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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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• Open access
• Published: 27 June 2011

The case study approach

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

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

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

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Introduction

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

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

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

What is a case study?

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

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

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

What are case studies used for?

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

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

How are case studies conducted?

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

Defining the case

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Conclusions

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

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Acknowledgements

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

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Writing a Case Study

What is a case study?

A Case study is: 

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

What are the different types of case studies?

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

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

Who are your case study participants?

What is triangulation ? 

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

How to write a Case Study?

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

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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

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

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

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

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

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

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

 Famous Case Studies

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

Clinical Case Studies

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

Child Psychology Case Studies

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

Types of Case Studies

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

Where Do You Find Data for a Case Study?

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

1. Primary sources

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

2. Secondary sources

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

3. Archival records

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

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

4. Organizational records

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

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

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

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

How do I Write a Case Study in Psychology?

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

1. Introduction

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

2. Case Presentation

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

3. Management and Outcome

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

4. Discussion

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

5. Additional Items

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

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

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

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

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

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

Limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

A Case Study is a research method involving a detailed examination and in-depth description of a particular empirical case. This can be done in many different ways, and the unit of analysis can vary (a person, an institution, a country, etc.). Case Studies can include both quantitative and qualitative evidence (Stake, 1995) and typically rely on bringing together many different articles of evidence from various sources to illuminate the case as a whole.

Case Studies benefit from having a developed theoretical framework before data collection begins (Yin, 2003). At the same time, the Case Study approach allows flexibility and can be used in exploratory contexts. This can be attractive to the researcher because it allows data collection to begin immediately (though there remains a need to impose a theoretical structure in the analysis phase). Consequently, Case Studies can be conducted at different levels of formality and replicability (Hetherington, 2013).

The case study research design can be used to test whether theories and models work in real contexts of application (Shuttleworth, 2008) and, conversely, to generate hypotheses and theories.

Case Study: GO-GN Insights

Sarah Hutton used a hermeneutic phenomenological case study to illuminate a direct connection between undergraduate student participation in courses with a participatory OER authorship or open access publishing of student artefacts model, to the development of internal goals and deepened engagement:

“Participatory OER development and an open pedagogical model provide the potential for students to have autonomous control over the development of course content, fostering greater intrinsic motivation, and therefore more successful and transferable learning outcomes. The resulting analysis creates a compelling case for the adoption of OER materials beyond the affordability argument, further advocating for the engagement of students in open scholarship at the undergraduate level.”

Viviane Vladimirschi explored evidence-based guidelines in the context of Teacher Professional Development (TPD) for Brazilian fundamental education public school teachers by undertaking an intervention in one school. The main goal of the OER Development Program was to raise awareness and build teachers’ knowledge regarding OER adoption and use:

“The case study methodology used in this research is a very common approach within Educational Studies. It is also a fairly easy method to use and the analysis of multiple sources of data have the potential to not only generate new insights throughout the case study but also generate new theory. Theory-building is very well-suited to new research areas, which was the case of this research. However, there are some disadvantages to using this methodology. First, it is not possible to generalize the findings from a single case study. Second, achieving the balance between producing an overly complex theory or a narrow idiosyncratic theory is quite challenging. Theory generated by case studies must be testable, replicable and coherent. The TPD guidelines generated by this research are testable, replicable and pretty straightforward so I am confident I managed to achieve this balance. The Design Thinking for Educators approach (please note that it is not a method) that I used in this research for the face-to-face workshops I highly recommend to any researcher who wishes to undertake an intervention, especially in the K-12 sector. This approach not only enables researchers to gain more insight into potential solutions for introducing new professional practices, but also affords teachers multiple opportunities to participate in the process of determining how innovation may be best implemented. Its only potential disadvantage is that it requires a longer period of time of application during each of its distinct phases to obtain bottom-up buy-in to an innovation.”

Useful references for Case Studies: Hetherington (2013); Shuttleworth (2008); Stake (1995); Yin (2003)

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

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• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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What Is a Case Study?

Weighing the pros and cons of this method of research

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

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

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

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

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

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

At a Glance

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

What Are the Benefits and Limitations of Case Studies?

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

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

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

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

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

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

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

Case Study Examples

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

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

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

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

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

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

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

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

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

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

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

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

Section 1: A Case History

This section will have the following structure and content:

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

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

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

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

Section 2: Treatment Plan

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

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

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

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

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

Need More Tips?

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

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

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

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

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

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

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

The Case Study as Research Method: A Practical Handbook

Qualitative Research in Accounting & Management

ISSN : 1176-6093

Article publication date: 21 June 2011

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

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

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

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

Assessing appropriateness and usefulness (4).

Ensuring accuracy of results (21).

Preparation (6).

Selecting cases (4).

Collecting data (7).

Analyzing data (4).

Interpreting data (3).

Reporting results (4).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Case Study Research: Methods and Designs

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

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

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

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

What Is The Case Study Method?

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

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

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

Here are the primary features of case study research:

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

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

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

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

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

Let’s look now at case study methodology.

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

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

2. Semi-structured Interview

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

3. Focus Group Interview

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

4. Participant Observation

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

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

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

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

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

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

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

1. Illustrative case study

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

2. Cumulative case study

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

3. Exploratory case study

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

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

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

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

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Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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The case study approach

Sarah crowe.

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

Kathrin Cresswell

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

Ann Robertson

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

Anthony Avery

Aziz sheikh.

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

Introduction

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

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

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

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

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

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

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

What is a case study?

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

Definitions of a case study

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

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

What are case studies used for?

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

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

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

How are case studies conducted?

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

Defining the case

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

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Potential pitfalls and mitigating actions when undertaking case study research

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

Conclusions

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

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

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

Pre-publication history

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

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

Acknowledgements

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

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• Published: 10 April 2024

An integrated design concept evaluation model based on interval valued picture fuzzy set and improved GRP method

• Qing Ma 1 ,
• Zhe Chen 1 ,
• Yuhang Tan 1 &
• Jianing Wei 1  

Scientific Reports volume  14 , Article number:  8433 ( 2024 ) Cite this article

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• Computational methods
• Computational science
• Information technology

The objective of this research is to enhance the precision and efficiency of design concept assessments during the initial stages of new product creation. Design concept evaluation, which occurs at the end of the conceptual design phase, is a critical step in product development. The outcome of this evaluation significantly impacts the product's eventual success, as flawed design concepts are difficult to remedy in later stages. However, the evaluation of new product concepts is a procedure that encompasses elements of subjectivity and ambiguity. In order to deal with the problem, a novel decision-making method for choosing more logical new product concepts is introduced. Basically, the evaluation process is outlined in three main phases: the construction of evaluation index system for design concept alternatives, the calculation of weights for evaluation criteria and decision-makers, the selection of the best design concept alternatives. These stages are composed of a hybrid method based on kano model, multiplicative analytic hierarchy process (AHP) method, the entropy of IVPFS and improved grey relational projection (GRP) under interval-valued picture fuzzy set (IVPFS). The novel approach integrates the strength of interval-valued picture fuzzy number in handling vagueness, the advantage of multiplicative AHP and the merit of improved GRP method in modelling multi-criteria decision-making. In final, the effectiveness of the proposed model is validated through comparisons with other models. The potential applications of this study include but are not limited to product development, industrial design, and innovation management, providing decision-makers with a more accurate and comprehensive design concept evaluation tool.

