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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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• 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.

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 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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What is case study research?

Last updated

8 February 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

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

Organization

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

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

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

Analyze case study research

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

• What are the different types of case study designs?

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

Here are the common types of case study design:

Explanatory

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

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

Descriptive

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

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

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

Exploratory

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

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

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

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

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

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

Critical instance

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

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

Instrumental

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

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

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

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

• When do you use case studies?

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

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

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

1. Select a case

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

2. Create a theoretical framework

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

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

3. Collect the data

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

4. Analyze your case

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

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

• What are some case study examples?

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

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

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

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

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

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

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The Guide to Thematic Analysis

• What is Thematic Analysis?
• Advantages of Thematic Analysis
• Disadvantages of Thematic Analysis
• Thematic Analysis Examples
• How to Do Thematic Analysis
• Thematic Coding
• Collaborative Thematic Analysis
• Thematic Analysis Software
• Thematic Analysis in Mixed Methods Approach
• Abductive Thematic Analysis
• Deductive Thematic Analysis
• Inductive Thematic Analysis
• Reflexive Thematic Analysis
• Thematic Analysis in Observations
• Thematic Analysis in Surveys
• Thematic Analysis for Interviews
• Thematic Analysis for Focus Groups
• Introduction

What is a case study?

How to do a thematic analysis for a case study research project.

• Thematic Analysis of Secondary Data
• Thematic Analysis Literature Review
• Thematic Analysis vs. Phenomenology
• Thematic vs. Content Analysis
• Thematic Analysis vs. Grounded Theory
• Thematic Analysis vs. Narrative Analysis
• Thematic Analysis vs. Discourse Analysis
• Thematic Analysis vs. Framework Analysis
• Thematic Analysis in Social Work
• Thematic Analysis in Psychology
• Thematic Analysis in Educational Research
• Thematic Analysis in UX Research
• How to Present Thematic Analysis Results
• Increasing Rigor in Thematic Analysis
• Peer Review in Thematic Analysis

Thematic Analysis for Case Studies

Thematic analysis and case study research are widely used qualitative methods , each offering distinct ways to gather and interpret qualitative data . Thematic analysis allows researchers to identify patterns and themes within data sets, providing insight into shared experiences or perspectives. On the other hand, case study research focuses on in-depth analysis of a particular instance or case, offering detailed understanding of complex issues in real-life contexts. Combining these two methods can yield comprehensive insights, enabling researchers to analyze specific cases with a nuanced understanding of broader themes. This article provides a guide on conducting thematic analysis within the framework of case study research, outlining key steps and considerations to ensure rigorous and insightful outcomes to address your research objective .

A case study is a research strategy that involves an in-depth investigation of a single case or a number of cases within their real-life context. Unlike quantitative research , which seeks to quantify data and generalize results from a sample to a population, a case study approach allows for a more detailed and nuanced exploration of complex phenomena. This method is particularly useful in fields such as psychology, sociology, education, and business, where understanding the specifics of a single situation can require qualitative analysis to provide insights into broader patterns and issues.

Case studies can be based on various sources of evidence, including documents, archival records, interviews , direct observation , participant-observation, and physical artifacts. By employing multiple sources of data, case study research enhances the robustness of the findings, offering a more comprehensive view of the subject under study.

There are several types of case studies, each serving different purposes in research. These include exploratory, explanatory, and descriptive case studies. Exploratory case studies are often used as a prelude to further, more detailed research, allowing expert and novice researchers to gather initial insights and formulate hypotheses or propositions. Explanatory case studies are utilized to explain the mechanisms behind a particular phenomenon, often in response to theory-driven questions. Descriptive case studies, on the other hand, aim to provide a detailed account of the case within its context, without necessarily aiming to answer 'why' or 'how' questions.

One of the key strengths of case study research is its ability to provide insight into the context in which the case operates, which is often lost in larger-scale quantitative studies. This context can include social, economic, cultural, and other factors that significantly influence the case. Understanding these contextual factors is crucial for interpreting the findings accurately and can help to identify how the results of a case study might (or might not) be applicable in similar situations.

However, case study research is not without its challenges. The in-depth nature of the investigation often requires a significant amount of time and resources. Additionally, the findings from a case study are sometimes viewed as having limited generalizability due to the focus on a specific case or a small number of cases. To address this concern, researchers can employ a technique known as 'theoretical generalization,' where findings are related back to existing theories, contributing to a broader understanding of the phenomenon.

Whatever your qualitative research method, turn to ATLAS.ti

Narrative analysis, focus group discussions, do it all with our powerful tools. Start with a free trial today.

Thematic analysis is a method for identifying, analyzing, and reporting patterns (themes) within data. It provides a flexible and useful tool for qualitative research , especially within the context of case study research . This section outlines the steps for conducting a thematic analysis in a case study research project after data collection , ensuring a systematic and rigorous approach to data analysis . The process is divided into three key subsections: preparing your data, identifying themes, and reviewing and defining themes.

Preparing qualitative data

The first step in thematic analysis is to become familiar with your data. Usually this is textual data that can help you name relevant themes later on. This involves a detailed and immersive reading of the data collected from your case study. Data can include interview transcripts , observation notes , documents , and other relevant materials. During this phase, it's crucial to start making initial notes and marking ideas for coding. Remember to refer to important theories from your literature review to inform your subsequent analyses. Organizing your data systematically is also essential; this could mean arranging data into different types based on the source or nature of the information. This preparatory work lays the foundation for a more focused and efficient analysis process.

Identifying themes

After familiarizing yourself with the data, you can code the data by selecting interesting segments of data and attaching a code (or label) to capture the essence of each data segment and how it relates to your research question. After this initial coding, the next step is to begin theme development. This involves collating all the codes and the relevant data to identify themes that emerge across the dataset. A theme captures something important about the data in relation to the research question and represents some level of patterned response or underlying meaning within the data set. During this phase, it's important to be flexible - themes may evolve or merge as you refine your analysis and gain a deeper understanding of the data.

Reviewing and defining themes

Once potential themes have been identified from your qualitative study, the next step is to review and refine them. This involves a two-level review process: first, reviewing the themes identified in relation to the coded extracts, and then reviewing these themes in relation to the entire dataset. This step ensures that each theme is coherent, consistent, and distinct. It also involves determining the "story" that each theme tells about the data, which is critical for the next steps of analysis and for writing up the findings. Finally, it is necessary to define and name the themes, which involves a careful consideration of what each theme captures about the data and how it relates to the research questions and objectives .

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Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Seminal Authors
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Locating Theoretical and Conceptual Frameworks This link opens in a new window
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

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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Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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• Published: 27 June 2011

The case study approach

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

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

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

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Introduction

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

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

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

What is a case study?

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

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

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

What are case studies used for?

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

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

How are case studies conducted?

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

Defining the case

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Conclusions

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

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Acknowledgements

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

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Sarah Crowe & Anthony Avery

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

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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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A streamlined culturomics case study for the human gut microbiota research

Affiliations.

