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

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

Published on May 8, 2019 by Shona McCombes . Revised on June 22, 2023.

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

Prevent plagiarism, run a free check.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 14 November 2023, from https://www.scribbr.co.uk/research-methods/case-studies/

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  • Roberta Heale 1 ,
  • Alison Twycross 2
  • 1 School of Nursing , Laurentian University , Sudbury , Ontario , Canada
  • 2 School of Health and Social Care , London South Bank University , London , UK
  • Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Sudbury, ON P3E2C6, Canada; rheale{at}laurentian.ca


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What is it?

Case study is a research methodology, typically seen in social and life sciences. There is no one definition of case study research. 1 However, very simply… ‘a case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. 1 A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 2

Often there are several similar cases to consider such as educational or social service programmes that are delivered from a number of locations. Although similar, they are complex and have unique features. In these circumstances, the evaluation of several, similar cases will provide a better answer to a research question than if only one case is examined, hence the multiple-case study. Stake asserts that the cases are grouped and viewed as one entity, called the quintain . 6  ‘We study what is similar and different about the cases to understand the quintain better’. 6

The steps when using case study methodology are the same as for other types of research. 6 The first step is defining the single case or identifying a group of similar cases that can then be incorporated into a multiple-case study. A search to determine what is known about the case(s) is typically conducted. This may include a review of the literature, grey literature, media, reports and more, which serves to establish a basic understanding of the cases and informs the development of research questions. Data in case studies are often, but not exclusively, qualitative in nature. In multiple-case studies, analysis within cases and across cases is conducted. Themes arise from the analyses and assertions about the cases as a whole, or the quintain, emerge. 6

Benefits and limitations of case studies

If a researcher wants to study a specific phenomenon arising from a particular entity, then a single-case study is warranted and will allow for a in-depth understanding of the single phenomenon and, as discussed above, would involve collecting several different types of data. This is illustrated in example 1 below.

Using a multiple-case research study allows for a more in-depth understanding of the cases as a unit, through comparison of similarities and differences of the individual cases embedded within the quintain. Evidence arising from multiple-case studies is often stronger and more reliable than from single-case research. Multiple-case studies allow for more comprehensive exploration of research questions and theory development. 6

Despite the advantages of case studies, there are limitations. The sheer volume of data is difficult to organise and data analysis and integration strategies need to be carefully thought through. There is also sometimes a temptation to veer away from the research focus. 2 Reporting of findings from multiple-case research studies is also challenging at times, 1 particularly in relation to the word limits for some journal papers.

Examples of case studies

Example 1: nurses’ paediatric pain management practices.

One of the authors of this paper (AT) has used a case study approach to explore nurses’ paediatric pain management practices. This involved collecting several datasets:

Observational data to gain a picture about actual pain management practices.

Questionnaire data about nurses’ knowledge about paediatric pain management practices and how well they felt they managed pain in children.

Questionnaire data about how critical nurses perceived pain management tasks to be.

These datasets were analysed separately and then compared 7–9 and demonstrated that nurses’ level of theoretical did not impact on the quality of their pain management practices. 7 Nor did individual nurse’s perceptions of how critical a task was effect the likelihood of them carrying out this task in practice. 8 There was also a difference in self-reported and observed practices 9 ; actual (observed) practices did not confirm to best practice guidelines, whereas self-reported practices tended to.

Example 2: quality of care for complex patients at Nurse Practitioner-Led Clinics (NPLCs)

The other author of this paper (RH) has conducted a multiple-case study to determine the quality of care for patients with complex clinical presentations in NPLCs in Ontario, Canada. 10 Five NPLCs served as individual cases that, together, represented the quatrain. Three types of data were collected including:

Review of documentation related to the NPLC model (media, annual reports, research articles, grey literature and regulatory legislation).

Interviews with nurse practitioners (NPs) practising at the five NPLCs to determine their perceptions of the impact of the NPLC model on the quality of care provided to patients with multimorbidity.

Chart audits conducted at the five NPLCs to determine the extent to which evidence-based guidelines were followed for patients with diabetes and at least one other chronic condition.

The three sources of data collected from the five NPLCs were analysed and themes arose related to the quality of care for complex patients at NPLCs. The multiple-case study confirmed that nurse practitioners are the primary care providers at the NPLCs, and this positively impacts the quality of care for patients with multimorbidity. Healthcare policy, such as lack of an increase in salary for NPs for 10 years, has resulted in issues in recruitment and retention of NPs at NPLCs. This, along with insufficient resources in the communities where NPLCs are located and high patient vulnerability at NPLCs, have a negative impact on the quality of care. 10

These examples illustrate how collecting data about a single case or multiple cases helps us to better understand the phenomenon in question. Case study methodology serves to provide a framework for evaluation and analysis of complex issues. It shines a light on the holistic nature of nursing practice and offers a perspective that informs improved patient care.

  • Gustafsson J
  • Calanzaro M
  • Sandelowski M

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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  • Open access
  • Published: 10 November 2020

Case study research for better evaluations of complex interventions: rationale and challenges

  • Sara Paparini   ORCID: orcid.org/0000-0002-1909-2481 1 ,
  • Judith Green 2 ,
  • Chrysanthi Papoutsi 1 ,
  • Jamie Murdoch 3 ,
  • Mark Petticrew 4 ,
  • Trish Greenhalgh 1 ,
  • Benjamin Hanckel 5 &
  • Sara Shaw 1  

BMC Medicine volume  18 , Article number:  301 ( 2020 ) Cite this article

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The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

Peer Review reports

The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 , 5 , 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 , 21 , 22 , 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

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This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

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Paparini, S., Green, J., Papoutsi, C. et al. Case study research for better evaluations of complex interventions: rationale and challenges. BMC Med 18 , 301 (2020). https://doi.org/10.1186/s12916-020-01777-6

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What the Case Study Method Really Teaches

  • Nitin Nohria

why use case study design

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

  • Nitin Nohria is a professor and former dean at Harvard Business School and the chairman of Thrive Capital, a venture capital firm based in New York.

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Case study research: opening up research opportunities

RAUSP Management Journal

ISSN : 2531-0488

Article publication date: 30 December 2019

Issue publication date: 3 March 2020

The case study approach has been widely used in management studies and the social sciences more generally. However, there are still doubts about when and how case studies should be used. This paper aims to discuss this approach, its various uses and applications, in light of epistemological principles, as well as the criteria for rigor and validity.


This paper discusses the various concepts of case and case studies in the methods literature and addresses the different uses of cases in relation to epistemological principles and criteria for rigor and validity.

The use of this research approach can be based on several epistemologies, provided the researcher attends to the internal coherence between method and epistemology, or what the authors call “alignment.”


This study offers a number of implications for the practice of management research, as it shows how the case study approach does not commit the researcher to particular data collection or interpretation methods. Furthermore, the use of cases can be justified according to multiple epistemological orientations.

  • Epistemology

Takahashi, A.R.W. and Araujo, L. (2020), "Case study research: opening up research opportunities", RAUSP Management Journal , Vol. 55 No. 1, pp. 100-111. https://doi.org/10.1108/RAUSP-05-2019-0109

Emerald Publishing Limited

Copyright © 2019, Adriana Roseli Wünsch Takahashi and Luis Araujo.

Published in RAUSP Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The case study as a research method or strategy brings us to question the very term “case”: after all, what is a case? A case-based approach places accords the case a central role in the research process ( Ragin, 1992 ). However, doubts still remain about the status of cases according to different epistemologies and types of research designs.