Introduction

New Product Development (NPD) is crucial for manufacturers to excel in competitive markets. As a key corporate function, NPD involves critical decision-making, with design concept evaluation being a standout step. This process assesses potential designs against criteria to select the most viable option. Since a large portion of a product's cost and quality is set in the conceptual phase, accurate evaluations are vital to avoid costly redesigns 1 , 2 . Effective evaluations also help managers quickly focus on promising ideas, streamlining development and boosting NPD success rates.

In the evaluation process of NPD, the uncertainty and ambiguity arise from the different cognitive levels and experiences of DMs. These factors can generate a negative impact on the evaluation process and the results of design concept. Therefore, how to eliminate information ambiguity is an important issue in product concept design evaluation 3 .

In order to solve the ambiguity and uncertainty of evaluation information for DMs, previous researchers have proposed interval set 4 , rough set 5 and fuzzy set (FS) 6 theories. The interval number provides DMs with a clearer understanding of the meaning of design choices. At the same time, it is more helpful for DMs to make wise decisions, considering uncertainty and change. However, interval theory oversimplifies practical problems when dealing with uncertainty, ignoring the fuzziness and probability distribution of parameters. FS, along with its extended forms such as intuitionistic fuzzy sets (IFS) 7 , hesitant fuzzy sets (HFS) 8 , neutrosophic set (NS) 9 , 10 , pythagorean fuzzy sets 11 , and picture fuzzy sets (PFS) 12 , can compensate for the deficiencies of interval sets. The combination of interval theory and FS can express the degree of uncertainty of parameters within intervals using fuzzy membership functions. Compared to extended forms, FS still falls short in describing the ambiguity and uncertainty of DMs’ evaluation information. For instance, FS only considers membership degrees without taking into account non-membership degrees, hesitation degrees, or degrees of abstention. This may be insufficient to fully describe the DMs’ preferences in practical situations, leading to inaccurate evaluation results.

In order to overcome the above issues, this study proposes a novel and reasonable framework to select design concept schemes. The main innovations and contributions of this study are organized as:

The first study applied to the mapping relation between CRs and the evaluation index to determine criteria of design concept.

This study effectively proposed the transformation of linguistic values to IVPFN to express DM evaluation information, which solves the uncertainty in the design concept evaluation process.

This study proposed improved GRP method to determine the best alternative in product design concept evaluation process.

The subsequent sections of this study are organized as follows: In Section “ Literature review ”, an overview of the relevant literature is presented. Section “ Basic preliminaries ” sets out various essential concepts within the IVPFS, introduces fundamental operating principles of IVPFN. Section “ Proposed methodology ” elaborates a distinctive framework for assessing and selecting design concept alternatives, incorporating the Kano model and an enhanced GRP method with IVPFS. To showcase the applicability of the proposed approach, a case study is expounded upon in Section “ Case study ”. Section “ Conclusion ” summarizes the findings of the study and explores potential future applications.

Literature review

Our research aims to assess design concept alternatives using the Kano model, IVPFS, and an improved GRP method. Consequently, the literature review is divided into three sections: (1) research on the Kano model, (2) research on uncertainty and fuzzy modeling in evaluation information. (3) research on ranking the schemes through improved GRP method under IVPFS.

Kano and his colleagues first put forth the Kano model 13 . The Kano model aims to categorize the features of a product or service based on their ability to meet customer needs. In practical terms, the properties of the Kano model can be classified into five groups, as illustrated in Fig.  1 and Table 1 .

Kano model.

Applying the Kano model to define quality categories aids designers in understanding customers’ actual requirements. This, in turn, enables more precise control over quality and satisfaction during the product design and development process 14 . Wu et al. 15 proposed that an evaluation procedure based on the Kano model is mainly to help identify attractive customer requirements (CRs) through the use of the Kano model. To capture CRs and provide inspiring insights for emotional design from the perspective of businesses, Jin et al. 16 created the Kansei-integrated Kano model. In our research, we utilize the Kano model to categorize CRs, identify the ultimate CRs, and establish the evaluation index system by mapping the connection between CRs and attributes.

Uncertainty and fuzzy modeling in evaluation information

In the process of design concept evaluation, the fuzziness of individual experience and knowledge of DMs leads to uncertainty in evaluation information 17 . To ensure the accuracy of evaluation results, interval theory and various FS have been introduced, including IFS, NS, Pythagorean fuzzy sets and PFS.

Interval theory represent fuzziness by defining upper and lower bounds. This method can more intuitively describe the uncertainty of DMs regarding evaluation information, especially suitable for situations where precise values are difficult to define. Jiang et al. 18 proposed a new interval comparison relation and applied it to interval number programming, and established two transformation models for linear and nonlinear interval number programming problems to solve practical engineering problems. Yao et al. 19 defined an interval number ordering method and its application considering symmetry axis compensation. The feasibility and validity of the method are also verified through examples. However, interval theory also faces the problem of insufficient accuracy, as they typically represent uncertainty through ranges and fail to provide detailed fuzzy membership functions. FS use membership functions to model fuzziness, but their simplification of varying degrees of fuzziness limit their expressive power when dealing with complex design information. IFS emphasize the subjective cognition and experience of DMs. Wang et al. 20 combined intuitionistic fuzzy sets with the VIKOR method for the project investment decision-making process. Zeng et al. 21 proposed the weighted intuitionistic fuzzy IOWA weighted average operator. And using the proposed operator, they also developed a procedure for solving multi-attribute group decision-making problems. Nevertheless, they have certain shortcomings, such as the inability to accurately express the attitudes or opinions of DMs including affirmation, neutrality, negation, and rejection. NS theory has more extensive applications than FS and IFS theory. However, the function values of the three membership functions in the NS are subsets of non-standard unit intervals, making it difficult to apply to practical problems. Compared to others, PFS as a novel form of FS, introduces concepts such as membership degree, non-membership degree, neutrality degree, and abstention degree, which more comprehensively considers the psychological state of DMs in evaluation. Membership degree describes the degree of belonging between elements and FS, non membership degree reflects the degree to which elements do not belong to FS, and abstention degree expresses the degree of uncertainty that DMs have about certain elements. This comprehensive consideration of different aspects of information makes the PFS more adaptable and can more accurately and comprehensively reflect the psychological state of DMs in actual decision-making situations, providing more accurate information support for design concept evaluation. Kahraman 22 proposed proportion-based models for PFS, facilitating the utilization of PFS by incorporating accurate data that more effectively reflects the judgments of DMs. Luo et al. 23 introduced a novel distance metric for PFS, employing three-dimensional divergence aggregation. This proposed distance metric is then utilized to address MCDM problems. Wang et al. 24 devised a multi-attributive border approximation area comparison method based on prospect theory in a picture fuzzy environment. The algorithm's applicability is demonstrated through a numerical example, highlighting its advantages.

However, in MCDM, due to the limitations of DMs' understanding of the decision object and the ambiguity of the decision environment, DMs are often faced with situations that are difficult to define precisely, and thus prefer to give an interval number. In order to better deal with this challenge, the IVPFS has been proposed 12 . The innovation of IVPFS lies in its ability to represent membership degree, non-membership degree, neutrality degree, and abstention degree in the form of interval numbers 25 , 26 . In contrast, the interval-valued Pythagorean fuzzy set is composed of three parts: membership degree, non-membership degree, and hesitancy degree 27 , 28 . IVPFS can better describe and express the uncertainty and fuzziness of DMs in practical decision-making. This theory is proposed to improve the credibility of decision-making outcomes thus enhancing the usefulness and adaptability of DMs participation in MCDM problems. Cao et al. 29 proposed an innovative similarity measure for IVPFS, taking into account the impact of the margin of the degree of refusal membership. Mahmood et al. 30 introduced the interval-valued picture fuzzy frank averaging operator, and discussed their properties. The relationship between IVPFS and other sets is shown in Table 2 .