• 1 Research Institute of Eco-Friendly Livestock Science, Institute of Green-Bio Science and Technology, Seoul National University, Pyeongchang, 25354, South Korea. [email protected].
• 2 Research Institute of Eco-Friendly Livestock Science, Institute of Green-Bio Science and Technology, Seoul National University, Pyeongchang, 25354, South Korea.
• 3 Department of Agricultural Biotechnology, College of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, South Korea.
• 4 Department of Internal Medicine, Digestive Disease Center and Research Institute, Soon Chun Hyang University School of Medicine, Bucheon, 14584, South Korea.
• 5 Research Institute of Eco-Friendly Livestock Science, Institute of Green-Bio Science and Technology, Seoul National University, Pyeongchang, 25354, South Korea. [email protected].
• 6 Graduate School of International Agricultural Technology, Seoul National University, Pyeongchang, 25354, South Korea. [email protected].
• PMID: 39223323
• PMCID: PMC11368911
• DOI: 10.1038/s41598-024-71370-x

Bacterial culturomics is a set of techniques to isolate and identify live bacteria from complex microbial ecosystems. Despite its potential to revolutionize microbiome research, bacterial culturomics has significant challenges when applied to human gut microbiome studies due to its labor-intensive nature. Therefore, we established a streamlined culturomics approach with minimal culture conditions for stool sample preincubation. We evaluated the suitability of non-selective medium candidates for maintaining microbial diversity during a 30-day incubation period based on 16S rRNA gene amplicon analysis. Subsequently, we applied four culture conditions (two preincubation media under an aerobic/anaerobic atmosphere) to isolate gut bacteria on a large scale from eight stool samples of healthy humans. We identified 8141 isolates, classified into 263 bacterial species, including 12 novel species candidates. Our analysis of cultivation efficiency revealed that seven days of aerobic and ten days of anaerobic incubation captured approximately 91% and 95% of the identified species within each condition, respectively, with a synergistic effect confirmed when selected preincubation media were combined. Moreover, our culturomics findings expanded the coverage of gut microbial diversity compared to 16S rRNA gene amplicon sequencing results. In conclusion, this study demonstrated the potential of a streamlined culturomics approach for the efficient isolation of gut bacteria from human stool samples. This approach might pave the way for the broader adoption of culturomics in human gut microbiome studies, ultimately leading to a more comprehensive understanding of this complex microbial ecosystem.

Keywords: 16S rRNA gene amplicon analysis; Gut microbiota; Medium; Preincubation; Streamlined culturomics.

© 2024. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

Streamlined culturomics workflow. Blood culture…

Streamlined culturomics workflow. Blood culture tubes (BCT; BACT/ALERT FAN plus culture bottles, BioMérieux,…

Comparison of bacterial diversity estimated…

Comparison of bacterial diversity estimated by 16s rRNA gene amplicon sequence-base analysis in…

Venn diagrams showing unique and…

Venn diagrams showing unique and shared OTUs. Venn diagrams for ( a )…

Cultured isolates and species information.…

Cultured isolates and species information. ( a ) Number of species classified into…

Impact of streamlined culturomics approach…

Impact of streamlined culturomics approach on enhancing cultured bacterial species diversity. ( a…

Comparison of microbial diversity between…

Comparison of microbial diversity between streamlined culturomics and 16S rRNA gene amplicon analysis…

• Lewis, W. H., Tahon, G., Geesink, P., Sousa, D. Z. & Ettema, T. J. G. Innovations to culturing the uncultured microbial majority. Nat Rev Microbiol.19, 225–240. 10.1038/s41579-020-00458-8 (2021). 10.1038/s41579-020-00458-8 - DOI - PubMed
• Oulas, A. et al. Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinform Biol Insights.9, 75–88. 10.4137/BBI.S12462 (2015). 10.4137/BBI.S12462 - DOI - PMC - PubMed
• Wang, W. L. et al. Application of metagenomics in the human gut microbiome. World J. Gastroenterol.21, 803–814. 10.3748/wjg.v21.i3.803 (2015). 10.3748/wjg.v21.i3.803 - DOI - PMC - PubMed
• Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature.568, 499–504. 10.1038/s41586-019-0965-1 (2019). 10.1038/s41586-019-0965-1 - DOI - PMC - PubMed
• Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol.39, 105–114. 10.1038/s41587-020-0603-3 (2021). 10.1038/s41587-020-0603-3 - DOI - PMC - PubMed
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• Volume 14, Issue 9
• Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI): protocol for a prospective observational study
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• http://orcid.org/0000-0002-5880-8235 Alex Novak 1 ,
• Max Hollowday 2 ,
• http://orcid.org/0000-0003-0967-3554 Abdala Trinidad Espinosa Morgado 1 ,
• Jason Oke 3 ,
• http://orcid.org/0000-0001-6642-9967 Susan Shelmerdine 4 , 5 , 6 ,
• http://orcid.org/0000-0001-9598-189X Nick Woznitza 7 , 8 ,
• David Metcalfe 2 ,
• Matthew L Costa 2 , 3 , 9 ,
• http://orcid.org/0000-0003-3964-0809 Sarah Wilson 10 ,
• Jian Shen Kiam 2 ,
• http://orcid.org/0000-0002-0513-7220 James Vaz 2 ,
• http://orcid.org/0000-0002-6123-9838 Nattakarn Limphaibool 2 ,
• Jeanne Ventre 11 ,
• Daniel Jones 11 ,
• Lois Greenhalgh 12 ,
• Fergus Gleeson 13 ,
• Nick Welch 12 ,
• Alpesh Mistry 14 , 15 ,
• Natasa Devic 2 ,
• James Teh 16 ,
• http://orcid.org/0000-0001-9614-5033 Sarim Ather 2
• 1 Emergency Medicine Research Oxford , Oxford University Hospitals NHS Foundation Trust , Oxford , UK
• 2 Oxford University Hospitals NHS Foundation Trust , Oxford , UK
• 3 Nuffield Department of Primary Care Health Sciences , University of Oxford , Oxford , UK
• 4 Clinical Radiology , Great Ormond Street Hospital for Children , London , UK
• 5 Radiology , UCL GOSH ICH , London , UK
• 6 NIHR Great Ormond Street Hospital Biomedical Research Centre , London , UK
• 7 Radiology , University College London Hospitals NHS Foundation Trust , London , UK
• 8 Canterbury Christ Church University , Canterbury Christ Church University , Canterbury , UK
• 9 Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Oxford Trauma & Emergency Care (OxTEC) , University of Oxford , Oxford , UK
• 10 Frimley Health NHS Foundation Trust , Frimley , UK
• 11 Gleamer SAS , Paris , France
• 12 Patient and Public Involvement Member , Oxford , UK
• 13 Department of Oncology , University of Oxford , Oxford , UK
• 14 Liverpool University Hospitals NHS Foundation Trust , Liverpool , UK
• 15 North West MSK Imaging , Liverpool , UK
• 16 Nuffield Orthopaedic Centre , Oxford University Hospitals NHS Foundation Trust , Oxford , UK
• Correspondence to Dr Alex Novak; Alex.Novak{at}ouh.nhs.uk

Introduction Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated.

Methods and analysis A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer ( www.raiqc.com ), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image.

Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal.

Trial registration numbers This study is registered with ISRCTN ( ISRCTN19562541 ) and ClinicalTrials.gov ( NCT06130397 ). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2).

• Artificial Intelligence
• Diagnostic Imaging
• Fractures, Closed
• Emergency Service, Hospital
• RADIOLOGY & IMAGING

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https://doi.org/10.1136/bmjopen-2024-086061

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This study uses a detailed artificial intelligence-assisted fracture detection algorithm with a National Health Service-derived dataset.

A broad set of health professionals will be recruited as participants, including under-represented groups such as nurse practitioners and physiotherapists.

The enhanced dataset will allow evaluation of a broad range of pathologies, including rare but significant fractures.

The dataset will have an abnormally high disease prevalence (50%) to include a broad range of pathologies.

The small number of readers may reduce the statistical power for comparison between professional groups.

