustrative example. One such example is Eisenhardt’s (1989) [5] study of how executives make decisions in high-velocity environments (HVE). Readers are advised to read the original paper published in Academy of Management Journal before reading the synopsis in this chapter. In this study, Eisenhardt examined how executive teams in some HVE firms make fast decisions, while those in other firms cannot, and whether faster decisions improve or worsen firm performance in such environments. HVE was defined as one where demand, competition, and technology changes so rapidly and discontinuously that the information available is often inaccurate, unavailable or obsolete. The implicit assumptions were thatit is hard to make fast decisions with inadequate information in HVE, and fast decisions may not be efficient and may result in poor firm performance.

Reviewing the prior literature on executive decision-making, Eisenhardt found several patterns, although none of these patterns were specific to high-velocity environments. The literature suggested that in the interest of expediency, firms that make faster decisions obtain input from fewer sources, consider fewer alternatives, make limited analysis, restrict user participation in decision-making, centralise decision-making authority, and have limited internal conflicts. However, Eisenhardt contended that these views may not necessarily explain how decision makers make decisions in high-velocity environments, where decisions must be made quickly and with incomplete information, while maintaining high decision quality.

To examine this phenomenon, Eisenhardt conducted an inductive study of eight firms in the personal computing industry. The personal computing industry was undergoing dramatic changes in technology with the introduction of the UNIX operating system, RISC architecture, and 64KB random access memory in the 1980s, increased competition with the entry of IBM into the personal computing business, and growing customer demand with double-digit demand growth, and therefore fit the profile of the high-velocity environment. This was a multiple case design with replication logic, where each case was expected to confirm or disconfirm inferences from other cases. Case sites were selected based on their access and proximity to the researcher, however, all of these firms operated in the high-velocity personal computing industry in California’s Silicon Valley area. The collocation of firms in the same industry and the same area ruled out any ‘noise’ or variance in dependent variables (decision speed or performance) attributable to industry or geographic differences.

The study employed an embedded design with multiple levels of analysis: decision (comparing multiple strategic decisions within each firm), executive teams (comparing different teams responsible for strategic decisions), and the firm (overall firm performance). Data was collected from five sources:

Initial interviews with Chief Executive Officers . CEOs were asked questions about their firm’s competitive strategy, distinctive competencies, major competitors, performance, and recent/ongoing major strategic decisions. Based on these interviews, several strategic decisions were selected in each firm for further investigation. Four criteria were used to select decisions: the decisions must involve the firm’s strategic positioning, the decisions must have high stakes, the decisions must involve multiple functions, and the decisions must be representative of strategic decision-making process in that firm.

Interviews with divisional heads . Each divisional head was asked sixteen open-ended questions, ranging from their firm’s competitive strategy, functional strategy, top management team members, frequency and nature of interaction with team, typical decision-making processes, how each of the decisions were made, and how long it took them to make those decisions. Interviews lasted between one and a half and two hours, and sometimes extended to four hours. To focus on facts and actual events rather than respondents’ perceptions or interpretations, a ‘courtroom’ style questioning was employed, such as ‘When did this happen?’, ‘What did you do?’, etc. Interviews were conducted by two people, and the data was validated by cross-checking facts and impressions made by the interviewer and notetaker. All interview data was recorded, however notes were also taken during each interview, which ended with the interviewer’s overall impressions. Using a ‘24-hour rule’, detailed field notes were completed within 24 hours of the interview, so that some data or impressions were not lost to recall.

Questionnaires . Executive team members at each firm were asked tocomplete a survey questionnaire that captured quantitative data on the extent of conflict and power distribution in their firm.

Secondary data . Industry reports and internal documents such as demographics of the executive teams responsible for strategic decisions, financial performance of firms, and so forth, were examined.

Personal observation . Lastly, the researcher attended a one-day strategy session and a weekly executive meeting at two firms in her sample.

Data analysis involved a combination of quantitative and qualitative techniques. Quantitative data on conflict and power were analysed for patterns across firms/decisions. Qualitative interview data was combined into decision climate profiles, using profile traits (e.g., impatience) mentioned by more than one executive. For within-case analysis, decision stories were created for each strategic decision by combining executive accounts of the key decision events into a timeline. For cross-case analysis, pairs of firms were compared for similarities and differences, categorised along variables of interest such as decision speed and firm performance. Based on these analyses, tentative constructs and propositions were derived inductively from each decision story within firm categories. Each decision case was revisited to confirm the proposed relationships. The inferred propositions were compared with findings from the existing literature to examine differences, and to generate new insights from the case findings. Finally, the validated propositions were synthesised into an inductive theory of strategic decision-making by firms in high-velocity environments.

Inferences derived from this multiple case research contradicted several decision-making patterns expected from the existing literature. First, fast decision-makers in high-velocity environments used more information, and not less information as suggested by the previous literature. However, these decision-makers used more real-time information—an insight not available from prior research—which helped them identify and respond to problems, opportunities, and changing circumstances faster. Second, fast decision-makers examined more—not fewer—alternatives. However, they considered these multiple alternatives in a simultaneous manner, while slower decision-makers examined fewer alternatives in a sequential manner. Third, fast decision-makers did not centralise decision-making or restrict inputs from others as the literature suggested. Rather, these firms used a two-tiered decision process in which experienced counsellors were asked for inputs in the first stage, followed by a rapid comparison and decision selection in the second stage. Fourth, fast decision-makers did not have less conflict—as expected from the literature—but employed better conflict resolution techniques to reduce conflict and improve decision-making speed. Finally, fast decision-makers exhibited superior firm performance by virtue of their built-in cognitive, emotional, and political processes that led to rapid closure of major decisions.

Positivist case research exemplar

Case research can also be used in a positivist manner to test theories or hypotheses. Such studies are rare, but Markus (1983) [6] provides an exemplary illustration in her study of technology implementation at the pseudonymous Golden Triangle Company (GTC). The goal of this study was to understand why a newly implemented financial information system (FIS)—intended to improve the productivity and performance of accountants at GTC—was supported by accountants at GTC’s corporate headquarters, but resisted by divisional accountants at GTC branches. Given the uniqueness of the phenomenon of interest, this was a single-case research study.

To explore the reasons behind user resistance of FIS, Markus posited three alternative explanations:

System-determined theory : The resistance was caused by factors related to an inadequate system, such as its technical deficiencies, poor ergonomic design, or lack of user friendliness.

People-determined theory : The resistance was caused by factors internal to users, such as the accountants’ cognitive styles or personality traits that were incompatible with using the system.

Interaction theory : The resistance was not caused not by factors intrinsic to the system or the people, but by the interaction between the two set of factors. Specifically, interaction theory suggested that the FIS engendered a redistribution of intra-organisational power, and accountants who lost organisational status, relevance, or power as a result of FIS implementation resisted the system while those gaining power favoured it.

In order to test the three theories, Markus predicted alternative outcomes expected from each theoretical explanation and analysed the extent to which those predictions matched with her observations at GTC. For instance, the system-determined theory suggested that since user resistance was caused by an inadequate system, fixing the technical problems of the system would eliminate resistance. The computer running the FIS system was subsequently upgraded with a more powerful operating system, online processing (from initial batch processing, which delayed immediate processing of accounting information), and a simplified software for new account creation by managers. One year after these changes were made, the resistant users were still resisting the system and felt that it should be replaced. Hence, the system-determined theory was rejected.

The people-determined theory predicted that replacing individual resistors or co-opting them with less resistant users would reduce their resistance toward the FIS. Subsequently, GTC started a job rotation and mobility policy, moving accountants in and out of the resistant divisions, but resistance not only persisted, but in some cases increased. In one instance, an accountant who was one of the system’s designers and advocates when he worked for corporate accounting started resisting the system after he was moved to the divisional controller’s office. Failure to realise the predictions of the people-determined theory led to the rejection of this theory.

Finally, the interaction theory predicted that neither changing the system nor the people (i.e., user education or job rotation policies) would reduce resistance until the power imbalance and redistribution from the pre-implementation phase was addressed. Before FIS implementation, divisional accountants at GTC felt that they owned all accounting data related to their divisional operations. They maintained this data in thick, manual ledger books, controlled others’ access to the data, and could reconcile unusual accounting events before releasing those reports. Corporate accountants relied heavily on divisional accountants for access to the divisional data for corporate reporting and consolidation. Because the FIS system automatically collected all data at the source and consolidated it into a single corporate database, it obviated the need for divisional accountants, loosened their control and autonomy over their division’s accounting data, and making their job somewhat irrelevant. Corporate accountants could now query the database and access divisional data directly without going through the divisional accountants, analyse and compare the performance of individual divisions, and report unusual patterns and activities to the executive committee, resulting in further erosion of the divisions’ power. Though Markus did not empirically test this theory, her observations about the redistribution of organisational power, coupled with the rejection of the two alternative theories, led to the justification of interaction theory.

Comparisons with traditional research

Positivist case research, aimed at hypotheses testing, is often criticised by natural science researchers as lacking in controlled observations, controlled deductions, replicability, and generalisability of findings—the traditional principles of positivist research. However, these criticisms can be overcome through appropriate case research designs. For instance, the problem of controlled observations refers to the difficulty of obtaining experimental or statistical control in case research. However, case researchers can compensate for such lack of controls by employing ’natural controls’. This natural control in Markus’ (1983) study was the corporate accountant who was one of the system advocates initially, but started resisting it once he moved to the controlling division. In this instance, the change in his behaviour may be attributed to his new divisional position. However, such natural controls cannot be anticipated in advance, and case researchers may overlook them unless they are proactively looking for such controls. Incidentally, natural controls are also used in natural science disciplines such as astronomy, geology, and human biology—for example, waiting for comets to pass close enough to the earth in order to make inferences about comets and their composition.

Third, the problem of replicability refers to the difficulty of observing the same phenomenon considering the uniqueness and idiosyncrasy of a given case site. However, using Markus’ three theories as an illustration, a different researcher can test the same theories at a different case site, where three different predictions may emerge based on the idiosyncratic nature of the new case site, and the three resulting predictions may be tested accordingly. In other words, it is possible to replicate the inferences of case research, even if the case research site or context may not be replicable.

Fourth, case research tends to examine unique and non-replicable phenomena that may not be generalised to other settings. Generalisability in natural sciences is established through additional studies. Likewise, additional case studies conducted in different contexts with different predictions can establish generalisability of findings if such findings are observed to be consistent across studies.

Lastly, British philosopher Karl Popper described four requirements of scientific theories: theories should be falsifiable, they should be logically consistent, they should have adequate predictive ability, and they should provide better explanation than rival theories. In case research, the first three requirements can be improved by increasing the degrees of freedom of observed findings—for example, by increasing the number of case sites, the number of alternative predictions, and the number of levels of analysis examined. This was accomplished in Markus’ study by examining the behaviour of multiple groups (divisional accountants and corporate accountants) and providing multiple (three) rival explanations. Popper’s fourth condition was accomplished in this study when one hypothesis was found to match observed evidence better than the two rival hypotheses.

• Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS Quarterly , 11(3), 369–386. ↵
• Yin, R. (1984). Case study research: Design and methods . London: Sage Publications. ↵
• Eisenhardt, K. M. (1989). Building theories from case research. Academy of Management Review , 14(4), 532–550 ↵
• Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research . New York: Aldine Pub Co. ↵
• Eisenhardt, K. M. (1989). Making fast strategic decisions in high-velocity environments. Academy of Management Journal , 32(3), 543–576. ↵
• Markus, M. L. (1983). Power, politics and MIS implementations. Communications of the ACM , 26(6), 430–444. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.2 Approaches to Research

Learning objectives.

By the end of this section, you will be able to:

• Describe the different research methods used by psychologists
• Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research
• Compare longitudinal and cross-sectional approaches to research
• Compare and contrast correlation and causation

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected. All of the methods described thus far are correlational in nature. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

Clinical or Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

Watch this CBC video about Krista's and Tatiana's lives to learn more.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

Over time, it has become clear that while Krista and Tatiana share some sensory experiences and motor control, they remain two distinct individuals, which provides invaluable insight for researchers interested in the mind and the brain (Egnor, 2017).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a precious amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this chapter: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway ( Figure 2.7 ).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall , for example, spent nearly five decades observing the behavior of chimpanzees in Africa ( Figure 2.8 ). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity , or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the chapter on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally ( Figure 2.9 ). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: People don't always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research . Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students ( Figure 2.10 ).