Despite these doubts, the case study is ever present in the management literature and represents the main method of management research in Brazil ( Coraiola, Sander, Maccali, & Bulgacov, 2013 ). Between 2001 and 2010, 2,407 articles (83.14 per cent of qualitative research) were published in conferences and management journals as case studies (Takahashi & Semprebom, 2013 ). A search on Spell.org.br for the term “case study” under title, abstract or keywords, for the period ranging from January 2010 to July 2019, yielded 3,040 articles published in the management field. Doing research using case studies, allows the researcher to immerse him/herself in the context and gain intensive knowledge of a phenomenon, which in turn demands suitable methodological principles ( Freitas et al. , 2017 ).

Our objective in this paper is to discuss notions of what constitutes a case and its various applications, considering epistemological positions as well as criteria for rigor and validity. The alignment between these dimensions is put forward as a principle advocating coherence among all phases of the research process.

This article makes two contributions. First, we suggest that there are several epistemological justifications for using case studies. Second, we show that the quality and rigor of academic research with case studies are directly related to the alignment between epistemology and research design rather than to choices of specific forms of data collection or analysis. The article is structured as follows: the following four sections discuss concepts of what is a case, its uses, epistemological grounding as well as rigor and quality criteria. The brief conclusions summarize the debate and invite the reader to delve into the literature on the case study method as a way of furthering our understanding of contemporary management phenomena.

2. What is a case study?

The debate over what constitutes a case in social science is a long-standing one. In 1988, Howard Becker and Charles Ragin organized a workshop to discuss the status of the case as a social science method. As the discussion was inconclusive, they posed the question “What is a case?” to a select group of eight social scientists in 1989, and later to participants in a symposium on the subject. Participants were unable to come up with a consensual answer. Since then, we have witnessed that further debates and different answers have emerged. The original question led to an even broader issue: “How do we, as social scientists, produce results and seem to know what we know?” ( Ragin, 1992 , p. 16).

An important step that may help us start a reflection on what is a case is to consider the phenomena we are looking at. To do that, we must know something about what we want to understand and how we might study it. The answer may be a causal explanation, a description of what was observed or a narrative of what has been experienced. In any case, there will always be a story to be told, as the choice of the case study method demands an answer to what the case is about.

A case may be defined ex ante , prior to the start of the research process, as in Yin’s (2015) classical definition. But, there is no compelling reason as to why cases must be defined ex ante . Ragin (1992 , p. 217) proposed the notion of “casing,” to indicate that what the case is emerges from the research process:

Rather than attempt to delineate the many different meanings of the term “case” in a formal taxonomy, in this essay I offer instead a view of cases that follows from the idea implicit in many of the contributions – that concocting cases is a varied but routine social scientific activity. […] The approach of this essay is that this activity, which I call “casing”, should be viewed in practical terms as a research tactic. It is selectively invoked at many different junctures in the research process, usually to resolve difficult issues in linking ideas and evidence.

In other words, “casing” is tied to the researcher’s practice, to the way he/she delimits or declares a case as a significant outcome of a process. In 2013, Ragin revisited the 1992 concept of “casing” and explored its multiple possibilities of use, paying particular attention to “negative cases.”

According to Ragin (1992) , a case can be centered on a phenomenon or a population. In the first scenario, cases are representative of a phenomenon, and are selected based on what can be empirically observed. The process highlights different aspects of cases and obscures others according to the research design, and allows for the complexity, specificity and context of the phenomenon to be explored. In the alternative, population-focused scenario, the selection of cases precedes the research. Both positive and negative cases are considered in exploring a phenomenon, with the definition of the set of cases dependent on theory and the central objective to build generalizations. As a passing note, it is worth mentioning here that a study of multiple cases requires a definition of the unit of analysis a priori . Otherwise, it will not be possible to make cross-case comparisons.

These two approaches entail differences that go beyond the mere opposition of quantitative and qualitative data, as a case often includes both types of data. Thus, the confusion about how to conceive cases is associated with Ragin’s (1992) notion of “small vs large N,” or McKeown’s (1999) “statistical worldview” – the notion that relevant findings are only those that can be made about a population based on the analysis of representative samples. In the same vein, Byrne (2013) argues that we cannot generate nomothetic laws that apply in all circumstances, periods and locations, and that no social science method can claim to generate invariant laws. According to the same author, case studies can help us understand that there is more than one ideographic variety and help make social science useful. Generalizations still matter, but they should be understood as part of defining the research scope, and that scope points to the limitations of knowledge produced and consumed in concrete time and space.

Thus, what defines the orientation and the use of cases is not the mere choice of type of data, whether quantitative or qualitative, but the orientation of the study. A statistical worldview sees cases as data units ( Byrne, 2013 ). Put differently, there is a clear distinction between statistical and qualitative worldviews; the use of quantitative data does not by itself means that the research is (quasi) statistical, or uses a deductive logic:

Case-based methods are useful, and represent, among other things, a way of moving beyond a useless and destructive tradition in the social sciences that have set quantitative and qualitative modes of exploration, interpretation, and explanation against each other ( Byrne, 2013 , p. 9).

Other authors advocate different understandings of what a case study is. To some, it is a research method, to others it is a research strategy ( Creswell, 1998 ). Sharan Merrian and Robert Yin, among others, began to write about case study research as a methodology in the 1980s (Merrian, 2009), while authors such as Eisenhardt (1989) called it a research strategy. Stake (2003) sees the case study not as a method, but as a choice of what to be studied, the unit of study. Regardless of their differences, these authors agree that case studies should be restricted to a particular context as they aim to provide an in-depth knowledge of a given phenomenon: “A case study is an in-depth description and analysis of a bounded system” (Merrian, 2009, p. 40). According to Merrian, a qualitative case study can be defined by the process through which the research is carried out, by the unit of analysis or the final product, as the choice ultimately depends on what the researcher wants to know. As a product of research, it involves the analysis of a given entity, phenomenon or social unit.

Thus, whether it is an organization, an individual, a context or a phenomenon, single or multiple, one must delimit it, and also choose between possible types and configurations (Merrian, 2009; Yin, 2015 ). A case study may be descriptive, exploratory, explanatory, single or multiple ( Yin, 2015 ); intrinsic, instrumental or collective ( Stake, 2003 ); and confirm or build theory ( Eisenhardt, 1989 ).

both went through the same process of implementing computer labs intended for the use of information and communication technologies in 2007;

both took part in the same regional program (Paraná Digital); and

they shared similar characteristics regarding location (operation in the same neighborhood of a city), number of students, number of teachers and technicians and laboratory sizes.

However, the two institutions differed in the number of hours of program use, with one of them displaying a significant number of hours/use while the other showed a modest number, according to secondary data for the period 2007-2013. Despite the context being similar and the procedures for implementing the technology being the same, the mechanisms of social integration – an idiosyncratic factor of each institution – were different in each case. This explained differences in their use of resource, processes of organizational learning and capacity to absorb new knowledge.

On the other hand, multiple case studies seek evidence in different contexts and do not necessarily require direct comparisons ( Stake, 2003 ). Rather, there is a search for patterns of convergence and divergence that permeate all the cases, as the same issues are explored in every case. Cases can be added progressively until theoretical saturation is achieved. An example is of a study that investigated how entrepreneurial opportunity and management skills were developed through entrepreneurial learning ( Zampier & Takahashi, 2014 ). The authors conducted nine case studies, based on primary and secondary data, with each one analyzed separately, so a search for patterns could be undertaken. The convergence aspects found were: the predominant way of transforming experience into knowledge was exploitation; managerial skills were developed through by taking advantages of opportunities; and career orientation encompassed more than one style. As for divergence patterns: the experience of success and failure influenced entrepreneurs differently; the prevailing rationality logic of influence was different; and the combination of styles in career orientation was diverse.