Introduction

Missed fractures are a source of serious harm for patients attending the emergency departments (EDs) and represent the most common diagnostic error in that clinical setting. 1 Almost 2 million fractures occur annually in the UK with a lifetime prevalence of nearly 40%, 2 while 5.1% of all ED attendances are for fractures or dislocations. National Health Service (NHS) Resolution has identified that misinterpretation of plain radiographs was the most common error leading to a successful claim for negligent ED care, leading to significant impacts on the lives of affected patients. 3 Reported consequences include death, disability, deformity, need for further or prolonged treatments, chronic pain, emotional distress and loss of trust in the health service. 4 Furthermore, the need for further attendances and prolonged or corrective treatment leads to significant excess healthcare costs. 5

Most acute fractures are diagnosed by ED clinicians using plain radiographs as the first-line imaging investigation (National Clinical Guideline Centre, 2016), a task which requires time, skill and expertise. However, few of the clinicians fulfilling this role have any formal image interpretation training, and they vary significantly in experience. 6 Furthermore, a workforce shortage of radiologists in the UK means that they are rarely able to undertake the primary evaluation of plain radiographs in ED. 7 The high service pressures in UK EDs combined with a highly transient workforce results in a busy and distracting clinical environment that predispose to error and missing fractures on plain radiographs. An estimated 3.3% of fractures are missed on initial interpretation by ED staff. 8 The error rate is higher on radiographs interpreted outside daytime working hours, which suggests that fatigue, workload and shift patterns may impact clinician performance. 9

Over the last decade, advances in computer vision and machine learning have been used to augment interpretation of medical imaging. 10 Several artificial intelligence (AI) algorithms have been developed that are able to detect fractures on plain radiographs with a high degree of accuracy. 11 One such algorithm is the Gleamer BoneView (Gleamer, Paris, France) (see figure 1 ), which is currently the mostly widely used fracture detection algorithm in the NHS as well as worldwide (>800 sites in 30 countries). This algorithm estimates the likelihood of a fracture being present on a radiograph and provides users with three outcomes: fracture , no fracture and uncertain . If the likelihood has been estimated to be above a designated cut-off value, the area of abnormality is highlighted as a region of interest on a secondary image, which is made available to clinicians via their picture archive and communication system. If no abnormality is detected, this is also stated on the secondary image. 12 13 Prior studies have demonstrated that the algorithm is highly accurate at detecting abnormalities, and it is already in use in a number of European centres, having received regulatory approval for use to support clinicians interpreting plain radiographs. Previous research has suggested that the algorithm is highly accurate at detecting abnormalities, and it is already in use in a number of European centres, having received regulatory approval for use to support clinicians interpreting X-rays. Moreover, recent studies have suggested that the use of AI software for detecting bone fractures 14 15 can drastically decrease the rate of missed fractures. However, this software has not yet been fully tested in a UK setting using a locally derived dataset, and it is unclear to what degree such systems would affect the diagnostic performance of certain staff groups specific to the NHS, such as reporting radiographers and specialist nurse practitioners.

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Image of Gleamer Boneview showing artificial intelligence-assisted overlay.

This study will evaluate the impacts of a commercially available AI-assisted image interpretation tool (Gleamer BoneView) on the diagnostic performance of the full range of clinicians (including nurses and allied health professionals) who routinely diagnose fractures in the NHS. It will address this evidence gap in the current evidence base, in line with the NICE (National Institute for Health and Care Excellence) Evidence Standards Framework for Digital Health Technologies, and recent Early Value Assessments which highlight the dearth of prospective evidence to support the use of AI-assisted image interpretation algorithms in the UK healthcare setting. Automation bias (the propensity for humans to favour suggestions from automated decision-making systems) is a known source of error in human-machine interaction 16 and has been one of a number of causes for concern regarding the increasing usage of AI in radiology. 17 A recent reader study in mammography, 18 suggested significant automation bias presence across all levels of experience, noting that it was only the high-experienced reporters that consistently picked up on AI error. During our study, we will also assess the impact of incorrect advice given by the algorithm on the clinical end users. 19

To evaluate the impact of AI-enhanced imaging on the diagnostic performance, efficiency and confidence of clinicians in detecting fractures on plain radiographs (primary).

To determine the stand-alone diagnostic accuracy of the BoneView AI tool with respect to the reference standard (secondary).

To determine associations between professional background and level of experience when determining the impact of AI support on clinician fracture detection (secondary).

To explore which imaging factors influence clinicians’ reporting accuracy and efficiency, and algorithm performance, for example, category of abnormality, size of abnormality, image quality, presence of multiple abnormalities (secondary).

To measure whether clinicians are more likely to make a mistake when AI provides an incorrect diagnosis (secondary).

Methods and analysis

Study design.

This study employs a multiple reader multiple case (MRMC) methodology. This approach involves multiple readers of various specialties and experience levels interpreting a large set of radiographs with and without AI assistance. The study processes are summarised in the flowchart in figure 2 , with the dataflows represented in figure 3 . The study design encompasses several key elements, including participant selection, case reading procedures, ground truthing process, case selection and AI algorithm inference on cases, which will be described in detail in the following subtitles.

Study flowchart for artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays multicase multireader study. AI, artificial intelligence; XRs, X-rays.

Artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays study dataflows. AI, artificial intelligence; RAIQC, Report and Image Quality Control; XR, X-rays.

Participants

In order to explore the effects of using the algorithm on the full range of clinicians who diagnose fractures in routine practice and minimise selection bias, we have created a balanced matrix of readers in terms of specialty and seniority. 18 readers will be recruited from the following specialties (six specialities with three readers from each):

Emergency physicians.

Trauma and orthopaedic surgeons.

Emergency nurses practitioners.

Physiotherapists.

General radiologists.

Reporting radiographers.

Each specialty group will consist of a reader each fulfilling one of the following three levels of seniority:

Consultant/senior/equivalent: >10 years experience.

Registrar/equivalent: 5–10 years experience.

Senior house officer/equivalent: <5 years experience.

Each specialty reader group will include one reader at each level of experience. Readers will be excluded if they have significant radiology experience in excess of their current specialty or grade. Prior use of fracture detection software does not exclude participation, as it is not expected in itself to confer a change in performance unless actively used during interpretation.

Readers will be recruited from across four NHS organisations that comprise the Thames Valley Emergency Medicine Research Network ( www.TaVERNresearch.org ):

Oxford University Hospitals (OUH) NHS Foundation Trust.

Royal Berkshire NHS Foundation Trust.

Frimley Health NHS Foundation Trust.

Milton Keynes University Hospital NHS Foundation Trust.

Participants will be recruited through a structured invitation process coordinated by the research team. A designated team member will collaborate with clinical leads and research coordinators at each participating site within the Thames Valley Emergency Medicine Research Network to identify potential participants based on predetermined criteria. These criteria include fulfilment of the required specialty and experience level categories, demonstrated commitment to professional development and research, and ability to commit to the full duration of the study.

All invitations will be extended based on the aforementioned criteria, and participation will be voluntary, maintaining objectivity throughout the recruitment process.

The reads will be performed using a secure web-based DICOM viewer ( www.raiqc.com ). The platform allows readers to view radiographs and identify the site of an abnormality with a mouse click. The images will be viewable through a web browser on desktop or laptop devices, reflecting standard real-world hospital practice in which radiographs are typically interpreted by clinicians without dedicated high-resolution viewing stations.

Prior to beginning each phase of the study, the readers will undergo a training module that includes reading 5 practice images (not part of the 500-image dataset) to familiarise themselves with the use of the study platform and the output of the AI tool.

Case selection and composition

The image dataset will include anonymised radiographs of adult patients (≥18 years) who presented to the EDs of OUH NHS Foundation Trust with a suspicion of fracture after injury to the limbs, pelvis or thoracolumbar spine. As CT is the investigation of choice for skull and many cervical spine injuries, these will be excluded from the study. Paediatric patients will be excluded from the dataset as their fracture types differ from those in adults, and there is an ongoing study evaluating this aspect (FRACTURE study; Fast Reporting using Artificial Intelligence for Children's TraUmaticRadiology Examinations 12 ). Obvious fractures (defined as fractures including any of the following: displacement>5 mm, shortening>5 mm or angulation>5°) will also be excluded.

To constitute the dataset, radiology reports will be screened from the radiology information system to develop an enriched dataset of the 500 standard clinical examinations evenly split between normal and abnormal, with one or more fractures. The ratio of radiographs from each anatomical location has been informed by the proportion of missed fractures mentioned in the NHS Resolution report ( table 1 ).

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Proportion of radiographs of each anatomical location, based on the proportion of missed fractures mentioned in the National Health Service Resolution report

To ensure a like-for-like comparison, image finding for abnormal cases will be performed first. The normal images will be age and sex matched per body part. We will aim to include representation of the different image views, system type (mobile or fixed), system vendors and patient demographics (eg, age, sex) without any prespecified quota.

The dataset will then be anonymised and uploaded to the Report and Image Quality Control platform under an existing data governance approval from the OUH NHS Foundation Trust Caldicott guardian.

Case inclusion and exclusion summary

Plain radiographs of adult patients (age>18 years) presenting to the OUH ED with a suspected fracture.