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research. In cross-sectional research , a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of studying a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals that make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) ( Figure 2.11 ).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increase over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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The Case Study as Research Method: A Practical Handbook

Qualitative Research in Accounting & Management

ISSN : 1176-6093

Article publication date: 21 June 2011

Scapens, R.W. (2011), "The Case Study as Research Method: A Practical Handbook", Qualitative Research in Accounting & Management , Vol. 8 No. 2, pp. 201-204. https://doi.org/10.1108/11766091111137582

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

This book aims to provide case‐study researchers with a step‐by‐step practical guide to “help them conduct the study with the required degree of rigour” (p. xi).

It seeks to “demonstrate that the case study is indeed a scientific method” (p. 104) and to show “the usefulness of the case method as one tool in the researcher's methodological arsenal” (p. 105). The individual chapters cover the various stages in conducting case‐study research, and each chapter sets out a number of practical steps which have to be taken by the researcher. The following are the eight stages/chapters and, in brackets, the number of steps in each stages:

Assessing appropriateness and usefulness (4).

Ensuring accuracy of results (21).

Preparation (6).

Selecting cases (4).

Collecting data (7).

Analyzing data (4).

Interpreting data (3).

Reporting results (4).

It is particularly noticeable that ensuring accuracy of results has by far the largest number of number of steps – 21 steps compared to seven or fewer steps in the other stages. This reflects Gagnon's concern to demonstrate the scientific rigour of case‐study research. In the forward, he explains that the book draws on his experience in conducting his own PhD research, which was closely supervised by three professors, one of whom was inclined towards quantitative research. Consequently, his research was underpinned by the principles and philosophy of quantitative research. This is clearly reflected in the approach taken in this book, which seeks to show that case‐study research is just as rigorous and scientific as quantitative research, and it can produce an objective and accurate representation of the observed reality.

There is no discussion of the methodological issues relating to the use of case‐study research methods. This is acknowledged in the forward, although Gagnon refers to them as philosophical or epistemological issues (p. xii), as he tends to use the terms methodology and method interchangeably – as is common in quantitative research. Although he starts (step 1.1) by trying to distance case and other qualitative research from the work of positivists, arguing that society is socially constructed, he nevertheless sees social reality as objective and independent of the researcher. So for Gagnon, the aim of case research is to accurately reflect that reality. At various points in the book the notion of interpretation is used – evidence is interpreted and the (objective) case findings have to be interpreted.

So although there is a distancing from positivist research (p. 1), the approach taken in this book retains an objective view of the social reality which is being researched; a view which is rather different to the subjective view of reality taken by many interpretive case researchers. This distinction between an objective and a subjective view of the social reality being researched – and especially its use in contrasting positivist and interpretive research – has its origins the taxonomy of Burrell and Morgan (1979) . Although there have been various developments in the so‐called “objective‐subjective debate”, and recently some discussion in relation to management accounting research ( Kakkuri‐Knuuttila et al. , 2008 ; Ahrens, 2008 ), this debate is not mentioned in the book. Nevertheless, it is clear that Gagnon is firmly in the objective camp. In a recent paper, Johnson et al. (2006, p. 138) provide a more contemporary classification of the different types of qualitative research. In their terms, the approach taken in this book could be described as neo‐empiricist – an approach which they characterise as “qualitative positivists”.

The approach taken in this handbook leaves case studies open to the criticisms that they are a small sample, and consequently difficult to generalise, and to arguments that case studies are most appropriate for exploratory research which can subsequently be generalised though quantitative research. Gagnon explains that this was the approach he used after completing his thesis (p. xi). The handbook only seems to recognise two types of case studies, namely exploratory and raw empirical case studies – the latter being used where “the researcher is interested in a subject without having formed any preconceived ideas about it” (p. 15) – which has echoes of Glaser and Strauss (1967) . However, limiting case studies to these two types ignores other potential types; in particular, explanatory case studies which are where interpretive case‐study research can make important contributions ( Ryan et al. , 2002 ).

This limited approach to case studies comes through in the practical steps which are recommended in the handbook, and especially in the discussion of reliability and validity. The suggested steps seem to be designed to keep very close to the notions of reliability and validity used in quantitative research. There is no mention of the recent discussion of “validity” in interpretive accounting research, which emphasises the importance of authenticity and credibility and their implications for writing up qualitative and case‐study research ( Lukka and Modell, 2010 ). Although the final stage of Gagnon's handbook makes some very general comments about reporting the results, it does not mention, for example, Baxter and Chua's (2008) paper in QRAM which discusses the importance of demonstrating authenticity, credibility and transferability in writing qualitative research.

Despite Gagnon's emphasis on traditional notions of reliability and validity the handbook provides some useful practical advice for all case‐study researchers. For example, case‐study research needs a very good research design; case‐study researchers must work hard to gain access to and acceptance in the research settings; a clear strategy is needed for data collection; the case researcher should create field notes (in a field notebook, or otherwise) to record all the thoughts, ideas, observations, etc. that would not otherwise be collected; and the vast amount of data that case‐study research can generate needs to be carefully managed. Furthermore, because of what Gagnon calls the “risk of mortality” (p. 54) (i.e. the risk that access to a research site may be lost – for instance, if the organisation goes bankrupt) it is crucial for some additional site(s) to be selected at the outset to ensure that the planned research can be completed. This is what I call “insurance cases” when talking to my own PhD students. Interestingly, Gagnon recognises the ethical issues involved in doing case studies – something which is not always mentioned by the more objectivist type of case‐study researchers. He emphasises that it is crucial to honour confidentiality agreements, to ensure data are stored securely and that commitments are met and promises kept.

There is an interesting discussion of the advantages and disadvantages of using computer methods in analysing data (in stage 6). However, the discussion of coding appears to be heavily influenced by grounded theory, and is clearly concerned with producing an accurate reflection of an objective reality. In addition, Gagnon's depiction of case analysis is overly focussed on content analysis – possibly because it is a quantitative type of technique. There is no reference to the other approaches available to qualitative researchers. For example, there is no mention of the various visualisation techniques set out in Miles and Huberman (1994) .

To summarise, Gagnon's book is particularly useful for case‐study researchers who see the reality they are researching as objective and researcher independent. However, this is a sub‐set of case‐study researchers. Although some of the practical guidance offered is relevant for other types of case‐study researchers, those who see multiple realities in the social actors and/or recognise the subjectivity of the research process might have difficulty with some of the steps in this handbook. Gagnon's aim to show that the case study is a scientific method, gives the handbook a focus on traditional (quantitatively inspired) notions rigour and validity, and a tendency to ignore (or at least marginalise) other types of case study research. For example, the focus on exploratory cases, which need to be supplemented by broad based quantitative research, overlooks the real potential of case study research which lies in explanatory cases. Furthermore, Gagnon is rather worried about participant research, as the researcher may play a role which is “not consistent with scientific method” (p. 42), and which may introduce researcher bias and thereby damage “the impartiality of the study” (p. 53). Leaving aside the philosophical question about whether any social science research, including quantitative research, can be impartial, this stance could severely limit the potential of case‐study research and it would rule out both the early work on the sociology of mass production and the recent calls for interventionist research. Clearly, there could be a problem where a researcher is trying to sell consulting services, but there is a long tradition of social researchers working within organisations that they are studying. Furthermore, if interpretive research is to be relevant for practice, researchers may have to work with organisations to introduce new ideas and new ways of analysing problems. Gagnon would seem to want to avoid all such research – as it would not be “impartial”.

Consequently, although there is some good practical advice for case study researchers in this handbook, some of the recommendations have to be treated cautiously, as it is a book which sees case‐study research in a very specific way. As mentioned earlier, in the Forward Gagnon explicitly recognises that the book does not take a position on the methodological debates surrounding the use of case studies as a research method, and he says that “The reader should therefore use and judge this handbook with these considerations in mind” (p. xii). This is very good advice – caveat emptor .

Ahrens , T. ( 2008 ), “ A comment on Marja‐Liisa Kakkuri‐Knuuttila ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 291 ‐ 7 , Kari Lukka and Jaakko Kuorikoski.

Baxter , J. and Chua , W.F. ( 2008 ), “ The field researcher as author‐writer ”, Qualitative Research in Accounting & Management , Vol. 5 No. 2 , pp. 101 ‐ 21 .

Burrell , G. and Morgan , G. ( 1979 ), Sociological Paradigms and Organizational Analysis , Heinneman , London .

Glaser , B.G. and Strauss , A.L. ( 1967 ), The Discovery of Grounded Theory: Strategies for Qualitative Research , Aldine , New York, NY .

Johnson , P. , Buehring , A. , Cassell , C. and Symon , G. ( 2006 ), “ Evaluating qualitative management research: towards a contingent critieriology ”, International Journal of Management Reviews , Vol. 8 No. 3 , pp. 131 ‐ 56 .

Kakkuri‐Knuuttila , M.‐L. , Lukka , K. and Kuorikoski , J. ( 2008 ), “ Straddling between paradigms: a naturalistic philosophical case study on interpretive research in management accounting ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 267 ‐ 91 .

Lukka , K. and Modell , S. ( 2010 ), “ Validation in interpretive management accounting research ”, Accounting, Organizations and Society , Vol. 35 , pp. 462 ‐ 77 .

Miles , M.B. and Huberman , A.M. ( 1994 ), Qualitative Data Analysis: A Source Book of New Methods , 2nd ed. , Sage , London .

Ryan , R.J. , Scapens , R.W. and Theobald , M. ( 2002 ), Research Methods and Methodology in Finance and Accounting , 2nd ed. , Thomson Learning , London .

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Case Study Research: Methods and Designs

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

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

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

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

What Is The Case Study Method?

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

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

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

Here are the primary features of case study research:

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

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

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

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

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

Let’s look now at case study methodology.

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

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

2. Semi-structured Interview

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

3. Focus Group Interview

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

4. Participant Observation

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

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

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

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

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

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

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

1. Illustrative case study

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

2. Cumulative case study

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

3. Exploratory case study

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

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

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

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

A Longitudinal Mixed Methods Case Study Investigation of the Academic, Athletic, Psychosocial and Psychological Impacts of Being of a Sport School Student Athlete

• Original Research Article
• Open access
• Published: 18 April 2024

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• Ffion Thompson   ORCID: orcid.org/0000-0002-5515-7633 1 , 2 ,
• Fieke Rongen 3 ,
• Ian Cowburn 1 &
• Kevin Till 1 , 4  

Sport schools are popular environments for simultaneously delivering education and sport to young people. Previous research suggests sport school involvement to have impact (i.e. the positive/negative, intended/unintended and long/short-term outcomes, results and effects) on student athlete’s holistic (i.e. academic, athletic, psychosocial and psychological) development. However, previous research is limited by (1) cross-sectional methods, (2) limited multidimensional assessments, (3) lack of consideration for athlete characteristics (e.g. sex) and (4) failure to evaluate how sport school features affect student-athlete impacts.

The study, using a mixed methods case study approach, aims to (1) longitudinally evaluate the impact of sport school involvement on the holistic development of student athletes, (2) evaluate the impact on holistic development by student-athlete characteristics and (3) explore the features and processes of the sport–school programme that drive/facilitate holistic impacts.

A longitudinal mixed methods design was employed across one full academic school year (33 weeks). Six data-collection methods (i.e. online questionnaire, physical fitness testing battery, academic assessment grades, log diaries, field notes/observation and timeline diagram/illustration) were used to assess the academic, athletic, psychosocial and psychological impacts for 72 student athletes from one sport school in the United Kingdom (UK).

Student athletes developed positive long-term holistic overall impacts (i.e. academically, athletically and personally), including maintaining stable and relatively high levels of sport confidence, academic motivation, general recovery, life skills, resilience and friends, family and free time scores. Despite positive impacts, juggling academic and sport workload posed challenges for student athletes, having the potential to lead to negative holistic impacts (e.g. fatigue, stress and injury). Positive and negative impacts were linked to many potential features and processes of the sport school (e.g. academic and athletic support services versus insufficient training load build-up, communication, coordination, flexibility and planning). Furthermore, when considering student-athlete characteristics, females had lower sport confidence, higher general stress and body image concerns and less general recovery than males and student athletes who played sport outside the school had lower general recovery.