A full discussion of choice of case study design is outside the scope of this article. For the sake of illustration, we make a brief mention to other selection criteria such as the purpose of the research, the state of the art of the research theme, the time and resources involved and the preferred epistemological position of the researcher. In the next section, we look at the possibilities of carrying out case studies in line with various epistemological traditions, as the answers to the “what is a case?” question reveal varied methodological commitments as well as diverse epistemological and ontological positions ( Ragin, 2013 ).

3. Epistemological positioning of case study research

Ontology and epistemology are like skin, not a garment to be occasionally worn ( Marsh & Furlong, 2002 ). According to these authors, ontology and epistemology guide the choice of theory and method because they cannot or should not be worn as a garment. Hence, one must practice philosophical “self-knowledge” to recognize one’s vision of what the world is and of how knowledge of that world is accessed and validated. Ontological and epistemological positions are relevant in that they involve the positioning of the researcher in social science and the phenomena he or she chooses to study. These positions do not tend to vary from one project to another although they can certainly change over time for a single researcher.

Ontology is the starting point from which the epistemological and methodological positions of the research arise ( Grix, 2002 ). Ontology expresses a view of the world, what constitutes reality, nature and the image one has of social reality; it is a theory of being ( Marsh & Furlong, 2002 ). The central question is the nature of the world out there regardless of our ability to access it. An essentialist or foundationalist ontology acknowledges that there are differences that persist over time and these differences are what underpin the construction of social life. An opposing, anti-foundationalist position presumes that the differences found are socially constructed and may vary – i.e. they are not essential but specific to a given culture at a given time ( Marsh & Furlong, 2002 ).

Epistemology is centered around a theory of knowledge, focusing on the process of acquiring and validating knowledge ( Grix, 2002 ). Positivists look at social phenomena as a world of causal relations where there is a single truth to be accessed and confirmed. In this tradition, case studies test hypotheses and rely on deductive approaches and quantitative data collection and analysis techniques. Scholars in the field of anthropology and observation-based qualitative studies proposed alternative epistemologies based on notions of the social world as a set of manifold and ever-changing processes. In management studies since the 1970s, the gradual acceptance of qualitative research has generated a diverse range of research methods and conceptions of the individual and society ( Godoy, 1995 ).

The interpretative tradition, in direct opposition to positivism, argues that there is no single objective truth to be discovered about the social world. The social world and our knowledge of it are the product of social constructions. Thus, the social world is constituted by interactions, and our knowledge is hermeneutic as the world does not exist independent of our knowledge ( Marsh & Furlong, 2002 ). The implication is that it is not possible to access social phenomena through objective, detached methods. Instead, the interaction mechanisms and relationships that make up social constructions have to be studied. Deductive approaches, hypothesis testing and quantitative methods are not relevant here. Hermeneutics, on the other hand, is highly relevant as it allows the analysis of the individual’s interpretation, of sayings, texts and actions, even though interpretation is always the “truth” of a subject. Methods such as ethnographic case studies, interviews and observations as data collection techniques should feed research designs according to interpretivism. It is worth pointing out that we are to a large extent, caricaturing polar opposites rather characterizing a range of epistemological alternatives, such as realism, conventionalism and symbolic interactionism.

If diverse ontologies and epistemologies serve as a guide to research approaches, including data collection and analysis methods, and if they should be regarded as skin rather than clothing, how does one make choices regarding case studies? What are case studies, what type of knowledge they provide and so on? The views of case study authors are not always explicit on this point, so we must delve into their texts to glean what their positions might be.

Two of the cited authors in case study research are Robert Yin and Kathleen Eisenhardt. Eisenhardt (1989) argues that a case study can serve to provide a description, test or generate a theory, the latter being the most relevant in contributing to the advancement of knowledge in a given area. She uses terms such as populations and samples, control variables, hypotheses and generalization of findings and even suggests an ideal number of case studies to allow for theory construction through replication. Although Eisenhardt includes observation and interview among her recommended data collection techniques, the approach is firmly anchored in a positivist epistemology:

Third, particularly in comparison with Strauss (1987) and Van Maanen (1988), the process described here adopts a positivist view of research. That is, the process is directed toward the development of testable hypotheses and theory which are generalizable across settings. In contrast, authors like Strauss and Van Maanen are more concerned that a rich, complex description of the specific cases under study evolve and they appear less concerned with development of generalizable theory ( Eisenhardt, 1989 , p. 546).

This position attracted a fair amount of criticism. Dyer & Wilkins (1991) in a critique of Eisenhardt’s (1989) article focused on the following aspects: there is no relevant justification for the number of cases recommended; it is the depth and not the number of cases that provides an actual contribution to theory; and the researcher’s purpose should be to get closer to the setting and interpret it. According to the same authors, discrepancies from prior expectations are also important as they lead researchers to reflect on existing theories. Eisenhardt & Graebner (2007 , p. 25) revisit the argument for the construction of a theory from multiple cases:

A major reason for the popularity and relevance of theory building from case studies is that it is one of the best (if not the best) of the bridges from rich qualitative evidence to mainstream deductive research.

Although they recognize the importance of single-case research to explore phenomena under unique or rare circumstances, they reaffirm the strength of multiple case designs as it is through them that better accuracy and generalization can be reached.

Likewise, Robert Yin emphasizes the importance of variables, triangulation in the search for “truth” and generalizable theoretical propositions. Yin (2015 , p. 18) suggests that the case study method may be appropriate for different epistemological orientations, although much of his work seems to invoke a realist epistemology. Authors such as Merrian (2009) and Stake (2003) suggest an interpretative version of case studies. Stake (2003) looks at cases as a qualitative option, where the most relevant criterion of case selection should be the opportunity to learn and understand a phenomenon. A case is not just a research method or strategy; it is a researcher’s choice about what will be studied:

Even if my definition of case study was agreed upon, and it is not, the term case and study defy full specification (Kemmis, 1980). A case study is both a process of inquiry about the case and the product of that inquiry ( Stake, 2003 , p. 136).

Later, Stake (2003 , p. 156) argues that:

[…] the purpose of a case report is not to represent the world, but to represent the case. […] The utility of case research to practitioners and policy makers is in its extension of experience.

Still according to Stake (2003 , pp. 140-141), to do justice to complex views of social phenomena, it is necessary to analyze the context and relate it to the case, to look for what is peculiar rather than common in cases to delimit their boundaries, to plan the data collection looking for what is common and unusual about facts, what could be valuable whether it is unique or common:

Reflecting upon the pertinent literature, I find case study methodology written largely by people who presume that the research should contribute to scientific generalization. The bulk of case study work, however, is done by individuals who have intrinsic interest in the case and little interest in the advance of science. Their designs aim the inquiry toward understanding of what is important about that case within its own world, which is seldom the same as the worlds of researchers and theorists. Those designs develop what is perceived to be the case’s own issues, contexts, and interpretations, its thick descriptions . In contrast, the methods of instrumental case study draw the researcher toward illustrating how the concerns of researchers and theorists are manifest in the case. Because the critical issues are more likely to be know in advance and following disciplinary expectations, such a design can take greater advantage of already developed instruments and preconceived coding schemes.

The aforementioned authors were listed to illustrate differences and sometimes opposing positions on case research. These differences are not restricted to a choice between positivism and interpretivism. It is worth noting that Ragin’s (2013 , p. 523) approach to “casing” is compatible with the realistic research perspective:

In essence, to posit cases is to engage in ontological speculation regarding what is obdurately real but only partially and indirectly accessible through social science. Bringing a realist perspective to the case question deepens and enriches the dialogue, clarifying some key issues while sweeping others aside.

cases are actual entities, reflecting their operations of real causal mechanism and process patterns;

case studies are interactive processes and are open to revisions and refinements; and

social phenomena are complex, contingent and context-specific.