Plain skull radiographs.

Plain cervical spine radiographs.

Follow-up radiographs for known fracture.

Paediatric radiographs (age<18).

Obvious fractures defined as:

Displacement>5 mm.

Shortening>5 mm.

Angulation>5°.

Inferencing the image dataset

The entire dataset of images will then be separately analysed using BoneView, creating a duplicate dataset of radiographs with alerts and regions of interest indicated.

Radiographic interpretation

All readers will review all 500 radiographs individually across 2 reporting rounds.

In the first round, they will interpret the images as per clinical practice without any AI assistance. After a washout period of a month to mitigate the effects of recall bias, they will review the same 500 radiographs a second time with the assistance of the algorithm, which will contribute its suggestions as to abnormality presence and location. In both sessions, clinicians will be blinded to the ground truth established by the MSK (musculoskeletal) radiologists.

Clinician readers will be asked to identify the presence or absence of fracture by placing a marker on the image at the location of the fracture (if present) and to rank their confidence for fracture identification. Confidence rating will take the form of a Likert scale from 1 to 5 with 1 being least confident and 5 most confident.

Ground truthing

The gold standard reference process will be conducted by two experienced musculoskeletal radiologists (>10 years’ experience) who will independently review and annotate each of the 500 radiographs in the dataset. They will draw bounding boxes around each detected fracture and grade the images on both image quality and difficulty of abnormality detection using a 5-point Likert scale.

In cases of disagreement between the two primary radiologists regarding the presence or absence of abnormalities, a third senior musculoskeletal radiologist will review the contentious images and make a final decision.

All annotations, gradings and arbitration decisions will be documented within the secure web-based DICOM viewer platform, establishing a reliable reference standard for evaluating both human reader performance and AI assistance.

In the event of significant discrepancies persisting after the initial arbitration process, a consensus meeting will be agreed. This meeting will include the primary ground truth radiologists, the arbitrator and key members of the research team. The purpose of this meeting will be to review and resolve any remaining discrepancies, ensuring the integrity and consistency of the final reference standard. This collaborative approach will be employed only for cases where substantial disagreement remains, thereby maintaining the overall objectivity of the ground truth process while addressing complex or ambiguous cases.

Study timeline

This study commenced on 8 February 2024 and is actively collecting data. The data collection and analysis phase is projected to finish by the end of September 2024 with write up and publication anticipated later in the year.

Outcome measures

Reader and AI performance will be evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under receiver operating characteristic curve (AUC). Reader performance will be evaluated with and without AI assistance.

Reader speed will be evaluated as the mean review time per scan, with and without AI assistance.

Reader confidence will be evaluated as self-reported diagnostic confidence on a 5-point Likert scale, with and without AI assistance.

Data statement and management

Radiographs selected for the study will be anonymised in accordance with OUH NHS Foundation Trust information governance protocol and uploaded to the secure image viewing platform ( www.raiqc.com ). Access to the radiographs will be controlled via the study platform using separate user accounts for each reader.

All study data will be entered into a password-protected and secure database. Individual reader accuracy scores will be anonymised, and the study team will not have access to the identifying link between the participants’ personal details and the data. Data about the participants’ seniority level and professional group will be retained to allow group comparisons.

Sample size and power calculation

The study’s sample size of 500 images, evenly split between normal and abnormal cases, was determined using the Multi-Reader Sample Size Program for Diagnostic Studies. This tool, developed by Hillis, 20 is specifically designed for MRMC study power calculations. Based on parameters derived from our previous MRMC study on pneumothorax detection, the programme calculated that with 18 readers and 500 cases, our study will achieve 85% power to detect a 10% difference in accuracy between unassisted and AI-assisted readings, with a 5% type 1 error rate (See output from software below).

The chosen sample size of 500 images ensures sufficient statistical power and adequate representation of fracture types and anatomical locations. This robust sample size, combined with our substantial and diverse reader pool, should enable the detection of clinically significant improvements in fracture detection accuracy and allow for subgroup analyses across specialties and experience levels. By using this rigorously calculated sample size, we aim to produce statistically robust and clinically relevant results that can inform the potential integration of AI assistance in fracture detection across various clinical settings, while adequately addressing our study objectives and maintaining statistical validity.

Statistical analyses

The performance of the algorithm will be compared with the ground truth generated by the musculoskeletal radiologist panel. The continuous probability score from the algorithm will be used for the AUC analyses, while binary classification results with three different operating cut-offs will be used for evaluation of sensitivity, specificity, PPV and NPV. Sensitivity and specificity of readers with and without AI will be tested based on the Obuchowski-Rockette model for MRMC analysis which will model the data using a two-way mixed effects analysis of variance (ANOVA) model treating readers and cases (images) as random effects and effect of AI as a fixed effect with recommended adjustment to df by Hillis. 21

The difference in diagnostic characteristics (sensitivity, specificity, accuracy, area under the receiver operating characteristic (ROC) curve) of readers as compared with ground truth with and without AI assistance will be the primary outcome on a per image and per abnormality basis. The main analysis will be performed as a single pooled analysis including all groups and sites. Secondary outcomes will include comparison between the performance of subgroups by specialty (emergency medicine, trauma/orthopaedics, physiotherapy, nurse practitioner, radiologist, radiographer), level of seniority (senior, middle grade, junior), degree of difficulty of the image and by anatomical region. Reader-reported confidence with and without the AI assistance will be compared. Secondary outcomes include the diagnostic characteristics of the AI algorithm alone. Surveys will be conducted throughout the study to measure the satisfaction, adoption and confidence in the AI algorithm of the study participants. Per-patient sensitivity will be defined as the proportion of reads in which all true fractures were marked as a proportion of the reads having at least one fracture. Per-patient specificity will be defined as the proportion of reads in which no fracture was marked by the reader as a proportion of the reads that did not show a fracture. These definitions disregard the detection of multiple fractures thus we will define the fracture-wise sensitivity as the proportion of fractures correctly detected as a proportion of all fractures. The two coprimary outcomes will be patient-wise sensitivity and patient-wise specificity. The stand-alone algorithm performance will be assessed by calculating the area under the curve (AUC) of the ROC and free-response ROC curves plotted with their variance. To account for correlated errors arising from readers interpreting the same images with and without AI, the Obuchowski and Rockette, Dorfman-Berbaum-Metz 22 procedure; a modality-by-reader random effects ANOVA model will be used for estimation. Analyses will be carried out using R and the MRMCaov library.

Strengths and limitations

This study uses a CE (Conformité Européenne)-marked AI-assisted fracture detection algorithm with an NHS-derived dataset. The enhanced dataset will allow evaluation of a broad range of pathologies, including rare but significant fractures and its composition is mapped to mirror the proportions of missed fracture locations seen in the NHS Resolution report. A broad set of health professionals will be recruited as participants, including under-represented groups such as nurse practitioners and physiotherapists, from multiple hospital sites across the region—these reflect a reader group not yet explored in the literature, and one directly applicable to the NHS.

In terms of limitations, while the overall study group is large in comparison to other similar reader studies, the small number of readers in subgroups may reduce the statistical power for comparison between professional groups. The dataset will include an abnormally high disease prevalence (50%) to include a broad range of pathologies to facilitate meaningful statistical comparison, meaning that while the reader study will effectively explore the impact of the algorithm on readers interpreting a broad and detailed dataset, the results will not mirror the prevalence of pathologies encountered in normal clinical practice and further prospective study will be required to determine efficacy in this regard.

Patient and public involvement (PPI)

This protocol has been reviewed by the Oxford ACUTECare PPI group and PPI representatives on the artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays steering group. They have supported the study and its aims, were involved in the grant application, design and data management stages and have advised on dissemination strategies.

Ethics and dissemination

The study has been approved by the UK Health Research Authority (IRAS number 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by the Caldicott Guardian and information governance team at OUH NHS Foundation Trust. Readers will provide written informed consent and will be able to withdraw at any time.