Improved grey relational projection method

In the process of evaluating design concepts, one must choose a favorite from a multitude of options, a task that constitutes a MCDM issue. Traditional methods for solving the MCDM problem, including the AHP, TOPSIS method, EDAS method, and VIKOR method, which have the unique advantage of targeting specific decision scenarios. However, these methods generally have limitations in dealing with the early stages of design concept. As a multi-factor statistical analysis method, the GRP method excels in dealing with correlations between attributes. The main reasons for applying the GRP method to design concept evaluation are as follows. The GRP method's key benefits include easy-to-understand calculations, high accuracy, and reliance on actual data. In the decision-making process of design concept evaluation, each attribute is not independent of the others. Although the internal relationship is not clear, there is actually some correlation. In essence, it is a grey relationship. Therefore, in decision analysis of such a system, it is actually a grey MCDM problem. Decision making in the GRP approach is a mapping of the set of decision metrics. Once the set of attributes is identified, alternatives can be identified. This approach combines the effects of the entire decision indicator space. Especially when the attributes have discrete sample data, the GRP method avoids unilateral bias, i.e., the bias that arises from comparing a single attribute for each alternative, and thus integrates the analysis of the relationships between the indicators, reflecting the impact of the entire indicator space. Since most GRP methods are based on a single base point (the ideal alternative), our study builds on the existing literature and improves on the GRP method by determining the final score for each design alternative based on the IVPFS.

Table 3 contains a summary that compares the proposed technique to other multi-criteria concept evaluation approaches. These scholars investigated a number of potential aspects that could influence the decision-making process. However, significant obstacles remain in concept evaluation, which is the focus of this paper's research. To address the above issues thoroughly, a design concept evaluation technique is provided that incorporates the kano model, mapping relation, IVPFS, and improved GRP method to produce the best concept.

Basic preliminaries

We review several fundamental ideas in this section to provide some required background knowledge.

Construct the index of design concept evaluation

The Kano model finds extensive application in the realm of MCDM. The creation of the design concept evaluation indicator system, as proposed in this paper, primarily involves the following steps. First, relevant CRs for evaluating the design concept scheme are gathered. Then, employing the Kano model, requirement attributes are assessed, filtering out less critical requirements and retaining the most important ones. Ultimately, the evaluation index system for the design concept is formulated by establishing the mapping relationship between requirements and the evaluation indices.

Initially, we gathered and organized the primary CRs for the design concept schemes, as illustrated in Table 4 .

Next, we designed a questionnaire for CRs considering both a product with and without the same functional requirement. Each question in the questionnaire includes a description of the functional requirement to aid customers in comprehending its significance. To ensure uniform understanding among users, we provided consistent explanations for the meaning of the options in the questionnaire. This facilitates easy comprehension for users, allowing them to indicate their responses effectively. The design of the Kano questionnaire is presented in Table 5 .

Subsequently, we processed the feedback data from the returned questionnaires. Quantifying the two dimensions, namely “with function” and “without function,” we obtained an overlapping result by referencing Table 6 for the options corresponding to the scores. This approach allows us to discern the type of CRs.

The CRs established in this study are derived from an analysis of issues identified by research customers during product use in specific scenarios. The fulfillment of these requirements indicates customer satisfaction with the product’s usage. Consequently, the CRs serve as indicator factors for users to assess the design concept. The mapping relationship between the two is depicted in Fig.  2 .

The mapping relation between CRs and the evaluation index.

Ultimately, by excluding indicators that fall outside the scope of CRs, the evaluation index system for design concept alternatives based on CRs can be established.

The multiplicative AHP method

AHP is widely used for attribute weight determination, relying on an additive value function and making decisions through pairwise comparisons. However, AHP may encounter rank reversals, potentially leading to incorrect results. An enhanced method, the multiplicative AHP, addresses this by introducing a structured hierarchical approach, mitigating rank reversal issues associated with the original AHP 46 . In the multiplicative AHP method, DMs are tasked with comparing schemes in pairs and rendering decisions based on attributes. Subsequently, these judgments are aggregated, and the criteria weights are calculated using the compiled information 47 . The specific steps of the multiplicative AHP approach are as follows: Assume there are \(t\) experts in the decision-making group \(E\) , denoted as \(E=\{{e}_{1},{e}_{2},\dots ,{e}_{t}\}\) . \({A}_{j}\) and \({A}_{k}\) are two alternatives, the expert’s preference of \({A}_{j}\) and \({A}_{k}\) are present to two stimuli \({S}_{j}\) and \({S}_{k}\) , and expert \(e\) in group \(E\) is assigned to make pairwise comparisons according to an attribute by the linguistic information in Table 7 . The linguistic information is then converted into numerical scales denoted as \({\delta }_{jke}\) . Comparisons made by expert \(e\) are denoted as \({\delta }_{12e}\) , \({\delta }_{13e}\) ,…, \({\delta }_{23e}\) , \({\delta }_{24e}\) , … , \({\delta }_{(t-1)(t)e}\) . To eliminate the bias caused by the individual emotional factor, the comparisons with the expert themself are invalid and not included in the evaluation. Hence, for expert group \(E\) , the maximum number of valid judgements is \((t-1)(t-2)/2\) .

Step 1 : From the judgements made by the experts in group \(E\) , establish the decision matrix \({\{r}_{jke}\}\) by combining the judgements of the experts, denoted as:

Here the variant \(\mathrm{\gamma d}\) enotes a scale parameter commonly equal to \({\text{ln}}2\) , \(j=\mathrm{1,2},\dots ,t\) .

Step 2 : Determine the approximate vector \(p\) of stimulus values by the logarithmic least-squares method:

where \({S}_{jk}\) denotes the expert set who judged \({S}_{j}\) with respect to \({S}_{k}\) . Let \({\lambda }_{j}={\text{ln}}{p}_{j}\) , \({\lambda }_{k}={\text{ln}}{p}_{k}\) and \({q}_{jke}={\text{ln}}{r}_{jke}=\upgamma {\delta }_{jke}\) . Rewrite Eq. ( 2 ) with these substitutions as

Let \({N}_{jk}\) be the cardinality of the expert set \({S}_{jk}\) , Eq. ( 3 ) can be transferred to

If the comparisons including the expert are not considered, then

As the maximum pairwise comparison is \(\left(t-1\right)\left(t-2\right)\) , Eq. ( 4 ) can be rewritten as

A simplified style of the equation is

Step 3 : From Table 7 , for \({A}_{k}\) and \({A}_{j}\) , the sum of the numerical scale \({\delta }_{jke}\) and \({\delta }_{kje}\) is equal to 0, which means \({q}_{jky}=-{q}_{kjy}\) . Hence \({q}_{jjy}=0\) , so let \({\sum }_{k=1,k\ne j}^{t}{{\text{w}}}_{k}=0\) . Equation ( 7 ) can be further simplified and \({\lambda }_{j}\) can be determined as

Hence, the \({p}_{j}\) can be computed as:

Step 4 : Calculate the normalized weight \({w}_{j}\) determined by multiplicative AHP as

• Interval-valued picture fuzzy set

In 2013, Cuong et al. proposed a new concept of IVPFN to quantify vague DMs’perception based on the basic principles of IVPFS. IVPFN more accurately captures the genuine insights of DMs, thus increasing the objectivity of the evaluation data. According to Cuong et al., the definition of IVPFS is shown below.