Conclusions

This mixed method, longitudinal study demonstrated sport school involvement resulted in many positive academic (e.g. good grades), athletic (e.g. fitness development), psychosocial (e.g. enhanced confidence) and psychological (e.g. improved resilience) impacts attributed to the academic and athletic support services provided. However, juggling heavy academic and athletic workloads posed challenges leading to negative impacts including fatigue, pressure, stress and injury. Furthermore, holistic impacts may be sex dependent and further support may be required for female student athletes in sport school environments. Overall, these findings demonstrate the complex nature of combining education and sport commitments and how sport schools should manage, monitor and evaluate the features of their programme to maximise the holistic impacts of sport–school student athletes.

Avoid common mistakes on your manuscript.

1 Introduction

In response to the potential negative consequences associated with the intensification of youth sports programmes [e.g. 1 , 2 ] and the drive for a more holistic approach to youth athlete development [e.g. 3 ], there has been a cultural shift towards combining sport and education in supportive environments to appropriately prepare individuals for working life if they do not become professional athletes [ 4 ]. This type of approach is referred to as a ‘dual career’ (DC) approach (i.e. combining sporting pursuits alongside education or vocational endeavours). A DC approach has long been evident in the USA, where collegiate athletes pursue university education alongside elite performance in Olympic sports or before entering the draft system for professional sports (Ryba et al. 2015). However, it has recently become more prominent in the United Kingdom (UK, [ 5 ]). Morris et al. [ 6 ] further distinguishes between different dual career development environments (DCDEs; i.e. environments that support DC approaches) based on the different structures and approaches used to provide both athlete development and academic support.

One example of a DCDE that aims to cater for youth athletes’ holistic development is a sports school. Sport schools are a key environment for DC development in many countries and are considered an increasingly integral part of a nations’ elite sport performance strategy [ 7 ]. Sport schools aim to combine sport and education to offer student athletes considerable academic flexibility (e.g. adaptation of school and training schedules and lighter load by one subject) and athletic support (e.g. high-quality coaches and physiotherapy) [ 8 ]. Recently, Morris et al. [ 6 ] categorised two types of sport schools: sport-friendly and elite. Both sport-friendly schools and elite sport schools are situated in lower and upper general and vocational secondary education (i.e. International Standard Classification of Education level 2–5). However, unlike a sport-friendly school, an elite sport school has formal communication with a sport federation, often receiving funding [ 6 ].

While a DC approach holds promise for enhancing the development of school-aged athletes, it brings forth various potential challenges. These challenges include managing academic study and training alongside competition schedules, dealing with fatigue/lack of sleep and being forced to make personal sacrifices [ 9 , 10 , 11 ]. Consequently, despite the intention of sport schools to provide a platform for athletes to balance sport and education, the reality is that they introduce heightened demands, potentially subjecting student athletes to risks of burnout and injury, as identified in previous research on intensified youth sports (e.g. [ 1 , 12 ]).

The process of youth athletic development within a school is complex, as athletes experience psychological, physical and psychosocial growth in an environment where they are navigating competing sport, academic and social demands [ 13 ]. Consequently, sport school involvement will impact (i.e. the positive/negative, intended/unintended and long/short-term outcomes, results and effects) an individual’s holistic development across academic, athletic, psychosocial and psychological dimensions [ 3 , 14 ]. Recognizing the diverse and extensive potential impacts of DCDEs (such as sport schools), aligns with the overarching idea of examining student athletes holistically. This comprehensive perspective is vital in understanding and navigating the multifaceted impacts of sport schools on the developmental trajectory of individuals [ 3 , 14 ].

Increasingly, research has explored such impacts on holistic athlete development. A recent mixed methods systematic review [ 8 ] highlighted there are a multitude of immediate, short- and long-term positive (e.g. physical development, more stable levels of general health and well-being, status/popularity and life skills) and negative (e.g. lower higher education attainment, limited experience with ordinary life outside of competitive sport, high number of injuries and performance pressure) impacts associated with the athletic, academic, psychosocial and psychological development of sport school student athletes. However, this systematic review identified several limitations within the current evidence base, including: (1) limited research examining how sport-friendly school features are operationalised in different contexts (e.g. UK), (2) a failure to evaluate multi-dimensional domains of athlete impact, often focussing on one or two dimensions and (3) limited research evaluating how features affect athlete impacts (i.e. causal relationship between the characteristics and features of sport school and holistic athlete impacts).

Subsequently, two studies [ 15 , 16 ] assessed the impacts of a UK sport-friendly school on student athletes across all four domains of holistic athlete development (i.e. academic, athletic, psychosocial and psychological). Overall, the findings of both studies demonstrated a multitude of positive impacts associated with being a sport school student athlete but, also, impacts of concern. However, both studies were cross-sectional in nature (i.e. use of a single moment of measurement), where exposure and impacts were simultaneous. Consequently, these studies oppose the nature of ‘transition’ as a process and the dynamic nature of sport-friendly school environments. Therefore, longitudinal research designs are required to investigate student-athlete development or changes over time. Additionally, although Thompson et al. [ 15 , 16 ] provided a general overview of the features and multiple possible impacts of sport school involvement, it is important to note that not every athlete experienced every potential impact. Instead, impacts varied across individuals and were driven by their individual characteristics and experiences of sport school features over time. Sport schools would benefit from an approach that is aware of individual differences and how they may impact a student athlete’s journey. Accordingly, it is important to explore the specificity of athlete characteristics/variables (e.g. biological sex) as holistic impacts may vary considerably depending upon an athlete's sex, sport requirements and boarding status [ 17 , 18 , 19 ].

Finally, given the complex and dynamic nature of DC environments [ 20 ], where student athletes have to interact with coaches, programme culture and practices, research needs to explore the features and processes (i.e. the context-individual interactions) of sport-friendly school programmes that drive and facilitate positive and negative holistic impacts described by Thompson and colleagues [ 15 , 16 ]. Moreover, within the UK, there are substantially more sport-friendly schools, with only one identified example of an elite sport school found in Scotland [ 5 ]. Sport-friendly schools in the UK tend to be more independent than the systemic approach in other countries (e.g. Germany and Sweden [ 21 ]). In the UK, the development of a sport-friendly school is primarily a matter for individual schools and is often pursued as part of a strategy to create a distinct identity. As a result, it is important to investigate the individual context of a sport-friendly school within the UK as a case study.

Based on the above, this study, using a mixed methods longitudinal case study design, aims to (1) longitudinally evaluate the impact of sport-friendly school involvement on the holistic (i.e. academic, athletic, psychological and psycho-social) development of student athletes, (2) evaluate the impact on holistic development by athlete characteristics (i.e. sex, boarding status and external sport involvement) and (3) explore the features and processes of the sport-friendly school programme that drive/facilitate positive and negative holistic impacts.

2.1 Research Approach

This study was aligned with and guided by a critical realist (CR) perspective. In line with North’s [ 22 ] perspective on CR, this study was guided by the principles of developing theory (i.e. first understanding of sport schools impacts, then, second, developing an understanding of ‘how,’ ‘why’, ‘what’ and ‘for whom’). As such, the researcher first engaged in contextual description (aims 1 and 2), then, second, started to develop an understanding/explanation of how observed patterns were generated (aim 3). To help achieve the study aims, this study adopted a concurrent mixed methods approach (i.e. qualitative and quantitative data collected simultaneously [ 23 ]). This design aims to create mutually exclusive sets of data that inform each other [ 24 ]. Furthermore, the qualitative and quantitative data were analysed separately but then integrated to cross-validate findings. Finally, in line with the CR stance of establishing ‘how’, ‘why’, ‘what’ and ‘for whom’, Pawson and Tilley's [ 25 ] and Yin’s [ 26 ] guiding principles for an explorative case study approach were used.

2.2 Positionality of the Researchers

It is also important to acknowledge the collective roles of the researchers’ autobiographies, values and beliefs in describing, designing and interpreting the findings [ 27 ]. To acknowledge this, we consciously outline them to help appreciate and evaluate the results in nuanced ways [ 28 ]. The first author, F.T., collected the data and was lead on the analysis and writing. As the school’s lead strength and conditioning (SC) coach and a previous student athlete at a different sport-friendly school for 5 years, this would have inevitably shaped the primary researchers’ conceptions and influenced the study’s initial framing, design and analysis. Furthermore, the collective experiences of the remainder of the research team will have contributed to the interpretation of the data and shaping of the results. Combined, K.T., F.R. and I.C. have over 30 years of research and applied experience within athlete development systems.

2.3 Context of Study

One sport-friendly school (pseudonym ‘Nunwick High’) was selected for the study based on Morris et al.’s [ 6 ] definition of a sport-friendly school. The selection of ‘Nunwick High’ was information-oriented and opportunistic. ‘Nunwick High’ has 8 years of experience providing DC support through a performance sport pathway embedded within a UK independent school. ‘Nunwick High’ has eight performance sports as part of its performance programmes: athletics, basketball, cricket, football, hockey, netball, rugby and swimming, targeted at year groups 7–13 (aged 12–18 years). Each student athlete enrolled on ‘Nunwick High’ performance sport programme receives a place to study, train and, in some cases, live during their lower and upper secondary school years, including access to learning facilities, a sport science centre, a sport treatment centre, sport facilities, accommodation buildings and a canteen all in one proximity (single campus). Based on the information above, ‘Nunwick High’ represented an established and mature environment that should be a rich source of information.

2.4 Participants

Participants had to meet the following inclusion criteria: participate as a student athlete in one of the performance sport programmes within ‘Nunwick High’ and be aged 16 or above (years 12–13). Years 12–13 were chosen specifically, as during this stage student athletes are transitioning to a more intense and structured period of athletic development [ 29 , 30 ], and increased educational demands, with the consequence that the management of their DC, is a distinct concern. A total of 72 student athletes (mean age 17.29 ± 0.52 years, 48 male and 24 female) participated in the study. At baseline (T1) the student athletes had been attending and competing at ‘Nunwick High’ for an average of 1.2 ± 1.5 years (range from 2 weeks to 7 years). Out of the 72 student athletes, 31 were boarders (i.e. live at the school) and 41 were non-boarders, 31 played sport externally to the sport-friendly school and 41 only played sport for the sport-friendly school, representing the following sport: athletics ( n  = 4), cricket ( n  = 4), hockey ( n  = 12), netball ( n  = 9), football ( n  = 18), rugby ( n  = 15) and basketball ( n  = 10).

2.5 Study Design

A longitudinal mixed methods case study design was employed across one full academic school year (33 weeks). To engage in a comprehensive and holistic investigation of the impacts of being a sport-friendly school student athlete and the features and processes that drive/facilitate such impacts, six data-collection methods were utilised: (1) online questionnaire, (2) physical fitness testing battery, (3) academic assessment grades, (4) log diaries, (5) field notes/observation and (6) timeline diagram/illustration.

The online questionnaire occurred over five data collection periods (Q1, September; Q2, November/December; Q3, February; Q4, March; and Q5, May). The physical fitness testing battery occurred over three data collection periods (PFT1, September; PFT2, December; and PFT3, March/April). The academic assessment grades occurred across four data collection periods (A1, October; A2, December; A3, February; and A4, June). The log diary occurred over four data collection periods (L1, October; L2, December; L3, January; and L5, March). The observational research was ongoing throughout the whole academic year (33 weeks). Finally, the timeline diagram/illustration was collected once at the end of the academic year. Figure  1 provides an overview of the data collection timeline. The university sub-ethics committee granted this study (ref. 86728) with online informed assent and parental written consent obtained.

Overview of data collection points at the sport-friendly school

2.6 Measures

2.6.1 online questionnaire.

Data collection involved participants completing an online questionnaire (predicted completion time, 29 min) that provided a multi-dimensional assessment of holistic athlete impacts identified in previous literature [ 8 , 15 , 16 ]. The online questionnaire comprised of 12 domains (i.e. academic and sport workload, difficulty balancing sport and academics, academic support and satisfaction, injury and illness, rest and recovery, body image, family, free time and friends; sport competence; sport confidence; life skills, dual career motivation and resilience) as presented in Table  1 . The questionnaire was conducted in a quiet room, and student athletes were allowed sufficient breaks when required and were allowed to return to the questionnaire at a later time within the same day. Further, open-ended questions were used to help expand on responses to close-ended questions [ 31 ], providing further information on the features and processes that drove/facilitated specific impacts. All questionnaires were collected across all timepoints (T1–T5) apart from The Life Skills Scale for Sport (LSSS) questionnaire which was added in from T2 as the LSSS requires participants to rate how much their environmental exposure has taught them to perform the skills listed within the questionnaire and a baseline value was not appropriate. Completion rates: 97% for Q1, 90% for Q2, 94%for Q3, 93% for Q4 and 99% Q5.