The study is registered at Clinicaltrials.gov ( NCT06130397 ) and the ISRCTN ( ISRCTN19562541 ) registry (approval pending reference 44612). The results of the study will be presented at relevant conferences and published in peer-reviewed journals. The detailed study protocol will be freely available on request to the corresponding author. Further dissemination strategy will be strongly guided by our PPIE (Patient and Public Involvement and Engagement) activities. This will be based on co-productions between patient partners and academics and will involve media pieces (mainstream and social media) as well as communication through charity partners. Key target audiences will include non-specialist clinicians routinely involved in fracture detection, as well as hospital managers, health policy-makers and academics working in AI-assisted image analysis.

Ethics statements

Patient consent for publication.

Consent obtained directly from patient(s).

Acknowledgments

The authors would link to thank FRACT-AI steering committee: Matthew Costa, Natasa Devic, Fergus Gleeson, Divyansh Guilati, Daniel Jones, Jian Shen Kiam, Nattakarn Limphaibool, David Metcalfe, Jason Oke, Ravi Shashikala, Susan Shelmerdine, James Teh, Simon Triscott, Jeanne Ventre, James Vaz, Nick Welch, Sarah Wilson, Nicholas Woznitza.

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Contributors AN and SA led the conception and overall study design, contributed to protocol review and are co-chief investigators of the study. Both will carry out the analysis and interpretation of the results, independently write up the findings and handle publication. AN led the NIHR grant application and provided specialist emergency medicine input. SA provided specialist radiology input. MH and ATEM contributed to protocol drafting, study registration and recruitment. JO will carry out the independent statistical analysis. FG, SS, NW, DM, MLC, SW, JSK, JVaz, NL, JVentre and DJ were involved in study design and provided steering group inputs. ND and AM serve as ground truthers for the study, and JT serves as the arbitrator. LG and NW are PPI representatives. All authors contributed to the writing of the protocol and reviewed the manuscript. The guarantor of the study is AN; accepts full responsibility for the finished work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

Funding This work was supported by the NIHR Research for Patient Benefit in Health and Care Award (NIHR204982).

Competing interests JV and DJ of the Steering Committee are employees of Gleamer SAS, France. SA is a shareholder of RAIQC, UK. All other authors declare no competing interests.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods and analysis section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Author note Transparency Declaration: The lead author, AN, affirms that this manuscript is an honest, accurate and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

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Node attribute analysis for cultural data analytics: a case study on Italian XX–XXI century music

• Open access
• Published: 05 September 2024
• Volume 9 , article number  56 , ( 2024 )

Cite this article

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• Michele Coscia 1  

Cultural data analytics aims to use analytic methods to explore cultural expressions—for instance art, literature, dance, music. The common thing between cultural expressions is that they have multiple qualitatively different facets that interact with each other in non trivial and non learnable ways. To support this observation, we use the Italian music record industry from 1902 to 2024 as a case study. In this scenario, a possible research objective could be to discuss the relationships between different music genres as they are performed by different bands. Estimating genre similarity by counting the number of records each band published performing a given genre is not enough, because it assumes bands operate independently from each other. In reality, bands share members and have complex relationships. These relationships cannot be automatically learned, both because we miss the data behind their creation, but also because they are established in a serendipitous way between artists, without following consistent patterns. However, we can be map them in a complex network. We can then use the counts of band records with a given genre as a node attribute in a band network. In this paper we show how recently developed techniques for node attribute analysis are a natural choice to analyze such attributes. Alternative network analysis techniques focus on analyzing nodes, rather than node attributes, ending up either being inapplicable in this scenario, or requiring the creation of more complex n-partite high order structures that can result less intuitive. By using node attribute analysis techniques, we show that we are able to describe which music genres concentrate or spread out in this network, which time periods show a balance of exploration-versus-exploitation, which Italian regions correlate more with which music genres, and a new approach to classify clusters of coherent music genres or eras of activity by the distance on this network between genres or years.

Explore related subjects

• Artificial Intelligence

Introduction

Node attribute analysis has recently been enlarged by the introduction of techniques to calculate the variance of a node attribute (Devriendt et al. 2022 ), estimate distances between two node attributes (Coscia 2020 ), calculating their Pearson correlations (Coscia 2021 ), and cluster them (Damstrup et al. 2023 ) without assuming they live in a simple Euclidean space—or learnable deformation thereof.

These techniques are useful only insofar the network being analyzed has rich node attribute data, and that analyzing their relationships is interesting. This is normally the case in cultural analytics, the use of analytic methods for the exploration of contemporary and historical cultures (Manovich 2020 ; Candia et al. 2019 ). Example range from archaeology—where related artifacts have a number of physical characteristics and can be from different places/ages (Schich et al. 2008 ; Brughmans 2013 ; Mills et al. 2013 ); to art history—where related visual artifacts can be described by a number of meaningful visual characteristics (Salah et al. 2013 ; Hristova 2016 ; Karjus et al. 2023 ); to sociology—where different ideas and opinions distribute over a social network as node attributes (Bail 2014 ; Hohmann et al. 2023 ); to linguistics—with different people in a social network producing content in different languages (Ronen et al. 2014 ); to music—with complex relations between players and informing meta-relationships between the genres they play (McAndrew and Everett 2015 ; Vlegels and Lievens 2017 ).

In this paper we aim at showing the usefulness of node attribute analysis in cultural analytics. We focus on the Italian record music industry since its beginnings in the early XX century until the present time. We build a temporally-evolving bipartite network connecting players with the bands they play in. For each band we know how many records of a given genre they publish, whether they published a record in a given year, and from which Italian region they originate—all node attributes of the band. By applying node attribute analysis, we can address a number of interesting questions. For instance:

How related is a particular music genre to a period? Or to a specific Italian region?

Is the production of a specific genre concentrated in a restricted group of bands or generally spread through the network?

Does clustering genres according to their distribution on the collaboration network conform to our expectation of meta-genres or can we discover a new network-based classification?

Can we use the productivity of related bands across the years as the basis to find eras in music production?

The music scene has been the subject of extensive analysis using networks. Some works focus on music production as an import–export network between countries (Moon et al. 2010 ). Other model composers and performers as nodes connected by collaboration or friendship links (Stebbins 2004 ; Park et al. 2007 ; Gleiser and Danon 2003 ; Teitelbaum et al. 2008 ; McAndrew and Everett 2015 ). Studies investigate how music consumption can inform us about genres (Vlegels and Lievens 2017 ) and listeners influencing each other (Baym and Ledbetter 2009 ; Pennacchioli et al. 2013 ; Pálovics and Benczúr 2013 ). Differently from these studies, we do not focus on asking questions about the network structure itself. For our work, the network structure is interesting only insofar it is the mediator of the relationships between node attributes—the genres, years, and regions the bands are active on –, rather than being the focus of the analysis.

This is an important qualitative distinction, because if one wanted to perform our genre-regional analysis on the music collaboration network without our node attribute analysis, they would have to deal with complex n-partite objects—a player-band-year-genre-region network—which can become unwieldy and unintuitive. On the other hand, with our approach one can work with a unipartite projection of the player-band relationships, and use years, genres, and regions as node attributes, maintaining a highly intuitive representation.

Deep learning techniques and specifically deep neural networks can handle the richness of our data (Aljalbout et al. 2018 ; Aggarwal et al. 2018 ; Pang et al. 2021 ; Ezugwu et al. 2022 ). These approaches can attempt to learn, e.g., the true non-Euclidean distances between genres played by bands (Mahalanobis 1936 ; Xie et al. 2016 ). The problem is that this learning is severely limited if the space is defined by a complex network (Bronstein et al. 2017 ), as is the case here. Therefore, one would have to use Graph Neural Networks (GNN) (Scarselli et al. 2008 ; Wu et al. 2022 ; Zhou et al. 2020 ). However, GNNs focus on node analysis (Bo et al. 2020 ; Tsitsulin et al. 2020 ; Bianchi et al. 2020 ; Zhou et al. 2020 ), usually via finding the best way of creating node embeddings (Perozzi et al. 2014 ; Hamilton et al. 2017 ). GNNs only use node attributes for the purpose of aiding the analysis of nodes rather than analyzing the attributes themselves (Perozzi et al. 2014 ; Zhang et al. 2019 ; Wang et al. 2019 ; Lin et al. 2021 ; Cheng et al. 2021 ; Yang et al. 2023 ). Previous research shows that, when focusing on node attributes rather than on nodes, the techniques we use here are more suitable than adapting GNNs developed with a different focus (Damstrup et al. 2023 ).