Definition 1

12 Considering a designated domain of discourse denoted as \(X\) , where U [0,1] signifies the set of subintervals within the interval [0,1], and \(x\ne 0\) is a given set. In this study, the IVPFS is defined as follows:

The intervals \({\varrho }_{B}\left(x\right),{\xi }_{B}\left(x\right),{\upsilon }_{B}\left(x\right)\) represent positive, negative and neutral membership degrees of \(B\) , Additionally, \({\varrho }_{B}^{L}\left(x\right), {\varrho }_{B}^{U}\left(x\right), {\xi }_{B}^{L}\left(x\right), {\xi }_{B}^{U}\left(x\right), {\upsilon }_{B}^{L}\left(x\right), {\upsilon }_{B}^{U}\left(x\right)\) represent the lower and upper end points. Consequently, the IVPFS B can be expressed as:

where \({\varrho }_{B}^{L}\left(x\right)\ge 0, {\xi }_{B}^{L}\left(x\right)\ge 0 \& {\upsilon }_{B}^{L}\left(x\right)\ge 0\) and \(0\le {\varrho }_{B}^{U}\left(x\right)+{\xi }_{B}^{U}\left(x\right)+{\upsilon }_{B}^{U}\left(x\right)\le 1\) .Refusal membership degree expressed by \({\sigma }_{B}\) can be calculated using the Eq. ( 13 ).

Definition 2

48 Let that \({{\text{B}}}_{{\text{i}}}=(\left[{\varrho }_{{\text{i}}}^{{\text{L}}},{\varrho }_{{\text{i}}}^{{\text{U}}}\right],\left[{\xi }_{{\text{i}}}^{{\text{L}}},{\xi }_{{\text{i}}}^{{\text{U}}}\right],\left[{\upsilon }_{{\text{i}}}^{{\text{L}}},{\upsilon }_{{\text{i}}}^{{\text{U}}}\right])({\text{i}}=\mathrm{1,2},\ldots ,{\text{n}})\) be the IVPFN, \(\Omega\) is the set of IVPFNs. \(\upomega ={\left({\upomega }_{1},{\upomega }_{2},\ldots ,{\upomega }_{{\text{n}}}\right)}^{{\text{T}}}\) as the weight vector of them, a mapping IVPFOWIA: \({\Omega }^{{\text{n}}}\to\Omega\) of dimension n is an IVPFOWIA operator, with \(\sum_{i=1}^{n}{\omega }_{i}=1\) , \({\omega }_{i}=\left[\mathrm{0,1}\right]\) . Then,

Definition 3

49 For two IVPFNs \(A={(\varrho }_{A}\left(x\right),{\xi }_{A}\left(x\right),{\upsilon }_{A}\left(x\right))\) and \({B=(\varrho }_{B}\left(x\right),{\xi }_{B}\left(x\right),{\upsilon }_{B}\left(x\right))\) . \(\lambda\) as a scalar value \(\lambda >0\) . The following shows the basic and significant operations of IVPFS:

\(A\oplus B=\left(\left[{\varrho }_{A}^{L}+{\varrho }_{B}^{L}-{\varrho }_{A}^{L}{\varrho }_{B}^{L},{\varrho }_{A}^{U}+{\varrho }_{B}^{U}-{\varrho }_{A}^{U}{\varrho }_{B}^{U}\right],\left[{\xi }_{A}^{L}{\xi }_{B}^{L},{\xi }_{A}^{U}{\xi }_{B}^{U}\right],\left[{\upsilon }_{A}^{L}{\upsilon }_{B}^{L},{\upsilon }_{A}^{U}{\upsilon }_{B}^{U}\right]\right)\)

\(A\otimes B=([{\varrho }_{A}^{L}{\varrho }_{B}^{L},{\varrho }_{A}^{U}{\varrho }_{B}^{U}],[{\xi }_{A}^{L}+{\xi }_{B}^{L}-{\xi }_{A}^{L}{\xi }_{B}^{L},{\xi }_{A}^{U}+{\xi }_{B}^{U}-{\xi }_{A}^{U}{\eta }_{B}^{U}],[{\upsilon }_{A}^{L}+{\upsilon }_{B}^{L}-{\upsilon }_{A}^{L}{\upsilon }_{B}^{L},{\upsilon }_{A}^{U}+{\upsilon }_{B}^{U}-{\upsilon }_{A}^{U}{\upsilon }_{B}^{U}])\)

\({A}^{\lambda }=\left(\left[{\left({\varrho }_{A}^{L}\right)}^{\lambda },{\left({\varrho }_{A}^{U}\right)}^{\lambda }\right],\left[1-{\left(1-{\xi }_{A}^{L}\right)}^{\lambda },1-{\left(1-{\xi }_{A}^{U}\right)}^{\lambda }\right],\left[1-{\left(1-{\upsilon }_{A}^{L}\right)}^{\lambda },1-{\left(1-{\upsilon }_{A}^{U}\right)}^{\lambda }\right]\right)\)

\(\lambda A=\left(\left[1-{\left(1-{\varrho }_{A}^{L}\right)}^{\lambda },1-{\left(1-{\varrho }_{A}^{U}\right)}^{\lambda }\right],\left[{({\xi }_{A}^{L})}^{\lambda },{({\xi }_{A}^{U})}^{\lambda }\right],\left[{({\upsilon }_{A}^{L})}^{\lambda },{({\upsilon }_{A}^{U})}^{\lambda }\right]\right)\)

Definition 4

30 Let \({B}_{i}=(\left[{\varrho }_{{\text{i}}}^{{\text{L}}},{\varrho }_{{\text{i}}}^{{\text{U}}}\right],\left[{\xi }_{{\text{i}}}^{{\text{L}}},{\xi }_{{\text{i}}}^{{\text{U}}}\right],\left[{\upsilon }_{{\text{i}}}^{{\text{L}}},{\upsilon }_{{\text{i}}}^{{\text{U}}}\right])\) be an IVPFN, then the score function \(SF\left({B}_{i}\right)\) and the accuracy function \(AF\left({B}_{i}\right)\) of the IVPFNs can be described as:

Based on the \(SF\left({B}_{i}\right)\) and \(AF\) of each IVPFN, the comparison rules 50 between two IVPFNs are given as follows:

For any two IVPFNs \({B}_{1}, {B}_{2}\) ,

If \(SF\left({B}_{1}\right)> SF\left({B}_{2}\right)\) , then \({B}_{1}>{ B}_{2}\) ;

If \(SF\left({B}_{1}\right)= SF\left({B}_{2}\right)\) , then

If \(AF\left({B}_{1}\right)> AF\left({B}_{2}\right)\) , then \({B}_{1}>{ B}_{2};\)

If \(AF\left({B}_{1}\right)= AF\left({B}_{2}\right)\) , then \({B}_{1}={ B}_{2}\) .