2.6.2 Academic Assessments Grades

To assess educational attainment, termly academic subject assessment grades were extracted from the school administrative system. As all student athletes were in years 12–13, and grades were provided in the UK national curriculum grading format for Advanced level (A-level) and Business and Technology Education Council (BTEC) qualifications. To adequately compare BTEC and A-level grades, in addition to statistical purposes, academic assessment grades were converted to a number using a school grades translation matrix in Table  2 (similar to [ 58 ]). After conversion, an average of each individual’s subject score was calculated to get one overall academic assessment score for each student athletes. Completion rates: 96% for A1, 90% for A2, 94% for A3 and 94% for A4.

2.6.3 Physical Fitness Testing Battery

To assess physical development, a fitness testing battery which included; lower-body power, strength, speed and cardiovascular fitness tests were conducted in line with previous studies [ 59 ]. Speed was reported at 10 and 40 m distances [ 60 ], lower-body power was reported using countermovement jump (CMJ) height (m) and strength was reported using the isometric mid-thigh pull (IMTP) [ 61 , 62 ,– 63 ] peak force (kg) and relative peak force (kg −1 ) measures. The fitness testing battery was conducted over 2 weeks. In week 1, subjects performed measures of strength via the IMTP and power via the CMJ. In week 2, field-based measures of 10–40 m sprints were performed to measure acceleration and max velocity. On all testing days, the test causing the greatest strain on the neuromuscular system was performed first to enhance the reliability of all maximal testing procedures [ 64 ]. Completion rates: 97% for PFT1, 96% for PFT2 and 97% for PFT3.

2.6.4 Log Diary

Student athletes were asked to fill in a log diary across four timepoints in the academic year consisting of open-ended questions that explored the positive and negative holistic impacts and any features and processes of the sport-friendly school that caused, attributed or drove these impacts. Open-ended questions allowed the respondents to express opinions without being influenced by the researcher [ 65 ]. For example, student athletes were asked to reflect on the last month and outline the positive and negative impacts they had experienced on their athletic/physical, academic, psychosocial and psychological development. Furthermore, open-ended questions allowed respondents to include more contextual information, giving more feedback on the features and processes of the sport-friendly school programme that drove/facilitated positive student athlete holistic impacts [ 31 ]. For example, student athletes were asked to outline what caused, attributed or drove these impacts/outcomes to happen (e.g. what characteristics, features or processes?). Completion rates: 24% for L1, 42% for L2, 38% for L3 and 38% for L4.

2.6.5 Observational Field Notes

To achieve contextual sensitivity, emphasis was placed on participant observation of the daily lives of the student athletes in their natural setting as an essential method of data collection [ 66 ]. Over the 33-week academic term, the primary researcher completed observational field notes throughout each academic day relating to objective observations and conversations and subjective reflections of the actions, behaviours and interactions observed at ‘Nunwick High’ [ 67 , 68 ]. Observations were made from a holistic viewpoint, generally attuned to the broader context of the school, including context-individual interactions and processes between sport school features and holistic athlete impacts. Notes were also taken on specific coaching actions and behaviours, individual participant experiences and the interactions observed between student athletes, coaches and teachers. The observations enhanced the researcher’s understanding of the ‘Nunwick High’ context and student athletes’ holistic development [ 68 ].

2.6.6 Timeline Diagram/Illustration

At the end of the academic year, a convenience sample of 15 participants (mixture of sport and sex) were chosen to complete a timeline diagram/illustration visualising and displaying their personal experiences of the fluctuations in academic stress and sport workload across the academic year. Within the group, each individual was asked to draw a graph representing their academic stress and sport workload across different periods of the academic year (term 1 to term 6). In addition, they were asked to highlight the key academic assessment periods across this time period. After the student athletes completed their timeline, they described and discussed their diagrams as a group, providing personal explanations and rationale for the timelines they had drawn with the primary researcher who wrote down additional notes. Successively, findings (from both quantitative and qualitative data) were fed back to participants and an opportunity was given for participants to elaborate and provide more contextual information on the findings. The data were then integrated as part of the results, complementing and enriching the data generated in the TA [ 69 ]. Although the researcher made sure to keep the discussion on topic, as well as reiterate that there were no right or wrong viewpoints [ 70 ], the direction of the discussion was driven by the student athletes. This form of research has been used in previous studies [e.g., 71 ] and provided student athletes with a sense of engagement and ownership over the research process.

2.7 Data Analysis

2.7.1 aims 1 and 2 data analysis, 2.7.1.1 quantitative analysis.

To address research aims 1 and 2, two generalised mixed models were conducted using R (Version 4.1.3). The first model (addressing aim 1) assessed the changes in impacts across the school year (33 weeks). The change in score of each holistic variable was used as the dependent variable, with time (i.e. Q1–Q5, PFT1–PFT3 and A1–A3) added as the fixed factor. Individual participants and sport were used as covariates (random factors). The second model (addressing aim 2) considered the specificity of athlete characteristics. Each holistic variable was used as the dependent variable, with biological sex (female versus male), living status (border versus non-boarder) and external sport commitment (a student athlete who played sport externally to the sport-friendly school versus a student athlete who only played sport for the sport-friendly school) added as fixed factors. Individual participants and sport were again used as covariates (random factors). The p -value was set at 0.05. Injury and illness incidence rates were processed separately using Excel (Microsoft Office 2021) and described using percentages with frequencies due to being bi-nominal data.

2.7.1.2 Qualitative Analysis

Alongside the quantitative data, qualitative data were used to evaluate the impacts of sport-friendly school involvement. The data was coded using a largely deductive approach [ 72 ]. First, during the preparation phase, qualitative data was organised and managed into categories to be analysed together (i.e. log diaries, open-ended questionnaires and observation field notes and timeline diagram/illustration transcripts) and the primary researcher obtained a sense of the whole data through reading the transcripts several times. Next, during the organisational phase, data were generated through coding [ 73 ]. Our coding approach was deductive in nature as most codes were generated through the available systematic review [ 8 ] and the online questionnaire items (refer to Table  1 ). Inductive coding was used as new themes specific to the holistic impacts of student athletes and any specificity of athlete characteristics were identified during the coding process.

2.7.1.3 Triangulation

Given that quantitative and qualitative methods were used to investigate the same holistic student athlete impacts, the data for analysis were compatible for integration using the process of triangulation resulting in the creation of a number of themes [ 74 , 75 ]. As part of this process, the primary researcher compared the findings from the quantitative and qualitative analysis and considered where the findings from each method agree (converge), offer complementary information on the same issue (complementarity) or appear to contrast each other (discrepancy or dissonance) [ 75 ]. Subsequently, the assessment of convergency, complementary and discrepancy were discussed among the authors to (1) clarify interpretations of the findings and (2) determine the degree of agreement among researchers on triangulated findings [ 75 ]. Finally, after refining the themes, the primary researcher defined and named the themes.

2.7.2 Aim 3 Data Analysis

Aim 3 aimed to provide a more explanatory (i.e. seeking to explain the causes of phenomena) approach to research [ 76 ]. As such, Fryers’ [ 77 ] five-step CR approach to thematic analysis (TA) was used to analyse the qualitative data (i.e. log diaries, open-ended questionnaires, observation field notes and timeline diagram/illustration transcripts). As part of the first stage of TA, the primary author clearly outlined and refined the research aim and objective (i.e. explore the features and processes of the sport-friendly school programme that drive/facilitate positive and negative holistic impacts). In the second stage, the primary author immersed herself in the data by reading and re-reading texts to familiarise themselves with the findings and make notes on the initial thoughts and questions. Following familiarisation, stage three consisted of applying, developing and reviewing codes (step 3 [ 77 ]). Descriptive codes were applied to segments of qualitative text that were considered relevant to the research aims (e.g. features and processes of ‘Nunwick High’). Following the development of codes, step 4 entailed grouping all codes into themes [ 77 ]. Explanations were developed to suggest how particular features and processes of ‘Nunwick High’ produce the holistic impacts evidenced in the data (i.e., aims 1). Finally, within stage five [ 77 ], reflections on the overall analysis were discussed and reviewed among the research team, with a particular focus on checking the plausibility of the explanations against pre-existing evidence (i.e. in the data as well as existing theory).

2.8 Establishing Research Rigour

Following recent recommendations, Hirose and Creswell’s [ 78 ] six core quality criteria for mixed methods studies are proposed as useful in judging the rigour of the current study. First, the authors have outlined a clear rationale for the use and appropriateness of mixed methods methodology in this study (i.e. criteria 1). Second, throughout the design included specific quantitative (e.g. What are the impacts of sport school involvement on the physical development of athletes?), qualitative (e.g. Can you tell us about the balance between sport and school?) and mixed methods (e.g. How were changes in personal development brought about by the environment?) questions (i.e. criteria 2). Third, it has been clearly outlined which elements of data collection resulted in quantitative and qualitative data, as well as how each type of data was analysed. Furthermore, quantitative data are clearly presented in Table  3 , and qualitative data have been represented in direct quotes throughout the results (i.e. criteria 3). The mixed methods research design has been identified along with a diagram of data-collection moments (i.e. criterion 4). Fifth, the authors have clearly outlined how data-integration has taken place, this is then evidenced throughout the results and Fig.  3 captures a display of how findings have been integrated (i.e. criterion 5). The integration of data resulted in added value, as it allowed the authors to highlight similarities and differences between quantitative and qualitative findings throughout the results, providing a more nuanced understanding of the holistic impact of sport school involvement. Furthermore, the notion of meta-inferences (i.e. inferences that draw on both quantitative, qualitative and transcend both databases or what does it all mean together), fit very well with the CR stance of the study and the analytical process employed to formulate initial theories (i.e. explanations) as to how things worked within this sport school context (i.e. criterion 6). Finally, further in line with the CR philosophical underpinnings and aims [ 79 ], we also invite the reader to judge the findings presented in terms of their plausibility (i.e. do the offered explanations make sense, both in light of the presented data and the existing research literature) and utility (i.e. how well the research account offers predictions for likely outcomes and can be used to guide practical actions in the real world).

In line with the study’s aims, the results are presented in three higher-order themes: (3.1) longitudinal investigation of student-athlete holistic impacts, (3.2) specificity of athlete characteristics and (3.3) features and processes of the sport-friendly school program (i.e. what worked for whom and how).

3.1 Longitudinal Investigation of Student-Athlete Holistic Impacts

The triangulated holistic student-athlete impacts are presented below. Table 3 presents the quantitative statistical results for each impact at each timepoint. Furthermore, differences in student-athlete characteristics (i.e. sex, boarding and external sport) are presented. The descriptions below triangulate the quantitative and qualitative data within key themes to present the longitudinal holistic impacts.

3.1.1 Fluctuations in Academic and Sports Workload Over-time Culminate in a Variety of Impacts

Table 3 presents how sports training, competition frequency and the number of rest days changed across the academic year. Sport training and competition frequency significantly decreased in March and May (1.57–2.22 h/week and 0.49–1.14 competitions/week) compared with September–February (8.84–10.23 h/week and 1.86–2.26 competitions/week). Significantly more rest days were experienced during May (~ 2.00 per week) than in the other periods. This finding is supported by the student athletes’ timeline diagrams/illustrations whereby most student athletes’ sport workload was typically high across terms 1–4, with a drop off in terms 5 and 6. In contrast, for summer sports such as cricket and athletics, the highest sport workload appeared in term 6 when they were also doing their final academic examinations, as exemplified by a summer sport student athlete when talking about term 6: “I think for [summer sport] it is hard. We literally will have three games a week and two exams a week”.

Fluctuating patterns were also shown for academic hours (represented by hours spent in academic lessons plus hours doing home work) and number of lessons missed. Academic hours were significantly lower during November/December and February (23.6–26.7 h/week) and highest in September and May (~ 28 h/week), which coincided with the number of lessons missed (i.e. more lessons missed in November–February than September–May). Furthermore, when the student athletes were describing their timelines, they highlighted three time periods that could be considered the most stressful from an academic perspective: (1) the second week back after the Christmas break (mock exam week), (2) the final 2 weeks before Easter (final coursework deadlines) and (3) the whole of terms 5 and 6 (final academic examinations).