Another class of alternative to deal with this data richness is to use hypergraphs (Bretto 2013 ) and high order networks (Bianconi 2021 ; Benson et al. 2016 ; Lambiotte et al. 2019 ; Xu et al. 2016 ). With these techniques, it is possible to analyze relationships involving multiple actors at the same time—rather than only dyadic relationships like in simpler network representations—and encode path dependencies—e.g. using high order random walks where a larger portion of the network is taken into account to decide which node to visit next (Kaufman and Oppenheim 2020 ; Carletti et al. 2020 ). While a comparative analysis between these techniques and the ones used in this paper is interesting, in this paper we exclusively focus on the usefulness of techniques based on node attribute analysis. We leave the comparison with hypergraphs and high order networks as a future work.

Our analysis shows that the node attribute techniques can help addressing a number of interesting research tasks in cultural data analytics. We show that we are able to describe the eclecticism required by music genres—or expressed in time periods –, by how dispersed they are on the music network. We can determine the geographical connection of specific genres, by estimating their correlation not merely based on how many bands from a specific region play a genre, but how bands not playing that genre relate with those that do. We can create new genre categories by looking at how close they are to each other on the music network. We can apply the same logic to discover eras in Italian music production, clustering years into coherent periods.

Finally, we show that our node attribute analysis rest on some assumptions that are likely to be true in our network—that bands tend to share artists if they play similar genres, in similar time periods, and hailing from similar regions.

We release our data as a public good freely accessible by anyone (Coscia 2024 ), along with all the code necessary to reproduce our analysis. Footnote 1

In this section we present our data model and a summary description of the data’s main features. Supplementary Material Section 1 provides all the details necessary to understand our choices when it comes to data collection, cleaning, and pre-processing.

To obtain a coherent network and to limit the scope of our data collection, we focus exclusively on the record credits from published Italian bands. The data from this project comes from crowd-sourced user-generated data. We mainly use Wikipedia Footnote 2 and Discogs. Footnote 3 We should note that these sources have a bias favoring English-speaking productions. While this bias does not affect our data collection too much, since we focus on Italy without comparing it to a different country/culture, it makes it more likely that there are Italian records without credits, or that are simply missing.

Our bipartite network data model. Artists in blue, bands in red. Edges are labeled with the first-last year in which the collaboration was active. The edge width is proportional to the weight, which is the number of years in which the artist participated to records released by the band

Figure  1 shows our data model, which is a bipartite network \(G = (V_1, V_2, E)\) . The nodes in the first class \(V_1\) are artists. An artist is a disambiguated physical real person. The nodes in the second class \(V_2\) are bands, which are identified by their name. Note that we consider solo artists as bands, and they are logically different from the artist with the same name. Note how in Fig.  1 we have two nodes labeled “Ginevra Di Marco”, one in red for the band and the other in blue for the artist.

Each edge \((v_1, v_2, t)\) —with \(v_1 \in V_1\) and \(v_2 \in V_2\) —connects an artist if they participated in a record of the band. The bipartite network is temporal. Each edge has a single attribute t reporting the year in which this performance happened. This implies that there are multiple edges between the same artist and the same band, one per year in which the connection existed—for notation convenience, we can use \(w_{v_1,v_2}\) to denote this count for an arbitrary node pair \((v_1, v_2)\) , since it is equivalent to the edge’s weight.

We have multiple attributes on the band. The attributes are divided in three classes. First, we have genres. We recover from Discogs 477 different genres/styles that have been used by at least one band in the network. Each of these genres is an attribute of the band, and the value of the attribute is the number of records the band has released with that genre. We use S to indicate the set of all genres, and show an example of these attributes in Table 1 (first section). The second attribute class is the one-hot encoded geographical region of origin, with each region being a binary vector equal to one if the band originates from the region, zero otherwise. We use R to indicate the set of regions. Table 1 (second section) shows a sample of the values of these attributes. The final attribute class is the activity status of a band in a given year—with Y being the set of years. Similarly to the geographical region, this is a one-hot encoded binary attribute. Table 1 (third section) shows a sample of the values of these attributes.

Summary description

For the remainder of the paper, we limit the scope of the analysis to a projection of our bipartite network. We focus on the band projection of the network, connecting bands if they share artists. We do so to keep the scope contained and show that even by looking at a limited perspective on the data, node attribute analysis can be versatile and open many possibilities. Supplementary Section 2 contains summary statistics about the bipartite network and the other projection—connecting artists with common bands.

There are many ways to perform this projection (Newman 2001 ; Zhou et al. 2007 ; Yildirim and Coscia 2014 ), which result in different edge weights. Here we weight edges by counting the number of years a shared artist has played for either band. Supplementary Material Section 1 contains more details about this weighting scheme. Since we care about the statistical significance—assuming a certain amount of noise in user-generated data—we deploy a network backboning technique to ensure we are not analyzing random fluctuations (Coscia and Neffke 2017 ).

Table 2 shows that the band projection has a low average degree and density, with high clustering coefficient and modularity—which indicate that one can find meaningful communities in the band projection. These are are typical characteristics of a wide variety of complex networks that can be found in the literature.

Table 3 summarizes the top 10 bands according to three standard centrality measures: degree, closeness, and betweenness centrality. Degree is biased by the density of the hip hop cluster—which, as we will see, is a large quasi-clique, including only hip hop bands. Closeness is mostly dominated by alternative rock bands, as they happen to be in the center of mass of the network. The top bands according to betweenness are those bands that are truly the bridges connecting different times, genres, and Italian regions. Note that we analyze the network as a cumulative structure, therefore these centrality rankings are prone to overemphasize bands that are in the central period of the network, as they naturally bridge the whole final structure. In other words, it is harder to be central for very recent or very old bands.

The temporal component of the band projection. Each node is a band. Edges connect bands with significant number of artist overlap. The edge’s color encodes its statistical significance (in increasing significance from bright to dark). The edge’s thickness is proportional to the overlap weight. The node’s size is proportional to its betweenness centrality. The node’s color encodes the average year of the band in the data—from blue (low year, less recent) to red (high year, more recent)

We visualize the band projection to show visually the driving forces behind the edge creation process: temporal and genre assortativity. For this reason we produce two visualizations. First, we take on the temporal component in Fig.  2 . The network has a clear temporal dimension, which we decide to place on a left-to-right axis in the visualization, going from older to more recent.

Second, we show the genre component in Fig.  3 , which instead causes clustering—the tendency of bands playing the same genre to connect to each other more than with any other band. For simplicity, we focus on the big three genres—pop, rock, and electronic—plus hip hop, since the latter creates the strongest and most evident cluster notwithstanding being less popular than the other three genres. For each node, if the band published more than a given threshold records in one of those four genres, we color the node with the most popular genre among them. If none of those genres meets the threshold, we count the band as playing an “other” generic category.

The genre component of the band projection. Same legend as Fig.  2 , except for the node’s color. Here, color encodes the dominant genre among pop (green), rock (red), electronic (purple), hip hop (blue), and other (gray)

This node categorization achieves a modularity score of 0.524, which is remarkably high considering that it uses no network information at all—and it is not a given that this is the correct number of communities. This is a sign that the network is strongly assortative by genre. With our division in four genres plus other, we observe an assortativity coefficient of 0.689, which is quite high. The assortativity coefficient for the average year of activity is even higher (0.91).

We omit showing the network using the regional information on the bands for two reason. First, there are too many regions (20) to visualize them by using different colors for nodes. Second, the structural relationship between the network and the regions is weaker—the assortativity coefficient being 0.223—which would lead to a less clear visualization.