Definition 5

Let \({B}_{1}=\left(\left[{\varrho }_{1}^{{\text{L}}},{\varrho }_{1}^{{\text{U}}}\right], \left[{\xi }_{1}^{{\text{L}}},{\xi }_{1}^{{\text{U}}}\right], \left[{\upsilon }_{1}^{{\text{L}}},{\upsilon }_{1}^{{\text{U}}}\right]\right)\) and \({B}_{2}=(\left[{\varrho }_{2}^{{\text{L}}},{\varrho }_{2}^{{\text{U}}}\right], \left[{\xi }_{2}^{{\text{L}}},{\xi }_{2}^{{\text{U}}}\right],\left[{\upsilon }_{2}^{{\text{L}}},{\upsilon }_{2}^{{\text{U}}}\right])\) represent two IVPFNs, The Hamming distance between \({B}_{1}\) and \({B}_{2}\) is defined as follows:

The Euclidean distance of \({B}_{1}\) and \({B}_{2}\) is as follows:

The entropy of interval-valued picture fuzzy set

In this section, the entropy of IVPFS method is used to calculate criteria weights 48 . This method can handle uncertainty more flexibly and effectively capture measurement errors and fuzziness in practical problems by describing the membership degree of criteria through intervals. The specific calculation formula is as follows:

Finally, use Eq. ( 19 ) to calculate the weight of the criteria.

for all \(j=\mathrm{1,2},\ldots ,n.\)

Proposed methodology

In this section, we introduce a new framework for selecting yacht design alternatives based on IVPFS and the enhanced GRP technique. The procedural phases of the IVPFS-Improved GRP method are illustrated in Fig.  3 , comprising three stages: (1) Construct the collective IVPF decision matrix, (2) Enhance the GRP method under IVPFS theory, and (3) case study. In phase 1, the evaluation index system of the design concept is established using the Kano model, and the weight of each DM is computed through the multiplicative AHP method. With the help of IVPFOWIA, the collective IVPF decision matrix is formulated. In phase 2, the GRP technique is improved within the context of IVPFS to calculate the relative grey relational projection for each alternative. Finally, in phase 3, leveraging the outcomes from phases 1 and 2, the final ranking of different design concept schemes is determined.

The process of the improved GRP method based on IVPFS.

For the MCDM problem of design concept evaluation, we denote the set of DMs as \(D=\left\{{D}_{1},{D}_{2},\dots ,{D}_{k}\right\}\) , the set of design criteria \(C=\left\{{C}_{1},{C}_{2},\cdots ,{C}_{n}\right\}\) , and the set of design schemes as \(A=\left\{{A}_{1},{A}_{2},\dots ,{A}_{m}\right\}\) . The weights of design criteria are presented by \(w=({w}_{1},{w}_{2},\cdots ,{w}_{j})\) , where \(\sum_{{\text{j}}=1}^{{\text{n}}}{{\text{w}}}_{{\text{j}}}=1, 0\le {{\text{w}}}_{{\text{j}}}\le 1\) . The next sections discuss the specifics of the established design alternative evaluation model based on these assumptions.

Phase 1: Construct the collective IVPF decision matrix

Step 1 : Establish the evaluation index evaluation system of design concept by the Kano model.

Step 2 : Generate the IVPF decision matrix for each DM.

where \({r}_{ij}^{(k)}=\left\{\left[{\varrho }_{ij}^{L(k)}, {\varrho }_{ij}^{U(k)}\right],\left[{\xi }_{ij}^{L(k)}, {\xi }_{ij}^{U(k)}\right],\left[{\upsilon }_{ij}^{L(k)}, {\upsilon }_{ij}^{U(k)}\right]\right\}\) represents an IVPFN. this IVPFN signifies the evaluation value of the alternatives \({A}_{i}\) concerning the criterion \({C}_{j}\) as provided by the DM \({D}_{k}\in D\) . And

To specify each \({r}_{ij}^{(k)}\) , a 5-scale evaluation was conducted throughout this process. Table 8 illustrates the details of these linguistic scales and their IVPFN equivalents.

Step 3 : Apply the multiplicative AHP approach to determine the weight for each DM.

In this stage, we calculate the weight of each DM using the multiplicative AHP approach.

Step 4 : Build the collective IVPF decision matrix.

To improve the GRP method in the process of group decision-making, it is essential to aggregate all individual decision matrices \({R}^{(k)}={\left({r}_{ij}^{(k)}\right)}_{m\times n}\) into the collective IVPF decision matrix \(\widetilde{R}={\left({\widetilde{r}}_{ij}\right)}_{m\times n}\) . This cluster is achieved through the application of the IVPFOWIA operator, as specified in Eq. ( 14 ):

Phase 2: Improve GRP method under IVPFS

Traditional GRP method is based on a single base point, and the similarity between the alternatives and the ideal solution is determined by calculating the cosine value of the angle between the alternatives and the ideal solution. Our research has improved the GRP method based on the existing literature by calculating the relative grey relation projection of each yacht design alternative based on the IVPFS theory as a way to select the optimal design alternative. The extended GRP method not only improves the accuracy of evaluation, but also enhances the rationality and effectiveness of decision-making. The specific steps of the improved GRP method are as follows:

Step 1 : Normalize the decision-making evaluation matrix. In MCDM, we distinguish between two types of criteria: benefit type and cost type. Consequently, the risk evaluation matrix \(\widetilde{R}={\left({\widetilde{r}}_{ij}\right)}_{m\times n}\) is transformed into a normalized decision matrix \({\widetilde{R}}^{*}={\left({\widetilde{r}}_{ij}^{*}\right)}_{m\times n}\) . Where:

For \(i=\mathrm{1,2},\cdots m,j=\mathrm{1,2}\cdots ,n\) .

Step 2 : Under the normalized evaluation decision matrix by Eq. ( 23 ).

(a) Determine the interval-valued picture fuzzy positive ideal solution (IVPF-PIS): \({{\text{R}}}^{+}\) can be obtained using Eq. ( 24 ):

(b) Determine the interval-valued picture fuzzy negative ideal solution (IVPF-NIS), \({{\text{R}}}^{-}\) can be determined using Eq. ( 25 ):

Step 3 : Calculate positive and negative correlation matrices.

Represent the gray correlation matrix between the i th sample and the positive (negative) ideal sample as \({\varphi }^{+}\) ( \({\varphi }^{-}\) ), where \({\varphi }_{ij}^{+} {\text{and}}\) \({\varphi }_{ij}^{-}\) are the individual elements:

where \(\rho\) is referred to as the resolution coefficient, serving to modify the scale of the comparison environment. \(\rho =0\) implies the absence of a surrounding environment, while \(\rho = 1\) signifies no alteration in the surrounding environment. Typically, \(\rho = 0.5\) . The term \(d\left({\widetilde{r}}_{ij},{\widetilde{r}}_{j}^{+(-)}\right)\) represents the distance between \({\widetilde{r}}_{ij}\) and \({\widetilde{r}}_{j}^{+}({\widetilde{r}}_{j}^{-})\) , calculable using Eq. ( 17 ).

Through the \({\varphi }_{ij}^{+\left(-\right)}\left(i=\mathrm{1,2},\cdots ,m,j=\mathrm{1,2},\cdots ,n\right)\) , we can construct the two grey relational coefficient matrices:

Step 4 : Construct the two weighted grey relational coefficient matrices.