3.1.1.1 Periods of Difficulty Balancing Dual Demands and Changes in Stress and Recovery

Student athletes found balancing academic and sports workload significantly harder during November–March (3.22–3.34) and easiest during May (2.43). When student athletes were describing their timelines, they described a constant oscillation between periods of high academic stress (e.g. assessment time, mocks and exams) and high sport workload (e.g. busy fixture list, major tournaments and finals), with them often coinciding, resulting in increased stress and pressure.

“So, at the moment it is fine, but now gradually, academics are getting a lot more pressure on and the fixtures start to go like that again [demonstrated a steep incline with hand]. And then there is not really a break till March and by then should be absolutely ready for your A-levels and you are behind. Still revising some topics”.

Although student athletes’ general stress stayed stable across the academic year (no significant change across September to May), sport-specific stress levels varied across different time periods (highest in February and lowest in May). Regarding recovery, although general recovery stayed relatively stable across the academic year, sport-specific recovery was significantly lower in February compared with September (implying that student athletes were not recovering as well from sports during February compared with September).

3.1.1.2 Fatigue Accumulation, Culminating in Student-Athletic De-motivation

At the beginning of the academic year (September), student athletes were exposed to an immediate high academic and physical workload (i.e. 9.93 training hours/week and 28.1 academic hours/week). Additionally, from a physical fitness perspective, student athletes are physically less fit. Overall, the initial challenges (i.e. demanding schedule) and lack of physical fitness appeared to result in student athletes feeling fatigued, both mentally and physically at the start of the academic year. For example, a student athlete stated in their log diary 3 weeks into term 1:

“I’m keeping up with my school work but the workload is high due to not having free periods (because I play sport). I feel motivated to improve in both my academics and my sport. I am finding myself feeling more tired during the week but this is probably a combination of higher amounts of physical activity and not going to bed early enough.”

The feeling of fatigue was a common impact across the academic year. The student athletes frequently stated in their log diary that they were ‘always tired’, as exemplified by this student athlete: “I always want to sleep”. This impact was further exaggerated for student athletes with increased academic demands (e.g. undertaking four A-levels versus three), as exemplified by one student athlete’s log diary:

“The workload is high because I am taking 4 A-levels as well as doing my sport throughout the day- this means I have less time in school to complete work set and have to do the majority of it at home. This can build up and occasionally I find myself working until late which is leaving me feeling tired in the morning”.

Finally, there seemed to be an accumulative build-up of fatigue towards the end of each academic term and year. From a conversation with one of the coaches at the end of term 2, they stated: “This time of year everything changes. Kids getting tired, we are getting tired and boredom setting in”. The effect of fatigue on student athletes’ academic work was further elaborated on in a conversation with a student athlete: “I think there is enough time to do your work, it is just not enough time where you are not tired. You come home and you are knackered you don’t want to do work.” The student athletes described becoming demotivated during the end of term with a lack of physical development. “I plateaued. I started hating [sport]. I wasn’t improving, I was tired, I was stressed. To the point where I didn’t enjoy it”.

The feelings of de-motivation and mental and physical fatigue were further exaggerated in terms 5 and 6. A student athlete stated: “It is a bit burnout. You go, boom, boom, boom, boom, boom and now you just feel like flat”. By terms 5 and 6, student athletes appeared to have a lack of motivation and burnout for performance sports (consistent with student athletic motivation score, which was significantly lower in May), where student athletes wanted a period of unstructured training and time away from the performance environment.

“The last summer term with exams. I remember that first weekend after school finished, I literally couldn’t do anything else. I was so tired, like mentally and physically. And then I dunno, the feeling was awful”.

3.1.1.3 Immediate and Multiple Stresses

New student athletes at ‘Nunwick High’ experienced increased stress and pressure from an immediate intensive level of training and increased academic demands. In addition, they reported emotional and social stress from moving away from home, family and friends into a new environment. For example, from conversations with new student athletes who had transitioned into the school, they stated that they found the workload (both academic and physical) ‘a lot more’ than previous experiences. “You’re sort of chucked straight into it and expected to do everything basically. It is quite intense and a lot asked of you. Kind of have to do it and get it done”.

Then across the academic year at ‘Nunwick High’ there was evidence of three types of stressors: (1) Competitive stressors related to the demanding game schedules. “The upcoming matches that have been occurring have caused me to become more stressed”. (2) Organisational stressors from commitments to school sport balance.

“But I think sometimes, yeah it happens, but you are not enjoying it, it doesn’t become enjoyable it just becomes stressful. To go to a match and then come back and do your work. It is then not an enjoyable period”.

Finally, (3) Personal stressors when student athletes sacrifice social life for sport.

“I’m not as social as I was at the beginning of the year, I think this is due to the stress given by school. I feel as though I need to spend more time doing school work compared to socialising”.

However, contradictory to the personal stressors, student athletes’ friends and family and free time KIDSCREEN-27 Health Questionnaire scores stayed stable across the academic year.

3.1.1.4 Despite Challenges and Academic Pressure, Student Athletes Generally Achieved Good Academic Grades

As highlighted above, student athletes experienced challenges across the academic year (e.g. demanding schedule, fatigue and multiple stressors), in addition to academic pressure, as highlighted by a student athlete, “for me, academic pressure is a really big thing, because I am really scared, I am going to let it slip accidently”. Despite these challenges, overall, academic grades stayed relatively stable across the academic year (4.29–4.57), with only June significantly higher than October. This finding coincides with the fact that academic motivation also stayed stable across the academic year (4.78–4.88). This is further supported by the qualitative data which highlighted that student athletes at ‘Nunwick High’ generally achieved good academic grades. According to the log diaries, although some challenges around managing the multiple demands on their time were highlighted, most student athletes were generally happy with their academic development across the year: “I think I have developed academically in my exams; I have improved consistently throughout the year”.

3.1.2 Sport Performance Development and Well-Being Across the Year

As highlighted in the previous theme (3.1.1.2) student athletes are physically less fit at the beginning of the academic year. However, over time there were significant improvements in IMTP strength (123.3 kg September and 160.6 kg in March/April), CMJ height (34.3 cm in September and 36.5 cm in March/April) and 40 m max velocity (only September–December), whilst 10 m acceleration stayed stable.

Sport confidence was stable across the academic year (no significant change from September to May). However, there was a significant decrease in student athletes’ perceived sport competence during February (3.40) compared with September–December (3.59–3.58). Sport competence then recovered between February to May but not compared with September–December levels. This data contradicts the qualitative findings whereby student athletes largely expressed how being involved in the performance sports programme had resulted in them becoming better at their sport. They stated in their log diaries that they could see improvements in their physical, technical and tactical development and overall sporting performance across the academic year.

“My athletic development has gradually improved over time during all of the training and sessions. My physical development has improved slightly as well, especially with things like speed and size. My personal fitness has improved from the training and has encouraged me to do more out of the sessions”.

When evaluating injury and illness, injury incidence was higher than illness incidence. The greatest number of injuries occurred in November/December (47%), with the lowest injury incidence in May (23%). Illness incidence was highest in September (31%) and lowest in May (11%). Finally, although there was no significant difference or change in the student athletes’ EAT-26 score across the academic year and average scores were below 10, there were student athletes who scored ≥ 10, signifying disordered eating behaviour and attitude.

3.1.3 Personal Development

Student athletes also reported to have developed personally, although LSSS (3.51–3.56) and resilience (3.29–3.39) scores stayed stable across the academic year (no significant change across September–May). Through the qualitative data many student athletes emphasised they had developed a range of life skills and attitudes they could use both within and outside of sport. For example, they felt they had become more confident and developed their communication, social integration ability, social skills, work ethic, motivation, time-management skills, teamwork and leadership skills, in addition to becoming more independent, resilient, disciplined, mature and responsible adults. Student athletes highlighted these developments in their log diaries and through conversations with the primary researcher:

Student athlete 1: “Allowed me to develop my motivational skills. Training more and work in the gym helped to develop my social skills and my physical and mental abilities of perseverance during training and during my school work”.

Student athlete 2: “We talked about balancing a lot and if you are doing sport and academics, you kind of naturally build the skill of time-management and balancing stuff. I have got sport and A-levels as well, so I kind of have to think about time management as well. So, after I finish my sport, I know I need to go home and complete my prep. So, I kind of manage my day to get it all done”.

3.2 Specificity of Athlete Characteristics

There was no significant difference between boarders and non-boarders across all variables. For sex, females had significantly fewer weekly competitions than males. Female sport confidence scores and general recovery scores were significantly lower (− 0.68, − 0.47) and general stress and EAT-26 scores were significantly higher (+ 1.03, + 9.28) than males. Finally, females had significantly lower CMJ (− 10.49 cm) and IMTP (− 50.96 kg) and significantly slower 40 m max velocity (+ 0.93 s) scores than males. Internal sport-only student athletes had significantly less training (− 2.30 h/week) and competitions (− 1.42 number/week) and more rest days (0.52 number/week) per week compared with external sport student athletes. However, internal-only student athletes’ general recovery was significantly lower (− 0.32), which contradicts the qualitative findings where student athletes who played sports externally and for the sport-friendly school expressed feeling particularly fatigued and lacking rest and recovery.

3.3 Features and Processes of the Sport-Friendly School Program

While the primary aim of this study is to evaluate sports school holistic impacts, the third aim is to gain insight into the context-individual interactions underpinning them (i.e. features and processes). Accordingly, this section aims to provide a narrative overview describing insights into particular features and processes of ‘Nunwick High’.

3.3.1 Importance of Personal Motivation, Value of Education and Academic Support Services

Student athletes stated that they achieved good academic grades due to developing their personal motivation, organisational skills and commitment (i.e. hard work ethic, determination, self-motivation, developing a revision routine and creating a timetable of free time to balance workload), as highlighted in a student athletes log diary: “My work ethic and motivation have improved, which has caused me to work harder and put in more effort. I am not afraid to ask questions anymore to help me understand”.

Secondly, coach support was highlighted to assist student athletes’ academic development. There appeared to be flexibility with sports training and support from the coaches around the periods of high academic stress (i.e. student athletes were allowed to miss training sessions to do work), as exemplified by a student athlete: “Since my coaches have understood about me wanting to focus on my work, sometimes it has been helpful as I know that they support me”.

Finally, the student athletes received extra academic support. Teachers and fellow pupils provided extra tutoring (i.e. one-to-one help) in their own time. Teachers provided subject and revision clinics, and ‘Nunwick High’ had a learning development department. The extra academic support provided is demonstrated in the following quote from a student athlete’s log diary:

“Getting help from teachers—one-to-one help. Clinic revision—weekly revision after school to revise through any topics that I am not comfortable with. Microsoft Teams—online teams in which I can message my teachers directly whenever I am stuck”.

3.3.2 Performance Sports Program with Direct Sport-Related Practices, Staff and Support Services

‘Nunwick High’ was reported and observed as having high-quality facilities, fixtures, coaching staff and training partners. Student athletes had access to professional, high-quality facilities (e.g. a fully equipped gym, pool, indoor three-court sports hall and numerous astroturfs and grass fields). The performance sports program arranged high-level fixtures against top opposition (e.g. academy teams, high-level clubs and top sports schools). As a result, the student athletes were challenged technically, tactically, physically and psychologically against high-level opposition, as attributed by a student athlete in their log diary: “Recent fixtures, tournaments and matches have positively impacted my development, lifting to my maximum potential and pushing myself in court sessions”. Moreover, ‘Nunwick High’ employed high-quality coaches who could provide expert coaching, support and education to enhance the sporting development of the student athletes further. ‘Nunwick High’ was also described as attracting a big pool of talented student athletes providing high-quality training partners/teammates who acted as influential mentors—providing a high-quality training and learning environment where student athletes pushed their peers to be better and develop from one another. For example, a student athlete stated in their log diary:

“My skill and physical capabilities have improved drastically over the past month as the combination of regular strength and conditioning sessions as well as daily access to an indoor basketball court and high-quality players and coaching staff has driven me to become a completely different basketball player”.