Ragin (2013 , p. 532) concludes:

Lurking behind my discussion of negative case, populations, and possibility analysis is the implication that treating cases as members of given (and fixed) populations and seeking to infer the properties of populations may be a largely illusory exercise. While demographers have made good use of the concept of population, and continue to do so, it is not clear how much the utility of the concept extends beyond their domain. In case-oriented work, the notion of fixed populations of cases (observations) has much less analytic utility than simply “the set of relevant cases,” a grouping that must be specified or constructed by the researcher. The demarcation of this set, as the work of case-oriented researchers illustrates, is always tentative, fluid, and open to debate. It is only by casing social phenomena that social scientists perceive the homogeneity that allows analysis to proceed.

In summary, case studies are relevant and potentially compatible with a range of different epistemologies. Researchers’ ontological and epistemological positions will guide their choice of theory, methodologies and research techniques, as well as their research practices. The same applies to the choice of authors describing the research method and this choice should be coherent. We call this research alignment , an attribute that must be judged on the internal coherence of the author of a study, and not necessarily its evaluator. The following figure illustrates the interrelationship between the elements of a study necessary for an alignment ( Figure 1 ).

In addition to this broader aspect of the research as a whole, other factors should be part of the researcher’s concern, such as the rigor and quality of case studies. We will look into these in the next section taking into account their relevance to the different epistemologies.

4. Rigor and quality in case studies

Traditionally, at least in positivist studies, validity and reliability are the relevant quality criteria to judge research. Validity can be understood as external, internal and construct. External validity means identifying whether the findings of a study are generalizable to other studies using the logic of replication in multiple case studies. Internal validity may be established through the theoretical underpinning of existing relationships and it involves the use of protocols for the development and execution of case studies. Construct validity implies defining the operational measurement criteria to establish a chain of evidence, such as the use of multiple sources of evidence ( Eisenhardt, 1989 ; Yin, 2015 ). Reliability implies conducting other case studies, instead of just replicating results, to minimize the errors and bias of a study through case study protocols and the development of a case database ( Yin, 2015 ).

Several criticisms have been directed toward case studies, such as lack of rigor, lack of generalization potential, external validity and researcher bias. Case studies are often deemed to be unreliable because of a lack of rigor ( Seuring, 2008 ). Flyvbjerg (2006 , p. 219) addresses five misunderstandings about case-study research, and concludes that:

[…] a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one.

theoretical knowledge is more valuable than concrete, practical knowledge;

the case study cannot contribute to scientific development because it is not possible to generalize on the basis of an individual case;

the case study is more useful for generating rather than testing hypotheses;

the case study contains a tendency to confirm the researcher’s preconceived notions; and

it is difficult to summarize and develop general propositions and theories based on case studies.

These criticisms question the validity of the case study as a scientific method and should be corrected.

The critique of case studies is often framed from the standpoint of what Ragin (2000) labeled large-N research. The logic of small-N research, to which case studies belong, is different. Cases benefit from depth rather than breadth as they: provide theoretical and empirical knowledge; contribute to theory through propositions; serve not only to confirm knowledge, but also to challenge and overturn preconceived notions; and the difficulty in summarizing their conclusions is because of the complexity of the phenomena studies and not an intrinsic limitation of the method.

Thus, case studies do not seek large-scale generalizations as that is not their purpose. And yet, this is a limitation from a positivist perspective as there is an external reality to be “apprehended” and valid conclusions to be extracted for an entire population. If positivism is the epistemology of choice, the rigor of a case study can be demonstrated by detailing the criteria used for internal and external validity, construct validity and reliability ( Gibbert & Ruigrok, 2010 ; Gibbert, Ruigrok, & Wicki, 2008 ). An example can be seen in case studies in the area of information systems, where there is a predominant orientation of positivist approaches to this method ( Pozzebon & Freitas, 1998 ). In this area, rigor also involves the definition of a unit of analysis, type of research, number of cases, selection of sites, definition of data collection and analysis procedures, definition of the research protocol and writing a final report. Creswell (1998) presents a checklist for researchers to assess whether the study was well written, if it has reliability and validity and if it followed methodological protocols.

In case studies with a non-positivist orientation, rigor can be achieved through careful alignment (coherence among ontology, epistemology, theory and method). Moreover, the concepts of validity can be understood as concern and care in formulating research, research development and research results ( Ollaik & Ziller, 2012 ), and to achieve internal coherence ( Gibbert et al. , 2008 ). The consistency between data collection and interpretation, and the observed reality also help these studies meet coherence and rigor criteria. Siggelkow (2007) argues that a case study should be persuasive and that even a single case study may be a powerful example to contest a widely held view. To him, the value of a single case study or studies with few cases can be attained by their potential to provide conceptual insights and coherence to the internal logic of conceptual arguments: “[…] a paper should allow a reader to see the world, and not just the literature, in a new way” ( Siggelkow, 2007 , p. 23).

Interpretative studies should not be justified by criteria derived from positivism as they are based on a different ontology and epistemology ( Sandberg, 2005 ). The rejection of an interpretive epistemology leads to the rejection of an objective reality: “As Bengtsson points out, the life-world is the subjects’ experience of reality, at the same time as it is objective in the sense that it is an intersubjective world” ( Sandberg, 2005 , p. 47). In this event, how can one demonstrate what positivists call validity and reliability? What would be the criteria to justify knowledge as truth, produced by research in this epistemology? Sandberg (2005 , p. 62) suggests an answer based on phenomenology:

This was demonstrated first by explicating life-world and intentionality as the basic assumptions underlying the interpretative research tradition. Second, based on those assumptions, truth as intentional fulfillment, consisting of perceived fulfillment, fulfillment in practice, and indeterminate fulfillment, was proposed. Third, based on the proposed truth constellation, communicative, pragmatic, and transgressive validity and reliability as interpretative awareness were presented as the most appropriate criteria for justifying knowledge produced within interpretative approach. Finally, the phenomenological epoché was suggested as a strategy for achieving these criteria.

From this standpoint, the research site must be chosen according to its uniqueness so that one can obtain relevant insights that no other site could provide ( Siggelkow, 2007 ). Furthermore, the view of what is being studied is at the center of the researcher’s attention to understand its “truth,” inserted in a given context.

The case researcher is someone who can reduce the probability of misinterpretations by analyzing multiple perceptions, searches for data triangulation to check for the reliability of interpretations ( Stake, 2003 ). It is worth pointing out that this is not an option for studies that specifically seek the individual’s experience in relation to organizational phenomena.

In short, there are different ways of seeking rigor and quality in case studies, depending on the researcher’s worldview. These different forms pervade everything from the research design, the choice of research questions, the theory or theories to look at a phenomenon, research methods, the data collection and analysis techniques, to the type and style of research report produced. Validity can also take on different forms. While positivism is concerned with validity of the research question and results, interpretivism emphasizes research processes without neglecting the importance of the articulation of pertinent research questions and the sound interpretation of results ( Ollaik & Ziller, 2012 ). The means to achieve this can be diverse, such as triangulation (of multiple theories, multiple methods, multiple data sources or multiple investigators), pre-tests of data collection instrument, pilot case, study protocol, detailed description of procedures such as field diary in observations, researcher positioning (reflexivity), theoretical-empirical consistency, thick description and transferability.