Two weighted grey relational coefficient matrices \({\psi }^{+}={\left({\psi }_{ij}^{+}\right)}_{m\times n}\) and \({\psi }^{-}={\left({\psi }_{ij}^{-}\right)}_{m\times n}\) can be calculated by Eqs. ( 31 ) and ( 32 ), respectively.

where \({\psi }_{ij}^{+}={w}_{j}{\varphi }_{ij}^{+}\) , \({\psi }_{ij}^{-}={w}_{j}{\varphi }_{ij}^{-}\) . \({w}_{j}\) is the weight of the criterion \({C}_{j}\) , we can calculate it by Eqs. ( 18 ) and ( 19 ).

Step 5 : Calculate the grey relational projections of each scheme \({A}_{i} (i = \mathrm{1,2},\dots ,m)\) on the IVPF-PIS and IVPF-NIS, respectively.

Phase 3: Sort according to the final results and select the best design scheme

The relative grey relational projection of every alternative to the IVPF-PIS \({\psi }_{0}^{+}=\left({w}_{1},{w}_{2},\ldots ,{w}_{n}\right)\) is defined as follows:

The results are arranged in ascending order based on the values of \({\tau }_{i}\) . The relative closeness \({\tau }_{i}\) signifies the proximity of scheme \({A}_{i}\) to the ideal scheme. As the relative closeness become greater, the scheme improves.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Choosing the optimal alternative with the proposed methodology

In this phase, the aforementioned approach is employed to identify the optimal design among yacht alternatives. All DMs are seasoned experts in yacht design, possessing extensive design expertise. These DMs constitute an evaluation and selection group, comprising 10 members denoted as \(D=\left\{{D}_{1},{D}_{2},\ldots ,{D}_{10}\right\}\) , and considering three concept design alternatives \(A=\left\{{A}_{1},{A}_{2},{A}_{3}\right\}\) . The data, assessed by the 10 DMs, is represented as IVPFNs after statistical processing. Refer to the table below for the decision-making information. Following the outlined procedures of the proposed model, the specific steps for design concept evaluation are detailed as follows:

Step 1 : Determine the evaluation index evaluation system of design concept by the Kano model. First, we analyze the data through questionnaires, and the initial CRs for yacht design were determined as shown in Table 9 .

During Kano model evaluation on the attribute set shown in Table 9 , 126 questionnaires were issued and returned, including 120 valid results. The statistical results are shown in Table 10 .

According to Kano’s customer satisfaction model, the fundamental elements with A/M/O attributes are considered core requirements. By utilizing the mapping relationship shown in Fig.  2 , CRs are translated into evaluation criteria for the assessment of design concepts, as illustrated in Fig.  4 . It is crucial to understand that there is a unique, one-to-one correspondence in this mapping process.

The mapping relation of CRs- design concept evaluation index.

Step 2 : Construct the IVPF decision matrix for each DM.

Taking DM \({{\text{R}}}^{1}\) for example, the decision matrix for DM \({{\text{R}}}^{1}\) is built as shown in Table 11 . And all the DMs evaluated three yachts design alternatives \(A=\left\{{A}_{1},{A}_{2},{A}_{3}\right\}\) according to the attributes, as shown in Appendix A .

The linguistic evaluation value matrix in Table 8 can be converted into an IVPFN matrix through Table 11 , as shown in Table 12 .

Step 3 : Determine the weights of DMs by the multiplicative AHP approach.

With the help of the multiplicative AHP approach, we compute the weights of DMs \(\omega ={\left({\omega }_{1},{\omega }_{2},\dots ,{\omega }_{10}\right)}^{T}={\left(\mathrm{0.213,0.213,0.213,0.0533,0.0533,0.0533,0.0503,0.0503,0.0503,0.0503}\right)}^{T}\)

Step 4 : Construct the collective IVPF decision matrix.

Through the application of the IVPFOWIA, the collective decision matrix is derived, as depicted in Table 13 .

Step 5 : With the help of Eqs. ( 18 )–( 19 ), we can determine the entropy weights of IVPFS of \(C=\left\{{C}_{1},{C}_{2},{C}_{3},{C}_{4},{C}_{5},{C}_{6},{C}_{7},{C}_{8}\right\}\) is \(w={\left(\mathrm{0.167,0.133,0.37,0.048,0.119,0.223,0.090,0.082}\right)}^{T}\) .

Phase 2: Improved GRP method under IVPFS

Step 1 : Given that all eight criteria are benefits (not costs), according to Eq. ( 23 ), the standardized evaluation decision matrix aligns with the contents of Table 13 .

Step 2 : The IVPF-PIS and IVPF-NIS of the collective decision matrix are calculated through Eqs. ( 24 )–( 25 ).

Step 3 : Determine the grey relational coefficient matrices by Eqs. ( 29 ) and ( 30 ).

Step 4 : Calculate the weighted grey relational coefficient matrices through Eqs. ( 31 ) and ( 32 ), respectively.

Compute the grey relational projections of each alternative \({A}_{i} (i = \mathrm{1,2},3)\) on the IVPF-PIS and IVPF-NIS through Eqs. ( 33 )–( 35 ), respectively. The detailed parameters and alternatives are provided in Table 14 .

According to the \({\tau }_{i}\) , the ranking order is A 3 ≻ A 2 ≻ A 1 .

Sensitivity analysis

In this section, in order to further investigate the evaluation process of the IVPF-improved GRP method, a sensitivity analysis of the resolution coefficient \(\rho\) was conducted. When \(\rho =0.5\) , the ranking of the three design concept alternatives is A 3 ≻ A 2 ≻ A 1 . Table 15 shows the \({\tau }_{i}\) for different resolution coefficients \(\rho\) , and the corresponding figures are shown in Fig.  5 . As shown in Fig.  5 , A3 is consistently the optimal choice among the three design concept alternatives. It can be observed from Fig.  5 that as the resolution coefficient \(\rho\) changes, the gap between alternative 2 and alternative 3 gradually narrows. However, the ranking of the design concept alternatives remains unchanged (A 3 ≻ A 2 ≻ A 1 ). Therefore, the proposed improved GRP method based on IVPFS demonstrates stability and reliability in the evaluation of design concept alternatives.

Sensitivity analysis by different resolution coefficient \(\rho\) .

Alternatively, sensitivity analysis allows for a variety of change techniques. Because of space constraints, this research has only included the examples where the resolution coefficient \(\rho\) is employed. More extensions can be added to improve sensitivity analysis in the future research.

Comparative analysis and discussion

To assess the effectiveness of the proposed methodology, comparative studies are conducted alongside the case study, utilizing the Rough Entropy TOPSIS-PSI method 52 , Interval-Valued Intuitionistic Fuzzy (IVIF)-Improved GRP method, IVPF-VIKOR method 53 and IVPF-TOPSIS method. Table 16 and Fig.  6 present the results of a comprehensive comparison among different methodologies.

The close index between the four MAGDM methods.

From Fig.  6 it can be seen that \({A}_{3}\) represents the best alternative for yacht design through the Rough Entropy TOPSIS-PSI, IVPF-improved GRP, IVPF-VIKOR and IVPF-TOPSIS. From Fig.  6 , it can be seen that there are certain differences between different optimization models. These differences are reflected in the entire design optimization process or certain data processing stages. The specific details are summarized as follows:

Rough Entropy TOPSIS-PSI method: it is proposed by Chen, this method is fundamentally rooted in rough sets. The ranking approach emphasizes the subjectivity of the data, establishes a fuzzy environment using rough numbers, and finalizes scheme selection through proximity coefficients based on the TOPSIS method. Notably, this method does not consider DMs weights in the calculation process. Additionally, an interval weight calculation method based on entropy weight in the form of intervals is introduced for attribute weight calculation.