As highlighted in the qualitative and quantitative data, the student athletes trained regularly across the year. As a result, the student athletes had more opportunities to practice, play and develop in their sport. For example, a student athlete stated they had ‘developed as a player’ and that this was due to ‘training every day and having games regularly’.

‘Nunwick High’ also had a multi-disciplinary sports staff as part of the performance sports program (i.e. SC, physiotherapist and nutritionist). The student athletes had designated and regular SC sessions within their school timetable, where the SC staff provided them with tailored and sport-specific physical development programs. Additionally, the SC staff provided additional athletic and physical development resources (e.g. cardiovascular fitness sessions, advice on recovery, mobility sessions) and put on recovery sessions (e.g. stretching/yoga). This support was deemed to positively support the student athlete’s athletic and physical development. For example, a student athlete, when answering in their log diary what was the driving factor for their improved athletic and physical development, stated:

“I have had a personal SC programme fitted to what will help me make the biggest impact on my sport; this has been essential for me and helped me to push hard, knowing that my interests are being taken care of and frequently adapted to fit my needs and any progress that I make”.

Whilst another student athlete stated the support available when injured:

“Due to an injury, I haven’t been able to train as often as normal on the pitch; however, the programme has still been able to help me develop during this time. I have had a lot of physio sessions which have helped me understand what is wrong with me, and the physio works closely with the SC staff, who are then able to provide me with stretches related to my injury as well as exercises that help my performance whilst taking into consideration my injury/limitations”.

3.3.3 Because the Environment Demanded It

The requirement to take accountability and responsibility, live away from home, and the busy schedule of sports and academics required student athletes to manage themselves effectively, become better at managing multiple demands and be disciplined.

“Time-management as well. You don’t necessarily get taught it. But you learn it by having such a busy schedule. You have to work out what to do when”.

The school strongly focused on giving the players accountability and responsibility for their academic and sport development. As described above, an environment was witnessed where the student athletes were given the relevant tools to help aid their sporting development and academic development. The student athletes were responsible for using these resources and maximising the opportunities in their own time.

“Environment where everything the athletes need is available to them (e.g. video from games, SC, yoga, extra sessions, academic support, pastoral care), but although the athletes are encouraged to utilise everything that is on offer to them, it is the athlete’s responsibility on how they use their time and if they utilise their time here effectively”. [Field note, 03/02/2022]

However, there was a lack of upskilling to allow student athletes to maximise their development, particularly in managing their time effectively. The student athletes felt that sometimes, the staff presumed they had the relevant skills without providing them with the tools to facilitate appropriate ownership of their development (i.e. feeling left to their own devices). For example, student athletes stated the following comments when talking about taking responsibility:

Student athlete 1: “I think they just expect you to be more organised, to be able to fit your sport in”.

Student athlete 2: “What we get offered here, most of us haven't been exposed to it before coming here. Then you are expected to know how to use it. When a lot of people don’t. So, then they don’t get the most out of as they can do”.

Furthermore, the additional work student athletes were expected to do in their own time (e.g. clip their own video) adds to their workload, providing further conflicts with their academic study and personal time.

“Yeah, like no one tells you to go and watch the video. But I like to watch it and see what happens and see why we lost to [team]. But then that is an hour, hour and a half of Thursday when the video comes out. So that is when I should be working”.

3.3.4 Lack of Organisation and Planning of Training Load

When the student athletes first joined the performance sport programme in the sixth form, they transitioned into an intensive level of training. There was no preseason at the sport-friendly school, so the student athletes were immediately exposed to a high physical workload. Furthermore, first-team fixtures were organised within the second week of the term. When asked if the student athletes liked having fixtures within the second week of term, there was an overwhelming ‘no’ feeling. Student athletes stated they were ‘not adequately prepared’ and ‘had not had enough training time together’.

Additionally, from a physical fitness perspective, when student athletes transitioned into ‘Nunwick High’, or returned at the beginning of the academic year, they felt physically ill prepared for the immediate, intense training load. Through pre-season physical fitness testing, the primary researcher observed student athletes coming back from the summer holidays with lower physical fitness levels than expected.

“Just completed 30–15 running fitness test with [sport]. Generally, the student athlete’s cardiovascular fitness scores are lower than I would expect them to be at the beginning of the year in comparison to normative, expected data for their sport”. [Field note, 07/09/2021]

Coaches, in conversation with the researcher, emphasised that at the beginning of the year ‘students were not fit enough’. A student athlete further highlighted this comment when talking about the initial start of term: “And also, our fitness isn’t as good as it would have been after training all the time at school. So, I think we are lot more unfit as a lot of us don’t train outside of school”.

Within an academic year at ‘Nunwick High’, no periodised planning, tapering or deload was scheduled within the performance sport programme across a term. The primary researcher observed a lack of balance between high training loads followed by intentional low training loads (i.e. deload/tapering weeks).

“Season at [Nunwick High] is very full on and intense the whole time. There is no periodised planning, tapering or deload week within the term or season. This is resulting in kids being exhausted by the last 2 weeks”. [Field note, 15/03/2022] “Sometimes, I think we over train. Like having 2 h on Wednesday, then another 2 h on Thursday. Then an hour on Monday, SC on Tuesday and another SC on Friday. With no recovery. You know, it is really intense”.

Despite the benefit of offering high-quality competition from a sporting development point of view, ‘Nunwick High’ appeared to enter every competition, league and cup and has an extensive list of friendly competitions. As a result, some sports teams had two (on the rare occasion, three) internal sports fixtures a week (not considering the fixtures some student athletes have externally outside of school), leading to potential fixture congestion. Based on observations and log diaries, the extensive fixture list appeared to put further pressure on student athletes academically, as they missed many lessons and were fatigued. As exemplified by a student athlete in their log diary, “Having regular away fixtures has caused me to miss multiple lessons every week and afterschool training has limited time to catch up on homework”.

Finally, the primary researcher observed a lack of collaboration with external sports schedules. For example, based on her observations, the primary researcher reflected:

“Given that match play required a longer period of recovery than training, the school coach on Thursday often incorporated recovery sessions. However, unaware of the school match the previous day, one student athlete’s club team continued with an unmodified training session, including one to two hours of technical training on a Thursday night. As a result, negating the benefits of the recovery sessions within the school. The student athlete returned to school training on Friday, 24 h after the match, with the school coach presuming the fatigue from the match had largely dissipated. From chatting to the student athlete, they stated that they did not actually have the chance to recover from the match on Wednesday and entered the weekend fixtures feeling fatigued, which he believed compromised his performance”. [Field Note, 17/02/2022]

3.3.5 Lack of Coordination and Program Flexibility Between Academic and Sports Timetables

Although academic support services were available and some academic staff provided extra academic support and understanding for the student athletes when needed, there appeared to be a need for more understanding from all teachers. For example, in a conversation with two athletes, they stated:

Student athlete 1: “If you miss a lesson, then they just send you the work and expect you to do it yourself”.

Student athlete 2: “Sometimes I don't think my academic teachers understand. They are like ‘again, really’. I am like; it doesn’t change just because I did it last week”.

Moreover, although there appeared to be flexibility with physical training and support from the coaches around periods of high academic stress, there was a lack of planning, co-operation and compromise with scheduling, with sports fixtures clashing with periods of high academic stress (apart from in term 6). For example, as highlighted in Sect.  3.1.1 student athletes described a constant oscillation between periods of high academic stress and periods of high sport workload with them often coinciding, resulting in increased stress and pressure (as depicted in Fig.  2 which summarises the general patterns observed in the student athletes’ timeline diagrams/illustrations).

“I think my timetable doesn’t match up. So that means I get assessments and work I miss because I go to matches, and then I am still going to the gym and stuff like that. So, it feels like there is no compromise, and when it comes to assessments, I still feel like I need to do the match”.

Overview of oscillations in academic stress and sport workload across the school year

Despite academic flexibility/support by some coaches and teachers, due to the conflicts between academic and sport schedules, student athletes often felt conflicted, pressured and guilty towards both coaches and teachers if they chose one endeavour more than the other and were often reminded of it. “[Coach] will mention past things you have done. Like, yeah, but you didn't come to this one either, and you didn't come to this one, and now you are missing this one. So yeah, like the build-up of guilt”. Additionally, there seemed to be a lack of understanding and a conflict between what is a priority for student athletes regarding internal and external training, with student athletes feeling scared to come forward if they were tired.

“I feel like if I said to [coach], I can’t train as well on Wednesday as I was training for [club] on Tuesday. Then he would like to quit [club]. And I don’t want to quit it. But I don’t think I could come forward and say I was tired because I trained last night. As he would say that I am disrupting [school sport]. You are here to play [school sport]”.

There was little evidence of direct communication and alignment between sport coaches and teachers, where they worked together to ensure that their schedules were appropriately adjusted and aligned to the student’s academic (deadlines and submissions) and sport (tournaments and cup competitions) load. Instead, student athletes explained that they were the ‘middle ground’ for communication between coaches and teachers.

“I think the only thing that is hard about it is communication between your teachers and the coaches as well. As obviously, the teachers will have their say and be like, ‘You do too many matches’”.

Finally, student athletes at ‘Nunwick High’ had varying academic demands, extra-curricular activities and sporting commitments. Moreover, the performance sport teams’ schedules varied weekly (e.g. a team may compete in three competitions 1 week and no competitions the following week). Despite this, there appeared to be an overall ‘one size fits all’ approach to the overall planning, with a lack of adaptation to individual student athletes’ varying commitments and between-week team schedules, causing further competing demands and stress.

“Yeah. I think when we had gym and dance. That was sort of like a commitment. [Coach] would know we would have it after school but still expect me to go 100%, even though the night before we would have had a full run-through and everything went wrong and duh duh duh, school production itself. So it is sort of, I understand you have all this other stuff, but it doesn’t give you an excuse not to go 100% in training”.

4 Discussion

To our knowledge, this study is the first to longitudinally evaluate (1) the impact of sport-friendly school involvement on the holistic development (i.e. academic, athletic, psychosocial and psychological) of student athletes, (2) the holistic impact according to the specificity of student athlete characteristics (i.e. sex, boarding status and external sport involvement) and (3) the features and processes of the sport-friendly school programme that drive/facilitate positive and negative holistic impacts.

Overall, mixed-method data demonstrated that over-time student athletes, achieved good academic grades, enhanced their all-round sporting performance and developed personally, demonstrating positive short-term and potential long-term positive impacts of sport-friendly school involvement. In addition, student athletes’ sport confidence, academic motivation, academic grades, general recovery, life skills, resilience and friends, family and free time scores remained stable and relatively high across the academic year. Potential features and processes of ‘Nunwick High’ that contributed to these positive impacts included: high-quality facilities, fixtures, training partners and coaching staff, high frequency and extra training, multi-disciplinary sport support staff (e.g. SC, physiotherapist and nutritionist), academic support services, and self-reported motivation and hard work ethic to engage with training and academics. Despite these positive benefits, the simultaneous pursuit of academic and athletic achievements provided challenges for student athletes across an academic year. Potential negative impacts found included: increased stress and pressure at the beginning of the academic year, immediate accumulation of fatigue (both mentally and physically), competitive, organisational and personal stressors, high injury rates, potential body image concerns, conflicting demands and feeling “left to their own devices”. Furthermore, student athletes’ experienced significant fluctuations in their sport and academic workload, rest, academic lessons missed, sport-specific stress and recovery, sport competence and student-athletic motivation scores across the academic year. Many of the potential challenges/negative impacts student athletes experienced seemed to be attributed to a lack of (1) gradual increase in training exposure (intensity, frequency and volume) at the beginning of the academic year, (2) coordination and consideration between academic and sport timetables, (3) collaboration with external sport schedules, (4) direct communication and alignment between the coaches and teachers, (5) program flexibility and (6) periodised planning, tapering or deload scheduled within the sport timetable. However, it is worth noting that individual characteristics shaped the sport school experience and its impact on the holistic development of student athletes. Biological sex and external sport commitments were shown to influence student-athlete holistic impacts, however boarding status did not. Figure  3 summarises the longitudinal holistic impacts of sport-friendly school involvement, including the program’s features/processes driving positive and negative impacts.

Summary of the longitudinal holistic impacts of sport-friendly school involvement and the potential features and processes that drive/facilitate positive and negative holistic impacts

4.1 Longitudinal Investigation of Student Athlete Holistic Impacts

4.1.1 immediate and intermediate risk and challenges.