5. Conclusions

The central objective of this article was to discuss concepts of case study research, their potential and various uses, taking into account different epistemologies as well as criteria of rigor and validity. Although the literature on methodology in general and on case studies in particular, is voluminous, it is not easy to relate this approach to epistemology. In addition, method manuals often focus on the details of various case study approaches which confuse things further.

Faced with this scenario, we have tried to address some central points in this debate and present various ways of using case studies according to the preferred epistemology of the researcher. We emphasize that this understanding depends on how a case is defined and the particular epistemological orientation that underpins that conceptualization. We have argued that whatever the epistemological orientation is, it is possible to meet appropriate criteria of research rigor and quality provided there is an alignment among the different elements of the research process. Furthermore, multiple data collection techniques can be used in in single or multiple case study designs. Data collection techniques or the type of data collected do not define the method or whether cases should be used for theory-building or theory-testing.

Finally, we encourage researchers to consider case study research as one way to foster immersion in phenomena and their contexts, stressing that the approach does not imply a commitment to a particular epistemology or type of research, such as qualitative or quantitative. Case study research allows for numerous possibilities, and should be celebrated for that diversity rather than pigeon-holed as a monolithic research method.

why use case study design

The interrelationship between the building blocks of research

Byrne , D. ( 2013 ). Case-based methods: Why We need them; what they are; how to do them . Byrne D. In D Byrne. and C.C Ragin (Eds.), The SAGE handbooks of Case-Based methods , pp. 1 – 10 . London : SAGE Publications Inc .

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Dyer , W. G. , & Wilkins , A. L. ( 1991 ). Better stories, not better constructs, to generate better theory: a rejoinder to Eisenhardt . The Academy of Management Review , 16 , 613 – 627 .

Eisenhardt , K. ( 1989 ). Building theory from case study research . Academy of Management Review , 14 , 532 – 550 .

Eisenhardt , K. M. , & Graebner , M. E. ( 2007 ). Theory building from cases: Opportunities and challenges . Academy of Management Journal , 50 , 25 – 32 .

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Gibbert , M. , Ruigrok , W. , & Wicki , B. ( 2008 ). What passes as a rigorous case study? . Strategic Management Journal , 29 , 1465 – 1474 .

Gibbert , M. , & Ruigrok , W. ( 2010 ). The “what” and “how” of case study rigor: Three strategies based on published work . Organizational Research Methods , 13 , 710 – 737 .

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Author contributions: Both authors contributed equally.

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How to choose your study design


  • 1 Department of Medicine, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
  • PMID: 32479703
  • DOI: 10.1111/jpc.14929

Research designs are broadly divided into observational studies (i.e. cross-sectional; case-control and cohort studies) and experimental studies (randomised control trials, RCTs). Each design has a specific role, and each has both advantages and disadvantages. Moreover, while the typical RCT is a parallel group design, there are now many variants to consider. It is important that both researchers and paediatricians are aware of the role of each study design, their respective pros and cons, and the inherent risk of bias with each design. While there are numerous quantitative study designs available to researchers, the final choice is dictated by two key factors. First, by the specific research question. That is, if the question is one of 'prevalence' (disease burden) then the ideal is a cross-sectional study; if it is a question of 'harm' - a case-control study; prognosis - a cohort and therapy - a RCT. Second, by what resources are available to you. This includes budget, time, feasibility re-patient numbers and research expertise. All these factors will severely limit the choice. While paediatricians would like to see more RCTs, these require a huge amount of resources, and in many situations will be unethical (e.g. potentially harmful intervention) or impractical (e.g. rare diseases). This paper gives a brief overview of the common study types, and for those embarking on such studies you will need far more comprehensive, detailed sources of information.

Keywords: experimental studies; observational studies; research method.

© 2020 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

  • Case-Control Studies
  • Cross-Sectional Studies
  • Research Design*

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Single-Case Design, Analysis, and Quality Assessment for Intervention Research

Michele a. lobo.

1 Biomechanics & Movement Science Program, Department of Physical Therapy, University of Delaware, Newark, DE, USA

Mariola Moeyaert

2 Division of Educational Psychology & Methodology, State University of New York at Albany, Albany, NY, USA

Andrea Baraldi Cunha

Iryna babik, background and purpose.

The purpose of this article is to describe single-case studies, and contrast them with case studies and randomized clinical trials. We will highlight current research designs, analysis techniques, and quality appraisal tools relevant for single-case rehabilitation research.

Summary of Key Points

Single-case studies can provide a viable alternative to large group studies such as randomized clinical trials. Single case studies involve repeated measures, and manipulation of and independent variable. They can be designed to have strong internal validity for assessing causal relationships between interventions and outcomes, and external validity for generalizability of results, particularly when the study designs incorporate replication, randomization, and multiple participants. Single case studies should not be confused with case studies/series (ie, case reports), which are reports of clinical management of one patient or a small series of patients.

Recommendations for Clinical Practice

When rigorously designed, single-case studies can be particularly useful experimental designs in a variety of situations, even when researcher resources are limited, studied conditions have low incidences, or when examining effects of novel or expensive interventions. Readers will be directed to examples from the published literature in which these techniques have been discussed, evaluated for quality, and implemented.


The purpose of this article is to present current tools and techniques relevant for single-case rehabilitation research. Single-case (SC) studies have been identified by a variety of names, including “n of 1 studies” and “single-subject” studies. The term “single-case study” is preferred over the previously mentioned terms because previous terms suggest these studies include only one participant. In fact, as will be discussed below, for purposes of replication and improved generalizability, the strongest SC studies commonly include more than one participant.

A SC study should not be confused with a “case study/series “ (also called “case report”. In a typical case study/series, a single patient or small series of patients is involved, but there is not a purposeful manipulation of an independent variable, nor are there necessarily repeated measures. Most case studies/series are reported in a narrative way while results of SC studies are presented numerically or graphically. 1 , 2 This article defines SC studies, contrasts them with randomized clinical trials, discusses how they can be used to scientifically test hypotheses, and highlights current research designs, analysis techniques, and quality appraisal tools that may be useful for rehabilitation researchers.

In SC studies, measurements of outcome (dependent variables) are recorded repeatedly for individual participants across time and varying levels of an intervention (independent variables). 1 – 5 These varying levels of intervention are referred to as “phases” with one phase serving as a baseline or comparison, so each participant serves as his/her own control. 2 In contrast to case studies and case series in which participants are observed across time without experimental manipulation of the independent variable, SC studies employ systematic manipulation of the independent variable to allow for hypothesis testing. 1 , 6 As a result, SC studies allow for rigorous experimental evaluation of intervention effects and provide a strong basis for establishing causal inferences. Advances in design and analysis techniques for SC studies observed in recent decades have made SC studies increasingly popular in educational and psychological research. Yet, the authors believe SC studies have been undervalued in rehabilitation research, where randomized clinical trials (RCTs) are typically recommended as the optimal research design to answer questions related to interventions. 7 In reality, there are advantages and disadvantages to both SC studies and RCTs that should be carefully considered in order to select the best design to answer individual research questions. While there are a variety of other research designs that could be utilized in rehabilitation research, only SC studies and RCTs are discussed here because SC studies are the focus of this article and RCTs are the most highly recommended design for intervention studies. 7

When designed and conducted properly, RCTs offer strong evidence that changes in outcomes may be related to provision of an intervention. However, RCTs require monetary, time, and personnel resources that many researchers, especially those in clinical settings, may not have available. 8 RCTs also require access to large numbers of consenting participants that meet strict inclusion and exclusion criteria that can limit variability of the sample and generalizability of results. 9 The requirement for large participant numbers may make RCTs difficult to perform in many settings, such as rural and suburban settings, and for many populations, such as those with diagnoses marked by lower prevalence. 8 To rely exclusively on RCTs has the potential to result in bodies of research that are skewed to address the needs of some individuals while neglecting the needs of others. RCTs aim to include a large number of participants and to use random group assignment to create study groups that are similar to one another in terms of all potential confounding variables, but it is challenging to identify all confounding variables. Finally, the results of RCTs are typically presented in terms of group means and standard deviations that may not represent true performance of any one participant. 10 This can present as a challenge for clinicians aiming to translate and implement these group findings at the level of the individual.