IVIF- Improved GRP method: The main difference between this method and our model is the fuzzy environment used. As a method based on IVIFS, the IVIF-Improved GRP method has been successful in applications, but as an extended form of interval fuzzy sets, it does not take into account the degree of neutral when describing uncertain information compared to IVPFS, which means that IVIFS are not as detailed as IVPFS when describing uncertainty. As detailed and accurate as the IVPFS.

IVPF-TOPSIS method: The IVPF-TOPSIS method differs from our proposed model in the ranking model; the IVPF-TOPSIS method ranks the alternatives based on relative proximity. This method may be computationally more time-consuming, especially when dealing with a large amount of data or multiple attributes, and is unable to focus on the trends and similarities of the data sequences, leading to inaccurate final ranking results.

IVPF-VIKOR method: In this method, uncertainty and ambiguity in the decision-making process are addressed due to the benefits of the IVPFS environment. VIKOR method is used to reflect multiple criteria inherited from the selection problem into the solution, however, the VIKOR method may be affected by outliers, which may lead to unstable decision results in the presence of extreme values or outliers. of instability in the presence of extreme values or outliers.

The comparison with the Rough Entropy TOPSIS-PSI method is presented in Table 17 . Despite certain dissimilarities between the two methods, they share a foundation in membership relationships and linguistic information. Ultimately, both approaches apply a compromise theory-based model for design concept scheme optimization and ranking. Additionally, the grey correlation projection value \({\tau }_{i}\) involved in our method bears similarity to the calculation form of the closeness coefficient \({CI}_{i}\) in the Rough Entropy TOPSIS-PSI method. The values of both exhibit a positive relationship within the interval [0,1]. Consequently, \({\tau }_{i}\) and \({CI}_{i}\) are compared, as depicted in Fig.  7 . The results indicate that the scheme ranking of the Rough Entropy TOPSIS-PSI method aligns with the method based on membership relationships proposed in this manuscript. In both cases, \({A}_{3}>{A}_{2}>{A}_{1}\) , signifying that \({A}_{3}\) is the optimal design concept scheme. Notably, the differentiation between the three schemes in the method introduced in this chapter is more pronounced, showcasing a greater level of distinction compared to the Rough Entropy TOPSIS-PSI method.

The Close Index between the two MAGDM methods.

Figure  8 presents a comparison between the method proposed in this paper, the IVIF-Improved GRP method, and the IVPF-TOPSIS method. The results of the method proposed in this study and the IVIF-Improved GRP method exhibit similarities. In comparison with the IVIF-Improved GRP method, our proposed model possesses distinct advantages in addressing MADM problems. As an extension of IVIFS, IVPFS incorporate an increased neutral membership degree, providing richer decision information and aligning more closely with human cognition.

The comparison among the proposed method and IVIF-Improved GRP and IVPF-TOPSIS.

Furthermore, the IVPF-TOPSIS method differs from the above two methods in the ranking model, leading to some variations in the results. However, the ranking among the schemes has not undergone significant changes. Consequently, we assert that our IVPF-Improved GRP approach, as proposed in this manuscript, is more reliable and accurate in decision-making processes.

The comparison of the method proposed in this study with the IVPF-VIKOR method is shown in Fig.  9 . From Fig.  9 it can be seen that \({A}_{3}\) is the best design concept alternative. However, except for alternative 3, which is consistent, there are some differences in the other ranking results of the two models. One reason for this is because each attribute is not independent of the other during the design concept evaluation process. Although the internal relationship is not clear, there is actually some correlation. the VIKOR method cannot handle the correlation between the indicators internally; the second reason is that when the attributes have discrete sample data, the improved GRP method can avoid the unilateral bias, which is the bias resulting from comparing a single attribute for each alternative, and thus comprehensively analyze the relationship between the criteria, reflecting the impact of the whole attribute space.

The comparison among the proposed method and IVPF-VIKOR method.

Ultimately, the improved GRP approach with IVPF can be adjusted to accommodate any quantity of alternatives, evaluation criteria, resulting in a minimal increase in its complexity. Consequently, this expanded version of the GRP method is applicable to addressing any MCDM issue within the context of IVPFS.

The evaluation of design concepts plays a crucial role in the product development process. The purpose of this study is to introduce an innovative approach for design concept evaluation, taking into account inherent ambiguity and uncertainty present in information. The main contributions of this research are summarized as follows:

Utilizing the Kano model, the mapping relation between CRs and the evaluation index, we construct the decision attributes set for the design concept evaluation.

By applying IVPFS theory, this research effectively identifies and characterizes ambiguity and uncertainty in design concept evaluation. Specifically, we adopt a practical approach, transforming linguistic information in concept design evaluation into IVPFNs, facilitating flexible decision-making procedures.

Enhancements to the GRP method leads to the construction of IVPF-PIS and IVPF-NIS. The distance relationship between each scheme and IVPF-PIS and IVPF-NIS is calculated, ultimately determining the optimal design concept scheme by comparing the relative grey relational projection of each scheme. This improvement avoids the problem of inaccurate results caused by traditional GRP methods based on calculations from a single base point.

Results from a real yacht design case demonstrate the success of our proposed method in addressing the challenges of evaluating product conceptual designs in uncertain and ambiguous environments. It was compared with the Rough Entropy TOPSIS-PSI, IVPF-improved GRP, IVPF-VIKOR and IVPF-TOPSIS method. The results also showed that this novel method can effectively evaluate product concept design schemes.

Furthermore, our research lays the groundwork for potential future outcomes, such as applications in green supply chain management, project ranking, urban planning, and environmental governance. Future studies also can further explore the applicability and effectiveness of this framework across different industries and decision-making contexts, as well as how to further optimize the model for broader applications.

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Ma, Q., Chen, Z., Tan, Y. et al. An integrated design concept evaluation model based on interval valued picture fuzzy set and improved GRP method. Sci Rep 14 , 8433 (2024). https://doi.org/10.1038/s41598-024-57960-9

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Case study of 4-year-old with Down syndrome and sleep apnea suggests procedure can be effective at young ages

by Mass General Brigham

While Obstructive Sleep Apnea (OSA) affects about 5% of the general pediatric population, 80% of children with Down syndrome experience OSA. Continual OSA results in poor health, including disruptions to cognitive development and functioning, leading physician-researchers from Mass General Brigham to investigate better methods to treat these patients as early as possible to maximize their health outcomes.

In a new case study published April 11 in Pediatrics , they report on a 4-year-old boy with Down syndrome and OSA who underwent a procedure to implant a hypoglossal nerve stimulation device, and experienced improvements thereafter.

Currently, adenoidectomies and tonsillectomies are among first-line treatments for pediatric OSA, however they are not always effective for children with Down syndrome because OSA can recur. Additionally, continuous positive airway pressure (CPAP) treatment, which streams compressed air into airways during sleep, is often not tolerated by children with Down syndrome due to sensory sensitivities.

The hypoglossal nerve stimulation device by Inspire has been an option increasingly used to treat OSA in adults since its 2014 FDA-approval. The device detects when the airway is blocked and sends an electrical pulse to the hypoglossal nerve that controls the tongue, causing it to move forward in the mouth, thereby opening the airway.