The student athletes faced numerous challenges at the onset of the academic year (e.g. high physical training loads, frequent sport fixtures and psychosocial adjustments) aligned to existing research [ 8 ]. Longitudinal data suggested these continued throughout the academic year. The workload challenges are similar to previous research in sport schools [ 80 , 81 , 82 ] and youth sport [ 12 , 83 ] but providing sport and academic load simultaneously emphasises the challenge of combining student athletes workload with external sporting commitments. These workload challenges potentially contribute to various other impacts experienced by student athletes, such as increased rates of missed academic lessons, heightened susceptibility to injuries, and the ongoing struggle to effectively balance their athletic commitments with academic responsibilities. Consequently, this confluence of demands often results in elevated levels of fatigue, persistent feelings of tiredness, and heightened stress among student athletes. This explanation is plausible given previous literature (e.g., [ 84 ] and [ 85 ]) has emphasised that the time commitments associated with combining education alongside sports training were a crucial contributor to fatigue accumulation and stress.

The longitudinal data highlights, student athletes’ need to negotiate many fluctuating academic and sport demands and expectations across a school year, which are often conflicting [ 84 , 86 , 87 ]. In parallel, student athletes seemed to find the sport–academic balance easier when they had increased rest and reduced training/competitions. These findings are unsurprising, as fewer competing demands exist. Previous research similarly demonstrates that the commitment (i.e. time and effort) to sport coincide with youth athletes’ education [ 21 ] and competitions/training, resulting in youth athletes missing school for several days or even weeks/months a year [ 88 ], making balancing both sport and education challenging [ 9 , 10 , 11 ].

Finally, the qualitative data reveal a consistent cycle between periods of high academic stress, such as assessment times and exams, and periods of intense sports workload, such as busy fixture lists and major tournaments. These overlapping demands potentially contribute to three main categories of stress: competitive stress due to game schedules, organizational stress from balancing school and sports, and personal stress involving social sacrifices. This pattern is supported by the correlation between changes in student athletes’ training loads and their sport-specific stress levels throughout the academic year. Competitive, organisation and personal stressors are supported by Kristiansen and Stensrud’s [ 85 ] study, which found evidence of all three stressors among youth female handball sport school athletes.

4.1.2 Long-Term Positive Impact

This study suggests that despite the challenges (e.g. balancing both sporting and academic commitments) student athletes within sport schools can excel in both sport and academics. Student athletes maintained stable and high academic grades throughout the year, supported by the qualitative data. These findings are congruent with broader youth sport research, which has indicated that student athletes excel in education (e.g., [ 89 ]). However, these findings contradict previous sports school literature [ 81 , 90 ], which suggested that sport participation negatively affected student athletes’ academic success.

Regarding athletic impacts, physical fitness data also demonstrated enhanced strength, speed and power. These results align with Beckmann et al. [ 91 ] study, showing increased fitness measures in student athletes enrolled in a sport school over 5 years. While the student athletes’ sport competence scores dipped compared with baseline throughout the academic year, qualitative findings demonstrated that student athletes felt they became better athletes (technical, tactically and physically). These findings may be explained by student athletes perceiving themselves as getting better but also had enhanced (different) perceptions and judgment as to where their own skills lay in comparison to others. Over time, student athletes may enhance their capacity for self-reflection and the evaluation of their abilities in comparison to others (i.e. their self-evaluation becomes increasingly more accurate but also more negative [ 92 ]), which could influence the self-perceived ratings of their own sport competence.

Finally, although student athletes’ psychosocial scores did not improve across the school year, they were relatively high at baseline and remained stable. Qualitative data highlighted the development of life skills and attitudes applicable in and beyond sport, reinforcing this trend. Previous sport school literature [ 82 , 93 , 94 ] supports the idea that sport school involvement fosters qualities and skills applicable to various aspects of life. Furthermore, overall LSSS scores were similar to that of British youth sport [ 95 ] and sport high school [ 96 ] student athletes. As such, sport-friendly schools should continue to develop student athletes technical, tactical, physical and academic capabilities but additionally develop their personal, social and life skill capabilities [ 97 ], to ensure student athletes develop transferable skills for life beyond the sport-friendly school environment [ 98 ].

4.2 Specificity of Athlete Characteristics

Sex and external sport commitments were shown to influence student athlete holistic impacts, however boarding status did not. In accordance with O’Connor et al. [ 99 ], females demonstrated lower levels of sport confidence and perceived competence compared with males, along with higher general stress, lower general recovery and greater body image concerns. Literature suggests that youth athletes, particularly females, are becoming concerned about their body image at increasingly early ages [ 100 ] and body-related shame and guilt are increasing over time among female youth athletes [ 101 ]. Looking at the inter-relationship between variables, previous research has found a significant relationship between body image and sport-related variables (e.g. sport confidence [ 102 ]). Furthermore, Murray et al.’s [ 103 ] study found a significant association between higher body dissatisfaction and higher ratings of peer stress and lower self-esteem. Given the potential heightened vulnerability in females, further research should explore the holistic development of female student athletes in sport schools.

Student athletes (such as those at ‘Nunwick High’) often participate in multiple sport or for various teams within the same sport [ 33 , 104 ]. External sport involvement increased student athletes’ time commitments (more training hours and competitions and less rest), intensifying the competing demands between academic and athletic pursuits. The additional demands link with lower general recovery scores for external sport student athletes. Research demonstrates that student athletes with higher weekly training loads have higher recovery-stress states than student athletes with lower weekly loads [ 105 ]. Furthermore, the qualitative data highlighted further fatigue and recovery challenges amongst this group, exacerbated by unsynchronized schedules between external and internal sport commitments. Previous research supports this conclusion, which demonstrates the ‘tug of war’ scenario of various weekly sport commitments, which can result from separate and contrasting athlete-focused training plans and goals [ 33 , 104 ]. Collaborative management of training schedules among the various stakeholders (i.e. coaches) is crucial to prevent fatigue, overreaching and injury risks among this specific group [ 106 , 107 , 108 , 109 ], requiring aligned training aims, load management, fixture lists and flexible programming [ 33 ].

4.3 Features and Processes of the Sport-Friendly School Program

As DC environments are complex and dynamic, whereby student athletes have to interact with many features and processes of a sport school, this study aims to advance on existing research to understand what facilitated and drove the positive and negative impacts. This approach was a unique and novel aspect of this study resulting in five key findings as discussed below.

4.3.1 Importance of Personal Motivation, Value of Education and Academic Support Services

One clear positive impact was that student athletes’ academic performance was high and stable consistent with previous research [ 89 , 110 ]. These findings may be explained by the student athletes displaying stable and relatively high levels of academic motivation across the school year and personal attributes aligned to academic work (e.g. hard work, organisation skills and commitment). Research (e.g. [ 111 ]) supports the associations between individual traits (e.g. AM, educational goals and commitment) and academic achievement demonstrating that student athletes’ academic motivation is important to achieving academic success. Furthermore, academic performance may reflect the importance of the additional support offered by sport schools (e.g. extra tutoring, revision clinics and consistent check-ups from academic and sport staff) in protecting academic success [ 8 ]. Mentorship, monitoring and extra tutoring were some of the academic support services provided at ‘Nunwick High’, which are consistent with previous sport school literature [ 7 , 8 , 93 , 112 , 113 ] and recognised as essential for encouraging academic success [ 114 ]. Finally, coach support (e.g. flexibility with sport training and support around the periods of high academic stress) was highlighted to assist student athletes academic development. This result is similar to Knight and colleagues [ 115 ], who underscored the need for an athlete’s support network to consistently reinforce the importance of education and the value of maintaining a DC. Ensuring the support staff are on the same page and everyone’s expectations are aligned, eases tensions within the group and prevents the student athletes from feeling conflicted [ 115 ].

4.3.2 Performance Sport Program with Direct Sport-Related Practices, Staff and Support Services

The current study provides additional evidence of Thompson et al. [ 15 ] cross-sectional study, demonstrating that student athletes will improve their all-round sport performance across an academic year and this change may be facilitated by a multi-disciplinary sport staff, high quality facilities, fixtures, training partners and coaching staff, high frequency of training, individualised support and a positive team culture. High-quality coaches and multi-disciplinary teams (e.g. SC coaches, sports psychologists, nutritionists and physiotherapists) are raised in the wider literature as aiding talent development [ 116 , 117 , 118 , 119 ]. Accordingly, it seems plausible that sport-friendly school programmes should employ high-quality coaches and support sport staff to provide high-quality training programmes and sessions. However, whilst this study demonstrates the value of high-quality coaches and support staff, future research should explore how coaches achieved performance education and development in practice. Having high-level fixtures and training partners is supported by Henriksen’s research [ 120 ], which supports a culture where you foster competition between members of the same institution and challenge them externally. However, although frequent and additional training opportunities were deemed a positive in this study, future research should explore the workload of the sport-friendly school student athletes objectively and their subsequent correlation with rest, recovery and injury.

4.3.3 Lack of Organisation and Planning of Training Load

Student athletes at ‘Nunwick High’ attributed their initial hard transition partly to inadequate physical preparation. Likewise, student athletes in Andersson and Barker-Ruchti’s [ 80 ] study attributed the initial stress they experienced due to the lower level of physical training that had taken place in their previous club communities. ‘Nunwick High’ student athletes faced an immediate, intense training load (with no preseason), possibly contributing to a high November/December (T2) injury rate. Similar findings in prior research (e.g. [ 121 ]) noted increased injuries after school holidays (e.g. summer). These findings suggest that more careful consideration of return to training planning and monitoring of appropriate training loads may be warranted [ 122 , 123 ]. From a fatigue, illness and injury prevention perspective, student athletes (particularly those new to a performance sport program) may benefit from a gradual, sequential increase in intensity, frequency, and volume early in the academic year. Furthermore, student athletes may benefit from support to help them prepare for and cope with the challenges and changes of moving into or transitioning through the sport-friendly school environment [ 81 , 85 ].

A recurring ‘tiredness’ theme emerged among ‘Nunwick High’ student athletes, with subsequent mental and physical fatigue accumulation. Across an academic term, ‘Nunwick High’ lacked planned deloading or periodization, with no systematic high-to-low load transitions to facilitate recovery [ 104 ]. As such, the issue may not be the overall load buts its organisation and lack of external sport workload coordination [ 104 ]. Scantlebury et al. [ 33 ] highlighted that a failure to provide appropriate periods of recovery between training sessions and within programmes could lead to lowered training capacity [ 124 , 125 ] or increased incidence of injury, illness and overtraining [ 126 , 127 , 128 ]. Furthermore, the lack of periodised planning may explain the fact that ~ 30% of student athletes had sustained an injury. To provide a sufficient stimulus for progressive overload, student athletes need be exposed to periods of high training volume and/or intensity [ 2 , 129 ], reflected in the increase in physical fitness testing data. However, recovery must be implemented after periods of intensified or voluminous training to allow the athlete to dissipate fatigue, adapt and avoid maladaptive responses such as overuse injury [ 108 ]. Accordingly, in sport schools, planned high-load/low-load periods are crucial to facilitate recovery and adaptations [ 33 , including periodised tapering or deload weeks aligned with high academic stress periods (e.g. assessments or mock exams).

4.3.4 Lack of Coordination and Program Flexibility Between Academic and Sport Timetables

Competing demands can be stressful when activities across the school timetable are insufficiently coordinated [ 85 ]. ‘Nunwick High’ lacked coordination between academic and sport timetables (e.g. fixtures scheduled throughout high academic stress periods, where student athletes missed lessons). Although some academic staff offered extra support and coaches were somewhat flexible and supportive (although may subconsciously emphasise sport within their communication with student athletes), better program planning, communication and alignment between coaches and teachers are needed. Previous research has highlighted that flexibility and planning are key to managing student athletes’ schedules [ 33 ] and alignment between coaches and teachers is crucial [ 84 ]. Consequently, coaches and teachers should adopt an athlete-centred approach, coordinating to recognise periods of high academic stress (e.g. exams and coursework deadlines) and high sport workload (e.g. competitions, finals) before adjusting schedules to ensure student athletes can manage both demands [ 33 ]. However, this may be more difficult for some sport (e.g. summer sport, such as cricket), where timetable clashes may be unavoidable. Previous research supports such integrated efforts as critical features of successful talent development environments [ 20 , 115 ], alleviating tensions and helping prevent dual career demands conflict [ 115 ].