SC studies can provide a scientifically rigorous alternative to RCTs for experimentally determining the effectiveness of interventions. 1 , 2 SC studies can assess a variety of research questions, settings, cases, independent variables, and outcomes. 11 There are many benefits to SC studies that make them appealing for intervention research. SC studies may require fewer resources than RCTs and can be performed in settings and with populations that do not allow for large numbers of participants. 1 , 2 In SC studies, each participant serves as his/her own comparison, thus controlling for many confounding variables that can impact outcome in rehabilitation research, such as gender, age, socioeconomic level, cognition, home environment, and concurrent interventions. 2 , 11 Results can be analyzed and presented to determine whether interventions resulted in changes at the level of the individual, the level at which rehabilitation professionals intervene. 2 , 12 When properly designed and executed, SC studies can demonstrate strong internal validity to determine the likelihood of a causal relationship between the intervention and outcomes and external validity to generalize the findings to broader settings and populations. 2 , 12 , 13

Single Case Research Designs for Intervention Research

There are a variety of SC designs that can be used to study the effectiveness of interventions. Here we discuss: 1) AB designs, 2) reversal designs, 3) multiple baseline designs, and 4) alternating treatment designs, as well as ways replication and randomization techniques can be used to improve internal validity of all of these designs. 1 – 3 , 12 – 14

The simplest of these designs is the AB Design 15 ( Figure 1 ). This design involves repeated measurement of outcome variables throughout a baseline control/comparison phase (A ) and then throughout an intervention phase (B). When possible, it is recommended that a stable level and/or rate of change in performance be observed within the baseline phase before transitioning into the intervention phase. 2 As with all SC designs, it is also recommended that there be a minimum of five data points in each phase. 1 , 2 There is no randomization or replication of the baseline or intervention phases in the basic AB design. 2 Therefore, AB designs have problems with internal validity and generalizability of results. 12 They are weak in establishing causality because changes in outcome variables could be related to a variety of other factors, including maturation, experience, learning, and practice effects. 2 , 12 Sample data from a single case AB study performed to assess the impact of Floor Play intervention on social interaction and communication skills for a child with autism 15 are shown in Figure 1 .

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An example of results from a single-case AB study conducted on one participant with autism; two weeks of observation (baseline phase A) were followed by seven weeks of Floor Time Play (intervention phase B). The outcome measure Circles of Communications (reciprocal communication with two participants responding to each other verbally or nonverbally) served as a behavioral indicator of the child’s social interaction and communication skills (higher scores indicating better performance). A statistically significant improvement in Circles of Communication was found during the intervention phase as compared to the baseline. Note that although a stable baseline is recommended for SC studies, it is not always possible to satisfy this requirement, as you will see in Figures 1 – 4 . Data were extracted from Dionne and Martini (2011) 15 utilizing Rohatgi’s WebPlotDigitizer software. 78

If an intervention does not have carry-over effects, it is recommended to use a Reversal Design . 2 For example, a reversal A 1 BA 2 design 16 ( Figure 2 ) includes alternation of the baseline and intervention phases, whereas a reversal A 1 B 1 A 2 B 2 design 17 ( Figure 3 ) consists of alternation of two baseline (A 1 , A 2 ) and two intervention (B 1 , B 2 ) phases. Incorporating at least four phases in the reversal design (i.e., A 1 B 1 A 2 B 2 or A 1 B 1 A 2 B 2 A 3 B 3 …) allows for a stronger determination of a causal relationship between the intervention and outcome variables, because the relationship can be demonstrated across at least three different points in time – change in outcome from A 1 to B 1 , from B 1 to A 2 , and from A 2 to B 2 . 18 Before using this design, however, researchers must determine that it is safe and ethical to withdraw the intervention, especially in cases where the intervention is effective and necessary. 12

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An example of results from a single-case A 1 BA 2 study conducted on eight participants with stable multiple sclerosis (data on three participants were used for this example). Four weeks of observation (baseline phase A 1 ) were followed by eight weeks of core stability training (intervention phase B), then another four weeks of observation (baseline phase A 2 ). Forward functional reach test (the maximal distance the participant can reach forward or lateral beyond arm’s length, maintaining a fixed base of support in the standing position; higher scores indicating better performance) significantly improved during intervention for Participants 1 and 3 without further improvement observed following withdrawal of the intervention (during baseline phase A 2 ). Data were extracted from Freeman et al. (2010) 16 utilizing Rohatgi’s WebPlotDigitizer software. 78

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An example of results from a single-case A 1 B 1 A 2 B 2 study conducted on two participants with severe unilateral neglect after a right-hemisphere stroke. Two weeks of conventional treatment (baseline phases A 1, A 2 ) alternated with two weeks of visuo-spatio-motor cueing (intervention phases B 1 , B 2 ). Performance was assessed in two tests of lateral neglect, the Bells Cancellation Test (Figure A; lower scores indicating better performance) and the Line Bisection Test (Figure B; higher scores indicating better performance). There was a statistically significant intervention-related improvement in participants’ performance on the Line Bisection Test, but not on the Bells Test. Data were extracted from Samuel at al. (2000) 17 utilizing Rohatgi’s WebPlotDigitizer software. 78

A recent study used an ABA reversal SC study to determine the effectiveness of core stability training in 8 participants with multiple sclerosis. 16 During the first four weekly data collections, the researchers ensured a stable baseline, which was followed by eight weekly intervention data points, and concluded with four weekly withdrawal data points. Intervention significantly improved participants’ walking and reaching performance ( Figure 2 ). 16 This A 1 BA 2 design could have been strengthened by the addition of a second intervention phase for replication (A 1 B 1 A 2 B 2 ). For instance, a single-case A 1 B 1 A 2 B 2 withdrawal design aimed to assess the efficacy of rehabilitation using visuo-spatio-motor cueing for two participants with severe unilateral neglect after a severe right-hemisphere stroke. 17 Each phase included 8 data points. Statistically significant intervention-related improvement was observed, suggesting that visuo-spatio-motor cueing might be promising for treating individuals with very severe neglect ( Figure 3 ). 17

The reversal design can also incorporate a cross over design where each participant experiences more than one type of intervention. For instance, a B 1 C 1 B 2 C 2 design could be used to study the effects of two different interventions (B and C) on outcome measures. Challenges with including more than one intervention involve potential carry-over effects from earlier interventions and order effects that may impact the measured effectiveness of the interventions. 2 , 12 Including multiple participants and randomizing the order of intervention phase presentations are tools to help control for these types of effects. 19

When an intervention permanently changes an individual’s ability, a return to baseline performance is not feasible and reversal designs are not appropriate. Multiple Baseline Designs (MBDs) are useful in these situations ( Figure 4 ). 20 MBDs feature staggered introduction of the intervention across time: each participant is randomly assigned to one of at least 3 experimental conditions characterized by the length of the baseline phase. 21 These studies involve more than one participant, thus functioning as SC studies with replication across participants. Staggered introduction of the intervention allows for separation of intervention effects from those of maturation, experience, learning, and practice. For example, a multiple baseline SC study was used to investigate the effect of an anti-spasticity baclofen medication on stiffness in five adult males with spinal cord injury. 20 The subjects were randomly assigned to receive 5–9 baseline data points with a placebo treatment prior to the initiation of the intervention phase with the medication. Both participants and assessors were blind to the experimental condition. The results suggested that baclofen might not be a universal treatment choice for all individuals with spasticity resulting from a traumatic spinal cord injury ( Figure 4 ). 20