From the figures and the preliminary analysis, it appears quite evident that the structure of the network has a set of complex and interesting interactions with time, genres, and, to a lesser extent, geography. This means that it is meaningful to use the network structure to estimate the relationship between genres, time, and space. This is the main topic of the paper and we now turn our attention to this analysis.

In this section we investigate a number of potential research questions in cultural data analytics. Each of them is tackled with a different node attribute analysis technique: network variance (Devriendt et al. 2022 ), network correlation (Coscia 2021 ; Coscia and Devriendt 2024 ), and Generalized Euclidean distance (Coscia 2020 )—which is at the basis of node attribute clustering (Damstrup et al. 2023 ) and era discovery. Supplementary Material Section 3 explains in details each of these methods.

Genre specialization

When focusing on the genre attributes of the nodes, their network variance can tell us how concentrated or dispersed they are in the network. A disperse genre means that the bands playing that genre do not share artists, not even indirectly: they are scattered in the structure. Vice versa, a low-variance genre implies that there is a clique of artist playing it, and they are shared by most of the bands releasing records with that particular genre. Table 4 reports the five most (and least) concentrated genres in the network.

We only focus on genres that have a minimum level of use, in this specific case at least 1% of bands must have released at least one record using that specific genre. The values of network variance should be compared with a null version of the genre—the values themselves do not tell us whether they are significant or if we would get that level of variance simply given the popularity of the genre. For this reason we bootstrap a pseudo p-value for the variance.

Let’s assume that \(\mathcal {S}\) is a \(|V| \times |S|\) genre matrix. The \(\mathcal {S}_{v,s}\) entry tells us how many records with genre s the band v has published. We can create \(\mathcal {S}'\) , a randomized null version of \(\mathcal {S}\) . In \(\mathcal {S}'\) , we ensure that each null genre has the same number of records as it has in \(\mathcal {S}\) . We do so by extracting with replacement at random \(\sum \limits _{v \in V} \mathcal {S}_{v,s}\) bands for genre s . The random extraction is not uniform: each band has a probability of being extracted proportional to \(\sum \limits _{s \in S} \mathcal {S}_{v,s}\) . In this way, \(\mathcal {S}'\) has the same column sum and similar row sum as \(\mathcal {S}\) . In other word, we randomize \(\mathcal {S}\) preserving the popularity of each genre and each band. Then, we can count the number of such random \(\mathcal {S}'\) s in which the null genre has a higher (lower) variance than the observed genre.

Table 4 shows that stoner rock has a high and significant variance, indicating that bands playing stoner rock have a low degree of specialization. This can be contextualized by the fact that stoner rock was tried out unsystematically by a few unrelated bands, ranging from heavy metal to indie rock. On the other hand, many variants of heavy metal have low variance. This can be explained by the fact that heavy metal is a niche genre in Italy, and all bands playing specific heavy metal variants know each other and share members.

Two genres ( a Hip Hop, b Beat) with different variance. Node size, node definition, and edge thickness, color, and definition is the same as Fig. 2 . The color is proportional to the genre-band node attribute value, with bright colors for low values and dark colors for high values

In Fig.  4 we pick two representative genres—Hip Hop and Beat—which both have the same relatively high popularity in number of bands playing them, and have a significant (low or high) variance and we show how they look like on the network. The figure shows that the variance measure does what we intuitively think it should be doing: the Hip Hop bands have low variance and therefore strongly cluster in the network, while the Beat bands are more scattered.

Temporal variety

We are not limited to the calculation of variances for genres: we can perform the same operation for the years. If the variance of a genre tells us how diverse the set of bands playing is, the variance of a year can tell us how diverse the year was. Figure  5 shows the evolution of variances per year. We test the statistical significance of the observed variance value by shuffling the values of the node attribute for a given year a number of times, testing whether the observation is significantly higher, lower, or equal to this expectation.

The network variance (y axis) for a given decade (x axis). Background color indicates the statistical significance: red = lower than expected, green = higher than expected, white = not significantly different from expectation

From the figure we can see that there seems to be two phase transitions. In the first regime, we have an infancy phase with low activity and low variance. The first phase transition starts in the year 1960 and brings the network to a second regime of high activity and high variance. After the peak around the year 1980, a second phase transition introduces the third regime from the mid 90 s until the present, with high activity but low variance. In the latter years, we see hip hop cannibalizing all genres and compressing the record releases to its tightly-knit cluster.

Node attribute correlation

We can now shift our attention from describing a single node attribute at a time—its variance as we saw in the previous sections—to describing the relationships between pairs of attributes. In this section, we do so by calculating their network correlation. Specifically, we want to make a geographical analysis. The ultimate aim is to answer the question: what are some particular strong genre-region associations? We can answer the question by calculating the network correlation between two node attributes, one recording the genre intensity for a band and the other a binary value telling us whether the band is from a specific region or not. The network correlation is useful here, because it grows not only if there are a lot of bands playing that specific genre in that specific region, but also if the other bands in the region that do not play that genre are close in the network to—i.e. share members with—bands playing that genre.

In Table 5 we report some significant region-genre associations. For each region, we pick the most popular genre in the network to which they correlate at a significant level—and they have the highest correlation among all other regions that correlate significantly to that genre. The significance is estimated via bootstrapping, by randomly shuffling the region vector—i.e. changing the set of bands associated to the region while respecting its size. Table 5 does not report a genre for all regions, because for some regions there was no genre satisfying the constraints. Note that some regions might correlate more strongly or more significantly with a genre that is not reported in the table, but we omit it if there was another region with a stronger correlation for that genre.

Genre clusters

When we measure the pairwise distance between all node attributes systematically we can cluster them hierarchically. Here, we do such a network-based hierarchical clustering on the music genres and styles as recorded by Discogs. The aim is to see whether we can find groups of genres that are similar to each other, potentially informing a data-driven musical classification. Figure  6 shows a bird’s eye view of the hierarchical clustering, with the similarity matrix and the dendrogram.

The hierarchical genre clusters. The heatmap shows the pairwise similarity among the genres—from low (dark) to high (bright) similarity. The dendrograms show the hierarchical organization of the clusters

To make sense of it, we have selected some clusters, for illustrative purposes only. Table 6 shows what genres and styles from Discogs end up in the color-highlighted clusters from Fig.  6 . We can see that the clusters include similar genres which make as a coherent set of more general music styles. The figure also highlights that there is a hierarchical structure of music styles, with meaningful clusters-within-clusters, and clear demarcation lines between groups and subgroups.

Recall that these clusters are driven exclusively by the network’s topology and do not use any feature coming from the songs themselves. This means that using a network of shared members among bands is indeed insightful in figuring our the related genres these bands play. Therefore, network-based clustering has the potential to guide the definition of new musical classifications.

Temporal clusters

We now look at the eras of Italian music we can discover in the data. Figure  7 shows the dendrogram, connecting years and groups of years at a small network distance to each other. Each era we identify colors its corresponding branch in the dendrogram. We avoid assigning an era for years pre-1906 and post-2018, due to issues with the representativeness of the data. We also notice that the 1938–1945 period is tumultuous, with many small eras in a handful of years, which is understandable given the geopolitical situation at a time, and so we ignore that period as well.

The eras dendrogram. Clusters join at a height proportional to their similarity level (the more right, the less similar). Colors encode the detected eras with labels on the left

To make sense of temporal clustering, the standard approach in the literature would be to compare counts of activities across clusters. However, that would ignore the role of the network structure. In our framework, we can characterize eras applying the same logic used to find them. We calculate the network distance between a node attribute representing the era and each genre. The era’s node attribute simply reports, for each band, a normalized count of records they released within the bounds of that era. We normalize so that each era attribute sums to one, to avoid overpowering the signal with the scale of the largest and most active eras.

Then, for each era, we report the list of genres that have the smallest distance with that era. Note that some genres might still have a small distance with other eras, but we only report the smallest. These are the genres we use to label the eras in Fig.  7 . These genres are not the most dominant in that era—in almost all cases, pop and rock dominate—but they give an intuition of what was the most characteristic genre of the era, distinguishing it from the others.