Positive treatment data in adults first led lead study author Christopher Hartnick, MD, director of Pediatric Otolaryngology at Mass Eye and Ear, to wonder whether the treatment may help his patients with Down syndrome whose lives were impacted by OSA.

With Mass General Brigham colleague Brian Skotko, MD, MPP, the Emma Campbell Endowed Chair on Down Syndrome at Massachusetts General Hospital, they organized a clinical trial looking at the safety and effectiveness of the procedure in children between the ages of 10 and 22 with Down syndrome.

Results of a 42-patient trial showing the benefits and safety of the procedure were published in 2022, leading to FDA approval for the device for adolescents with Down syndrome over the age of 13 nearly a year later.

These results spurred the researchers to examine whether the procedure could benefit younger children who are impacted by the physical and neurocognitive effects of OSA during pivotal developmental years.

Hartnick and Skotko identified a patient candidate, 4-year-old Theodore "Theo" Scott of Knoxville, Tenn., who had been on CPAP therapy since he was 1 year old.

After Hartnick and his team had extensive discussions about potential risks with colleagues in other medical specialties and Theo's parents, Rachel and Andrew Scott, a surgery took place in May 2023. The surgery was successful without complications, and the procedure was modified to allow for Theo's continued growth.

After one month, Theo experienced an improvement in sleep, and his obstructive apnea-hypopnea index (a measure of apnea severity) decreased by 40%. Additional follow-up care will take place for Theo to monitor effects of the procedure on neurocognition and surveillance of the device as he grows.

"The most significant change we have seen is the amount of sleep Theo is now getting, routinely over 10 hours a night versus what we experienced with CPAP where he would pull his mask off up to fifteen times a night. Theo sleeping through the night has also benefitted us as parents since we would need to get up and assist him, and we could each feel the toll poor sleep was taking on our health," Rachel and Andrew Scott said in a statement.

"We have also noticed Theo wakes up more easily in the morning and has a longer attention span than before the surgery, and his language development has accelerated from one-word statements to multiple word sentences. This procedure has absolutely been a game-changing intervention in Theo's life and in our family's."

Hartnick and Skotko are currently leading an NIH-sponsored 4-year trial examining the impact of upper airway stimulation on neurocognition and language in young patients with Down syndrome.

"Children with Down syndrome are disproportionally affected by obstructive sleep apnea and often don't benefit from traditional interventions, and research shows this impacts their cognitive development and IQ scores," said Hartnick.

"The potential long-term impact on neurocognition was a major driver of our team and the family's shared decision-making to pursue this treatment, and this case suggests it may be a possible option for some families.

"In our Down Syndrome Program, I see first-hand how frustrated families become when their child with Down syndrome runs out of options to treat significant sleep apnea," said Skotko. "Theo now opens up a new frontier for research and potential clinical care."

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Princeton University

Princeton engineering, microscopes reveal a frozen moment in cellular time. this new method records cells as they work..

By Julia Schwarz

April 8, 2024

A group of killer T cells surround a cancer cell. When a killer T cell makes contact with a target cell, it attaches and spreads over the cell to neutralize the danger. This is one of many examples of cell-cell interaction. Image by Alex Ritter, Jennifer Lippincott Schwartz and Gillian Griffiths, National Institutes of Health

Researchers at Princeton and Rockefeller University have found a new way to study cellular communication, recording interactions between cells as they work in a living organism and unlocking new ways to understand how our bodies function.

Cell interactions are essential to fighting disease and forming tissue, said Yuri Pritykin , assistant professor of computer science , the Lewis-Sigler Institute for Integrative Genomics and the Omenn-Darling Bioengineering Institute . He is one of two senior authors on the paper , published March 6 in Nature. “Nevertheless, most efforts in molecular biology have been spent studying what happens inside a cell, rather than interactions between cells,” said Pritykin.

This is partly because it is very difficult to know precisely which cells are interacting. While powerful microscopes can show the positions of cells within a slice of tissue, these images represent a frozen moment in cellular time. It’s possible to infer which cells might interact by looking at a microscopic image, said Pritykin, but this requires making a lot of assumptions.

“Looking at a slice of tissue will tell you which cells are next to each other, but just because cells are near each other doesn’t mean they are interacting,” said Pritykin. “Finally, we now have an accurate way to measure cellular interactions in a living organism.”

The work is a collaboration between molecular biologists, led by Gabriel D. Victora , associate professor at Rockefeller University, and computational biologists, led by Pritykin. The biologists developed the experimental work, and the computer scientists created an algorithm to analyze the complex data set the experiments produced.

The key discovery came from Victora’s lab, which found a way to genetically engineer a complex organism — in this case, a mouse — so some of its cells would produce a peptide that remains on any cell it interacts with. Genetic engineering also allows researchers to measure the levels of this peptide, enabling them to understand exactly which cells are interacting. Crucially, researchers were able to record interactions between different types of immune cells as well as interactions between immune cells and epithelial cells in the intestine.

A previous version of this technology, developed by Victora’s lab in 2018, used a similar method of cellular engineering, but it only recorded interactions between two molecules that occur on the surfaces of certain types of immune cells. While not entirely new, the updated method is nevertheless a breakthrough: it is the first time that researchers have demonstrated it can be used to track interactions between any type of cell, not just between the specific immune cells used in the first version of the technology.

The researchers chose to keep their focus on immune cells, however, because they are great cellular communicators — one of their primary roles is to move through the body and respond to different kinds of stimuli, said Pritykin. In their experiments, the researchers were able to record the interactions of immune cells as they performed their duties over the course of an infection.

There is one other reason this updated method is a breakthrough: it combines data on cell-cell interaction with single-cell sequencing. Researchers can, for the first time, understand what happens between cells and within cells simultaneously. The method reveals not only cellular interactions over the course of an infection but also exactly what a particular cell is doing through this process. “This combination is very powerful,” said Pritykin.

The combination also yields an incredibly detailed and complex dataset. Single cell sequencing collects data on each gene in a particular cell, with thousands of genes in a cell and tens of thousands of cells being measured at once. Add to that the data on cellular interactions.

To solve this challenge, Pritykin and Sarah Walker , doctoral student in Quantitative and Computational Biology, created series of computational algorithms specifically to interpret this type of data. Their analysis allowed the molecular biologists to realize the full potential of the experimental method, said Pritykin.

Now that researchers can track which cells interact in addition to what is happening inside those cells, one of the next steps is to determine why. “We don’t know why they are communicating,” said Pritykin, “or which genes are responsible for driving these physical interactions.”  This new method, he said, “paves the way for us to start asking these questions in a way that nobody has been able to ask before.”

The paper, “Universal recording of immune cell interactions in vivo” was published March 6 in Nature. Pritykin and Walker contributed from Princeton. In addition to Victora, co-authors from Rockefeller University include Sandra Nakandakari-Higa, Maria C.C. Canesso, Aleksey Chudnovskiy, Dong-Yoon Kim, Johanne T. Jacobsen, Roham Parsa, Jana Bilanovic, S. Martina Parigi, Karol Fiedorczuk, Elaine Fuchs, Angelina M. Bilate, Giulia Pasqual and Daniel Mucida. Verena van der Heide and Alice O. Kamphorst from the Icahn School of Medicine at Mount Sinai also contributed.

The work was supported by the National Institutes of Health, the Robertson Foundation, and the Ludwig Center for Cancer Research.

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