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An example of results from a single-case multiple baseline study conducted on five participants with spasticity due to traumatic spinal cord injury. Total duration of data collection was nine weeks. The first participant was switched from placebo treatment (baseline) to baclofen treatment (intervention) after five data collection sessions, whereas each consecutive participant was switched to baclofen intervention at the subsequent sessions through the ninth session. There was no statistically significant effect of baclofen on viscous stiffness at the ankle joint. Data were extracted from Hinderer at al. (1990) 20 utilizing Rohatgi’s WebPlotDigitizer software. 78

The impact of two or more interventions can also be assessed via Alternating Treatment Designs (ATDs) . In ATDs, after establishing the baseline, the experimenter exposes subjects to different intervention conditions administered in close proximity for equal intervals ( Figure 5 ). 22 ATDs are prone to “carry-over effects” when the effects of one intervention influence the observed outcomes of another intervention. 1 As a result, such designs introduce unique challenges when attempting to determine the effects of any one intervention and have been less commonly utilized in rehabilitation. An ATD was used to monitor disruptive behaviors in the school setting throughout a baseline followed by an alternating treatment phase with randomized presentation of a control condition or an exercise condition. 23 Results showed that 30 minutes of moderate to intense physical activity decreased behavioral disruptions through 90 minutes after the intervention. 23 An ATD was also used to compare the effects of commercially available and custom-made video prompts on the performance of multi-step cooking tasks in four participants with autism. 22 Results showed that participants independently performed more steps with the custom-made video prompts ( Figure 5 ). 22

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An example of results from a single case alternating treatment study conducted on four participants with autism (data on two participants were used for this example). After the observation phase (baseline), effects of commercially available and custom-made video prompts on the performance of multi-step cooking tasks were identified (treatment phase), after which only the best treatment was used (best treatment phase). Custom-made video prompts were most effective for improving participants’ performance of multi-step cooking tasks. Data were extracted from Mechling at al. (2013) 22 utilizing Rohatgi’s WebPlotDigitizer software. 78

Regardless of the SC study design, replication and randomization should be incorporated when possible to improve internal and external validity. 11 The reversal design is an example of replication across study phases. The minimum number of phase replications needed to meet quality standards is three (A 1 B 1 A 2 B 2 ), but having four or more replications is highly recommended (A 1 B 1 A 2 B 2 A 3 …). 11 , 14 In cases when interventions aim to produce lasting changes in participants’ abilities, replication of findings may be demonstrated by replicating intervention effects across multiple participants (as in multiple-participant AB designs), or across multiple settings, tasks, or service providers. When the results of an intervention are replicated across multiple reversals, participants, and/or contexts, there is an increased likelihood a causal relationship exists between the intervention and the outcome. 2 , 12

Randomization should be incorporated in SC studies to improve internal validity and the ability to assess for causal relationships among interventions and outcomes. 11 In contrast to traditional group designs, SC studies often do not have multiple participants or units that can be randomly assigned to different intervention conditions. Instead, in randomized phase-order designs , the sequence of phases is randomized. Simple or block randomization is possible. For example, with simple randomization for an A 1 B 1 A 2 B 2 design, the A and B conditions are treated as separate units and are randomly assigned to be administered for each of the pre-defined data collection points. As a result, any combination of A-B sequences is possible without restrictions on the number of times each condition is administered or regard for repetitions of conditions (e.g., A 1 B 1 B 2 A 2 B 3 B 4 B 5 A 3 B 6 A 4 A 5 A 6 ). With block randomization for an A 1 B 1 A 2 B 2 design, two conditions (e.g., A and B) would be blocked into a single unit (AB or BA), randomization of which to different time periods would ensure that each condition appears in the resulting sequence more than two times (e.g., A 1 B 1 B 2 A 2 A 3 B 3 A 4 B 4 ). Note that AB and reversal designs require that the baseline (A) always precedes the first intervention (B), which should be accounted for in the randomization scheme. 2 , 11

In randomized phase start-point designs , the lengths of the A and B phases can be randomized. 2 , 11 , 24 – 26 For example, for an AB design, researchers could specify the number of time points at which outcome data will be collected, (e.g., 20), define the minimum number of data points desired in each phase (e.g., 4 for A, 3 for B), and then randomize the initiation of the intervention so that it occurs anywhere between the remaining time points (points 5 and 17 in the current example). 27 , 28 For multiple-baseline designs, a dual-randomization, or “regulated randomization” procedure has been recommended. 29 If multiple-baseline randomization depends solely on chance, it could be the case that all units are assigned to begin intervention at points not really separated in time. 30 Such randomly selected initiation of the intervention would result in the drastic reduction of the discriminant and internal validity of the study. 29 To eliminate this issue, investigators should first specify appropriate intervals between the start points for different units, then randomly select from those intervals, and finally randomly assign each unit to a start point. 29

Single Case Analysis Techniques for Intervention Research

The What Works Clearinghouse (WWC) single-case design technical documentation provides an excellent overview of appropriate SC study analysis techniques to evaluate the effectiveness of intervention effects. 1 , 18 First, visual analyses are recommended to determine whether there is a functional relation between the intervention and the outcome. Second, if evidence for a functional effect is present, the visual analysis is supplemented with quantitative analysis methods evaluating the magnitude of the intervention effect. Third, effect sizes are combined across cases to estimate overall average intervention effects which contributes to evidence-based practice, theory, and future applications. 2 , 18

Visual Analysis

Traditionally, SC study data are presented graphically. When more than one participant engages in a study, a spaghetti plot showing all of their data in the same figure can be helpful for visualization. Visual analysis of graphed data has been the traditional method for evaluating treatment effects in SC research. 1 , 12 , 31 , 32 The visual analysis involves evaluating level, trend, and stability of the data within each phase (i.e., within-phase data examination) followed by examination of the immediacy of effect, consistency of data patterns, and overlap of data between baseline and intervention phases (i.e., between-phase comparisons). When the changes (and/or variability) in level are in the desired direction, are immediate, readily discernible, and maintained over time, it is concluded that the changes in behavior across phases result from the implemented treatment and are indicative of improvement. 33 Three demonstrations of an intervention effect are necessary for establishing a functional relation. 1

Within-phase examination

Level, trend, and stability of the data within each phase are evaluated. Mean and/or median can be used to report the level, and trend can be evaluated by determining whether the data points are monotonically increasing or decreasing. Within-phase stability can be evaluated by calculating the percentage of data points within 15% of the phase median (or mean). The stability criterion is satisfied if about 85% (80% – 90%) of the data in a phase fall within a 15% range of the median (or average) of all data points for that phase. 34

Between-phase examination

Immediacy of effect, consistency of data patterns, and overlap of data between baseline and intervention phases are evaluated next. For this, several nonoverlap indices have been proposed that all quantify the proportion of measurements in the intervention phase not overlapping with the baseline measurements. 35 Nonoverlap statistics are typically scaled as percent from 0 to 100, or as a proportion from 0 to 1. Here, we briefly discuss the Nonoverlap of All Pairs ( NAP ), 36 the Extended Celeration Line ( ECL ), the Improvement Rate Difference ( IRD) , 37 and the TauU and the TauU-adjusted, TauU adj , 35 as these are the most recent and complete techniques. We also examine the Percentage of Nonoverlapping Data ( PND ) 38 and the Two Standard Deviations Band Method, as these are frequently used techniques. In addition, we include the Percentage of Nonoverlapping Corrected Data ( PNCD ) – an index applying to the PND after controlling for baseline trend. 39