We can see that the characterization makes intuitive sense, with the classical genres being particularly correlated with the 1906–1916 era. Beat and rock’n’roll are particularly associated to the 1965–1971 period, the dates corresponding to the British Invasion in Italy. Notably, the punk genre has its closest association with the most recent era we label, 2006–2017, proving that—at least in Italy—punk is indeed not dead.

Explaining the network

Wrapping up the analysis, one key assumption that underpins the analysis we made so far is that the connections in the band projection follow a few homophily rules. We can have meaningful genre (Sect. Genre clusters  ) and temporal (Sect.  Temporal Clusters ) clusters using our network distance measures only if bands do tend to connect if they have a genre or temporal similarity. Two bands should be more likely to share members if they play similar genres and if they do it at a similar point in time. More weakly, correlations between genres and geographical regions (Sect. Node Attribute Correlation  ) also make sense if bands with similar geographical origins also tend to share members more often than expected.

While proving this assumption would require a paper on its own, we can at least provide some evidence in favor of its reasonableness. We do so by running two linear regressions. In the first regression, we want to explain the likelihood of an edge to exist in the band projection with the genre, temporal, and geographical similarity between bands, or:

In this formula:

\(Y_{u,v}\) is a binary variable, equal to 1 if bands u and v shared at least one member, and zero otherwise;

\(\mathcal {G}_{u,v}\) is the genre similarity, which is the cosine similarity between the vectors recording how many records of a given genre bands u and v have published;

\(\mathcal {R}_{u,v}\) is the region similarity, equal to 1 if the bands originate from the same region, and zero otherwise;

\(\mathcal {T}_{u,v}\) is the temporal similarity, in which we take the logarithm of the number of years in which both bands released a record, plus one to counter the issue when the bands did not share a year;

\(\beta _0\) and \(\epsilon \) are the intercept and the residuals.

Note that \(Y_{u,v}\) contains all links with weight of at least one, even those that are not statistically significant and were dropped from our visualizations and analyses from the previous sections. Moreover, it also has to contain all non-links. However, since the network is sparse, it is not feasible to have all non-links in the regression. Thus, we perform a balanced negative sampling: for each link that exists we sample and include in \(Y_{u,v}\) a link that does not.

For \(\mathcal {G}_{u,v}\) we only consider the most popular 38 genres, since sparsely used genres would make bands more similar than what they would otherwise be.

The first column of Table 7 shows the result of the model. The first thing we can see is that we can explain 28.4% of the variance in the likelihood of a edge to exist. This means that 71.6% of the reasons why two bands share a member is not in our data—be it unrecorded social networks, random chance, impositions from labels, etc.

However, explaining 28.4% of the variance in the edge existence likelihood still provides a valid clue that our homophily assumptions should hold. All similarities we considered play a role in determining the existence of an edge: all of their coefficients are positive and statistically significant. Given that these similarity measures do not share the same units—and not even the same domain –, one cannot compare the coefficients directly. However, we can map their contributions to the \(R^2\) by estimating their relative importance (Feldman 2005 ; Grömping 2007 ), which we do in Fig.  8 . From the figure we can see that it is the temporal similarity the one playing the strongest role, closely followed by genre similarity. Spatial similarity, on the other hand, while still being statistically significant, provides little to no additional explanatory power to the other factors.

The relative importance of each explanatory variable to determine the existence of a link between two bands in the band projection

Once we establish that the existence of the connection is related to genre, temporal, and geographical similarity, we can ask the same question about the strength of the relationship between two bands. We apply the same model as before, changing the target variable:

Here, \(\log (W_{u,v})\) is the logarithm of the edge weight. Note that here we only focus on those edges that have a non-zero weight, i.e. those that exist. This is because we do not want this model to try and predict also edge existence, beside its strength, as we already took care of that problem with the previous model.

Table 7 contains the results in its second column. We can see that, also in this case, all three factors are significant predictors of the edge weights. The number of artists two bands share goes up if the two bands play similar genres, with temporal overlap, and if they originate from the same region. The \(R^2\) is noticeably lower, though, which means that \(\log (W_{u,v})\) is harder to predict than \(Y_{u,v}\) .

Figure  9 shows the same \(R^2\) decomposition we did in Fig.  8 for \(Y_{u,v}\) . All explanatory variables explain less variance than in the previous model. Relative to each other, the temporal overlap is the factor gaining more importance than genre similarity.

The relative importance of each explanatory variable to determine the weight of a link between two bands in the band projection

In this paper we have provided a showcase of the analyses and conclusions one could do in cultural data analytics by using node attribute analysis. We focused on the case study of Italian music from the past 120 years. We built a bipartite network connecting artists to bands and then projected it to analyze a band-band network. We have shown how one could identify genres concentrating in such a network, hinting at clusters of bands playing homogeneous genres, using network variance. We have shown a geographical analysis, calculating the network correlation between the region of origin of bands and the genres they play. We have shown how one could create a new music genre taxonomy by performing node attribute clustering on music genre data. We also proposed a novel way of performing era detection in a network, by finding clusters of similar consecutive years, where years are node attributes.

While we believe our analysis is insightful, there are a number of considerations that need to be made to contextualize our work. We can broadly categorize the limitations in two categories: the one relating to the domain of analysis, and the methodological ones.

When it comes to cultural data analytics, we acknowledge the fact that we are working with user-generated data. There is no guarantee that the data is free from significant mistakes, misleading entries, and incompleteness. Furthermore, our results might not be conclusive. We process data semi-automatically, and the coding process is not complete, meaning we miss a considerable amount of the lesser known artists. This also means that there could be biases in the data collection, induced by our decision on the order in which we explore the structure—which might be focusing too much or too little on specific areas of Italian music. As a specific example, in our project we have ignored another potentially rich source of node attributes: information about the music labels/publishers. This is available on Discogs, and we could envision a label to be represented as a node vector, whose entries are the number of records a specific label published for a specific band. We plan to use this information for future work. The coding process is still ongoing, and we expect to be able to complete the network in the near future.

On the methodlogical side, we point out that what we did is only possible in the presence of rich metadata—dozens if not hundreds of node attributes. Networks with scarce node attribute data would not be amenable to be analyzed with the techniques we propose here. However, in cultural data analytics, there is usually a high richness of metadata. Furthermore, many of the node attribute techniques only make sense if the node attributes are somehow correlated with the network structure. The musical genre clustering or the era detection would not produce meaningful results if the probability of two nodes of connecting was not influenced by their attributes—i.e. if the homophily hypothesis does not hold. In our case, the homophily assumption likely holds, as we show in Sect.  Explaining the Network .

When considering some specific analyses we performed other limitations emerge. For instance, our era discovery approach exclusively looks at node activities. However, structural changes in the network’s connections also play a key role in determining discontinuities with the past (Berlingerio et al. 2013 ). We should explore in future work how to integrate our node attribute approach with structural methods. When it comes to the use of network variance, how to properly estimate its confidence intervals without using bootstrapping remains a future work. Therefore, the results we present here should be taken with caution, as it might be that some of the patterns we highlight are not statistically significant.

On a more practical side, our node attribute techniques hinge on specific matrix operations. While these can be efficiently computed on GPU using tensor representations, this might put a limit on the size of the networks analyzed, which have to fit in the GPU’s memory.

Availability of data and materials

All data and code necessary to replicate our results are available at http://www.michelecoscia.com/?page_id=2336 and Coscia ( 2024 ).

http://www.michelecoscia.com/?page_id=2336 .

https://it.wikipedia.org .

https://www.discogs.com/ .

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Acknowledgements

The author is thankful to Amy Ruskin for the project’s idea, and to Seth Pate and Clara Vandeweerdt for insightful discussions.

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Michele Coscia

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M.C. designed and performed all experiments, prepared figures, and wrote and approved the manuscript.

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Coscia, M. Node attribute analysis for cultural data analytics: a case study on Italian XX–XXI century music. Appl Netw Sci 9 , 56 (2024). https://doi.org/10.1007/s41109-024-00669-5

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Received : 17 May 2024

Accepted : 27 August 2024

Published : 05 September 2024

DOI : https://doi.org/10.1007/s41109-024-00669-5

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