It appeared hard for practitioners within ‘Nunwick High’ to plan effective training loads, efficient recovery and sufficient academic time due to the ‘individualised chaos’ within and between studentathletes varying weekly schedules [ 130 ]. Qualitative and quantitative (95% CI) data confirmed this variability. The challenges of within and between youth-athlete variance in weekly training load has been previously shown [ 33 , 131 ]. Individual needs differ based on sport, academic path and circumstances [ 132 ]. Consequently, in addition to program flexibility, sport-friendly schools may consider monitoring sport school student athletes’ varying weekly schedules, coaches/teachers should monitor student athletes’ physical and academic loads (e.g. training/work diaries), wellness (e.g. daily wellness questionnaire [ 133 ], the profile of mood states questionnaire for adolescents [ 134 ]) and recovery states (e.g. perceived recovery scale [ 135 ]) on an individual basis.

4.3.5 Because the Environment Demanded It

A clear positive impact was that student athletes’ developed life skills and attitudes applicable in and beyond sport. The requirement to take accountability and responsibility, live away from home and balance the busy schedule of sport and academics enabled student athletes to manage themselves effectively (i.e. become better at managing multiple demands) and be disciplined. However, it is also important to acknowledge the skills required to negotiate these challenges (e.g. psychological characteristics and competencies [ 136 ]). As such, there appeared to be a need for upskilling to allow student athletes to maximise their development earlier, particularly when managing their time effectively. Collins and Macnamara [ 136 ] proposed that skills development in an appropriately challenging environment is a big factor in the pursuit of ‘super-champ’ status. As such, sport-friendly schools may consider educating the student athletes with essential skills that would aid the challenges they face during their time at the sport school (e.g. time-management skills, developing coping strategies, a programme focused on understanding the most efficient way to maximise their learning) to allow them to exploit their development by understanding the most efficient way to maximize their learning and balance the issues arising from their restricted time schedules [ 33 , 86 ].

4.4 Balance Between Optimising Experience and Appropriate Challenge

It is worth noting that while student athletes encountered many challenges throughout the school year (e.g. oscillations in stress and demanding schedules), longer-term they reported largely positive impacts, potentially preparing them for the multiple demands of being a professional athlete or adult in the future. Research emphasises the value of incorporating challenges into talent development pathways (e.g., [ 137 ] and [ 138 ]). Overcoming challenges is increasingly seen as favourable for aspiring student athletes [ 137 , 138 ] but developing skills to navigate these challenges (e.g. psychological characteristics and competencies) should be planned and managed too. As such, while helping manage some of the physical overloading and scheduling (e.g. to prevent harm through injury, stress and emotional/physical fatigue), helping coaches understand progressive tolerance to the stresses experienced and upskilling student athletes is clearly warranted, there may be a need for some of these challenges to develop long-term positive holistic impacts (i.e. where the immediate/short term negative impacts could have medium-longer term positive impacts). So, while potential recommendations within this study may help optimise the experience, they should be carefully considered regarding their impact on the student athletes’ development in other areas (e.g. resilience, independence and self-motivation). Consequently, future research needs to explore what short-term impacts and processes are needed for long-term positive impacts.

5 Limitations and Future Research

Although the longitudinal design, mixed methods approach (triangulation), and generalised mixed modelling analysis are key strengths, it is also important to be aware of the study’s limitations. Some would argue that due to the first-hand experiences of the primary author, they already had their preconceived ideas, potentially narrowing the analytic lens of the study. However, the quantitative statistical analysis alongside the use of critical friends and frequent peer-debriefing and reflection sessions among co-authors, to minimise any potential biases [ 69 ]. Self-reported measures introduce another limitation, including the potential influence of social desirability. Moreover, different questionnaires were necessary to capture diverse impacts, potentially impacting response quality due to the questionnaire’s length [ 139 ]. However, the questionnaire was conducted in a quiet room, student athletes were allowed sufficient breaks when required and were allowed to return to the questionnaire at a later time within the same day. Furthermore, while participant concerns might not have been openly expressed in front of an institution member, the primary author's rapport with student athletes and staff fostered positive interactions, emphasising confidentiality and encouraging open, honest responses. Finally, in the academic year, term 6 was only 3 weeks long, and most upper-sixth student athletes had already left after final exams, leading to the decision to omit the online questionnaire during this term. Despite this, observational research covered the full 33 weeks, with the timeline diagram conducted at the study’s conclusion, though the lack of log diary assessment in terms 5 and 6 is a limitation.

While this study offers an initial insight into sport–school student athletes’ holistic impacts and trajectories, future research could explore this further using longitudinal methods, such as Cobley et al. [ 140 ], tracking the comprehensive development of select youth players and employing different statistical techniques such as multivariate latent growth models (e.g. [ 141 ]). Moreover, while this study provides an initial insight into how individual characteristics shape the sport school experience and its impact on the holistic development of youth athletes, further research is needed to gain a more in-depth understanding. For example, exploring additional individual characteristics like sport-by-sport analysis, age, injury status and training cycles could further enrich understanding. Finally, while preliminary discussions about potential correlations between impacts were included (e.g. academic attainment and AM), these relationships lack statistical exploration, necessitating further modelling and investigation of direct impact relationships.

6 Conclusions

Overall, ‘Nunwick High’ student athletes developed positive long-term holistic impacts (i.e. academically, athletically and personally), including maintaining stable and relatively high levels of sport confidence, academic motivation, general recovery, life skills, resilience and friends, family and free time scores. Development was generally attributed to the sport school’s athletic and academic support services and personal traits of the student athletes and staff. Moreover, accountability, responsibility, independence and navigating busy schedules fostered crucial life skills. Despite positive impacts, juggling academic and sport workload posed challenges for student athletes, potentially leading to negative holistic impacts (e.g. fatigue, pressure, stress, injury and lessons missed). These issues were linked to insufficient training load build-up, communication, coordination, flexibility and planning. While addressing physical overloading and coach understanding is important, future research should evaluate other environments and explore what short-term impacts are needed for long-term positive impacts.

Additionally, individual characteristics (e.g. biological sex) influenced sport school impact. Females had lower sport confidence, higher general stress and body image concerns and less general recovery compared with males. This vulnerability warrants detailed research on female student athletes. Furthermore, engagement in external sport introduces additional time and workload commitments, prompting sport schools to collaborate with broader sporting partners to harmonise student athletes’ training schedules and create coordinated athlete-focused training plans and goals. In summary, these findings demonstrate the complex nature of combining education and sport commitments and how sport schools should manage, monitor and evaluate the features of their programme to maximise the holistic impacts of sport–school student athletes.

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Thompson, F., Rongen, F., Cowburn, I. et al. A Longitudinal Mixed Methods Case Study Investigation of the Academic, Athletic, Psychosocial and Psychological Impacts of Being of a Sport School Student Athlete. Sports Med (2024). https://doi.org/10.1007/s40279-024-02021-4

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The case study approach

Sarah crowe.

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

Kathrin Cresswell

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

Ann Robertson

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

Anthony Avery

Aziz sheikh.

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

Introduction

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

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

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

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

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

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

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

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

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

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

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

How are case studies conducted?

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

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

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

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

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

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

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

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

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

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

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

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

Analysing, interpreting and reporting case studies

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

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

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

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

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

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

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

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

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

Pre-publication history

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

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

Acknowledgements

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

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• Published: 18 April 2024

A method for identifying different types of university research teams

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

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

Metrics details

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

Introduction

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

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

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

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

How can we identify different types of university research teams?

What are the characteristics of research groups within universities?

Literature review

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

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

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

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

Team identification method

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

Classification of research team members

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

The original idea of team member classification

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

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

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

Establishment of team member classification criteria

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

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

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

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

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

Classification of research teams

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

Project-based research teams: identification based on research projects

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

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

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

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

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

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

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

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

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

Identification of university discipline backbone members

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

Team identification with discipline backbone members as the Core

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

Merging based on similarity perspective

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

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

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

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

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

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

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

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

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

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

Representative team: consolidation via backbone-based research group

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

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

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

Research team identification process and its pros and cons

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

Different university research teams are identified at different stage.

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

Validation of identification results

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

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

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

Data collection and preprocessing

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

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

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

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

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

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

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

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

Identification results of university research teams

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

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

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

Analysis of the characteristics of Bbrg in universities

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

Group scale

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

Group structure

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

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

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

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

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

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

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

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

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

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

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

Group output

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

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

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

Typical cases

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

Basic Information of the Bbrgs

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

Characteristics of team member network structure

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

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

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

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

Discussion and conclusion

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

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

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

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

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

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

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

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

Data availability

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

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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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Guidance on building better digital services in government

Determining the true value of a website: A GSA case study

Cleaning up: A hypothetical scenario

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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New EY US Consulting study: employees overwhelmingly expect empathy in the workplace, but many say it feels disingenuous

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The majority (86%) of employees believe empathetic leadership boosts morale while 87% of employees say empathy is essential to fostering an inclusive environment.

As many employees face downsizings, restructurings and a looming global recession, most say that empathic leadership is a desired attribute but feel it can be disingenuous when not paired with action, according to the 2023 Ernst & Young LLP ( EY US )  Empathy in Business Survey .

The study of more than 1,000 employed US workers examines how empathy affects leaders, employees, and operations in the workplace. The survey follows the initial EY Consulting analysis of empathy in 2021 and finds workers feel that mutual empathy between company leaders and employees leads to increased efficiency (88%), creativity (87%), job satisfaction (87%), idea sharing (86%), innovation (85%) and even company revenue (83%).

“A  transformation’s success  or failure is rooted in human emotions, and this research spotlights just how critical empathy is in leadership,” said  Raj Sharma , EY  Americas Consulting  Vice Chair. “Recent years taught us that leading with empathy is a soft and powerful trait that helps empower employers and employees to collaborate better, and ultimately create a culture of accountability.”

The evolving state of empathy in the workplace

There are many upsides to empathetic leadership in the workplace, including:

• Inspiring positive change within the workplace (87%)
• Mutual respect between employees and leaders (87%)
• Increased productivity among employees (85%)
• Reduced employee turnover (78%)

“Time and again we have found through our research that in order for businesses to successfully transform, they must put humans at the center with empathetic leadership to create transparency and provide employees with psychological safety,” said  Kim Billeter , EY Americas  People Advisory Services  Leader. “Empathy is a powerful force that must be embedded organically into every aspect of an organization, otherwise the inconsistency has a dramatic impact on the overall culture and authenticity of an organization.”

In fact, half (52%) of employees currently believe their company’s efforts to be empathetic toward employees are dishonest ― up from 46% in 2021, and employees increasingly report a lack of follow-through when it comes to company promises (47% compared to 42% in 2021).

To fulfill the authenticity equation, previous EY research indicates offering flexibility is essential. In the 2022 EY US Generation Survey, 92% of employees surveyed across all four workplace generations said that company culture has an impact on their decision to remain with their current  employer.

Lead with empathy  now  to combat the workplace challenges ahead

While leaders may experience lower employee attrition rates now when compared to the Great Resignation, a resurgence is brewing. Many economists expect a soft landing from the looming recession and with it may come turnover, particularly if employees already feel disconnected from their employer or from each other.

In fact, failing to feel a sense of belonging at work or connection with coworkers is a growing reason why employees quit their jobs. About half (50% and 48% in 2021) left a previous job because they didn’t feel like they belonged, and more employees now say they left a previous job because they had difficulty connecting with colleagues (42% vs. 37% in 2021).

“What happens outside of work has a direct impact on how people show up. It’s no longer enough for leaders to think of a person in one dimension – as an employee or as a professional within the organization,” said  Ginnie Carlier , EY Americas Vice Chair – Talent. “Leading with empathy helps move from the transactional and to the transformational Human Value Proposition, where people feel supported both personally and professionally.”

2023 EY Empathy in Business Survey methodology

EY US  commissioned a third-party vendor to conduct the 2023 EY Empathy in Business Survey, following the 2021 Empathy in Business Survey. The survey among 1,012 Americans who are employed, either full-time or part-time, was completed between October 23 and November 6, 2022. At the total level, the study has a margin of error of +/- 3 percentage points at the 95% confidence level.

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This news release has been issued by Ernst & Young LLP, a member firm of EY serving clients in the US.

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EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.