Nonoverlap of all pairs (NAP)

Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., N = n A * n B ). Count the number of overlapping pairs, n o , counting all ties as 0.5. Then define the percent of the pairs that show no overlap. Alternatively, one can count the number of positive (P), negative (N), and tied (T) pairs 2 , 36 :

Extended Celeration Line (ECL)

ECL or split middle line allows control for a positive Phase A trend. Nonoverlap is defined as the proportion of Phase B ( n b ) data that are above the median trend plotted from Phase A data ( n B< sub > Above Median trend A </ sub > ), but then extended into Phase B: ECL = n B Above Median trend A n b ∗ 100

As a consequence, this method depends on a straight line and makes an assumption of linearity in the baseline. 2 , 12

Improvement rate difference (IRD)

This analysis is conceptualized as the difference in improvement rates (IR) between baseline ( IR B ) and intervention phases ( IR T ). 38 The IR for each phase is defined as the number of “improved data points” divided by the total data points in that phase. IRD, commonly employed in medical group research under the name of “risk reduction” or “risk difference” attempts to provide an intuitive interpretation for nonoverlap and to make use of an established, respected effect size, IR B - IR B , or the difference between two proportions. 37

TauU and TauU adj

Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., n = n A * n B ). Count the number of positive (P), negative (N), and tied (T) pairs, and use the following formula: TauU = P - N P + N + τ

The TauU adj is an adjustment of TauU for monotonic trend in baseline. Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., n = n A * n B ). Each baseline observation can be paired with all later baseline observations (n A *(n A -1)/2). 2 , 35 Then the baseline trend can be computed: TauU adf = P - N - S trend P + N + τ ; S trend = P A – NA

Online calculators might assist researchers in obtaining the TauU and TauU adjusted coefficients ( http://www.singlecaseresearch.org/calculators/tau-u ).

Percentage of nonoverlapping data (PND)

If anticipating an increase in the outcome, locate the highest data point in the baseline phase and then calculate the percent of the intervention phase data points that exceed it. If anticipating a decrease in the outcome, find the lowest data point in the baseline phase and then calculate the percent of the treatment phase data points that are below it: PND = n B Overlap A n b ∗ 100 . A PND < 50 would mark no observed effect, PND = 50–70 signifies a questionable effect, and PND > 70 suggests the intervention was effective. 40 The percentage of nonoverlapping (PNDC) corrected was proposed in 2009 as an extension of the PND. 39 Prior to applying the PND, a data correction procedure is applied eliminating pre-existing baseline trend. 38

Two Standard Deviation Band Method

When the stability criterion described above is met within phases, it is possible to apply the two standard deviation band method. 12 , 41 First, the mean of the data for a specific condition is calculated and represented with a solid line. In the next step, the standard deviation of the same data is computed and two dashed lines are represented: one located two standard deviations above the mean and the other – two standard deviations below. For normally distributed data, few points (less than 5%) are expected to be outside the two standard deviation bands if there is no change in the outcome score due to the intervention. However, this method is not considered a formal statistical procedure, as the data cannot typically be assumed to be normal, continuous, or independent. 41

Statistical Analysis

If the visual analysis indicates a functional relationship (i.e., three demonstrations of the effectiveness of the intervention effect), it is recommended to proceed with the quantitative analyses, reflecting the magnitude of the intervention effect. First, effect sizes are calculated for each participant (individual-level analysis). Moreover, if the research interest lies in the generalizability of the effect size across participants, effect sizes can be combined across cases to achieve an overall average effect size estimate (across-case effect size).

Note that quantitative analysis methods are still being developed in the domain of SC research 1 and statistical challenges of producing an acceptable measure of treatment effect remain. 14 , 42 , 43 Therefore, the WWC standards strongly recommend conducting sensitivity analysis and reporting multiple effect size estimators. If consistency across different effect size estimators is identified, there is stronger evidence for the effectiveness of the treatment. 1 , 18

Individual-level effect size analysis

The most common effect sizes recommended for SC analysis are: 1) standardized mean difference Cohen’s d ; 2) standardized mean difference with correction for small sample sizes Hedges’ g ; and 3) the regression-based approach which has the most potential and is strongly recommended by the WWC standards. 1 , 44 , 45 Cohen’s d can be calculated using following formula: d = X A ¯ - X B ¯ s p , with X A ¯ being the baseline mean, X B ¯ being the treatment mean, and s p indicating the pooled within-case standard deviation. Hedges’ g is an extension of Cohen’s d , recommended in the context of SC studies as it corrects for small sample sizes. The piecewise regression-based approach does not only reflect the immediate intervention effect, but also the intervention effect across time:

i stands for the measurement occasion ( i = 0, 1,… I ). The dependent variable is regressed on a time indicator, T , which is centered around the first observation of the intervention phase, D , a dummy variable for the intervention phase, and an interaction term of these variables. The equation shows that the expected score, Ŷ i , equals β 0 + β 1 T i in the baseline phase, and ( β 0 + β 2 ) + ( β 1 + β 3 ) T i in the intervention phase. β 0 , therefore, indicates the expected baseline level at the start of the intervention phase (when T = 0), whereas β 1 marks the linear time trend in the baseline scores. The coefficient β 2 can then be interpreted as an immediate effect of the intervention on the outcome, whereas β 3 signifies the effect of the intervention across time. The e i ’s are residuals assumed to be normally distributed around a mean of zero with a variance of σ e 2 . The assumption of independence of errors is usually not met in the context of SC studies because repeated measures are obtained within a person. As a consequence, it can be the case that the residuals are autocorrelated, meaning that errors closer in time are more related to each other compared to errors further away in time. 46 – 48 As a consequence, a lag-1 autocorrelation is appropriate (taking into account the correlation between two consecutive errors: e i and e i –1 ; for more details see Verbeke & Molenberghs, (2000). 49 In Equation 1 , ρ indicates the autocorrelation parameter. If ρ is positive, the errors closer in time are more similar; if ρ is negative, the errors closer in time are more different, and if ρ equals zero, there is no correlation between the errors.

Across-case effect sizes

Two-level modeling to estimate the intervention effects across cases can be used to evaluate across-case effect sizes. 44 , 45 , 50 Multilevel modeling is recommended by the WWC standards because it takes the hierarchical nature of SC studies into account: measurements are nested within cases and cases, in turn, are nested within studies. By conducting a multilevel analysis, important research questions can be addressed (which cannot be answered by single-level analysis of SC study data), such as: 1) What is the magnitude of the average treatment effect across cases? 2) What is the magnitude and direction of the case-specific intervention effect? 3) How much does the treatment effect vary within cases and across cases? 4) Does a case and/or study level predictor influence the treatment’s effect? The two-level model has been validated in previous research using extensive simulation studies. 45 , 46 , 51 The two-level model appears to have sufficient power (> .80) to detect large treatment effects in at least six participants with six measurements. 21

Furthermore, to estimate the across-case effect sizes, the HPS (Hedges, Pustejovsky, and Shadish) , or single-case educational design ( SCEdD)-specific mean difference, index can be calculated. 52 This is a standardized mean difference index specifically designed for SCEdD data, with the aim of making it comparable to Cohen’s d of group-comparison designs. The standard deviation takes into account both within-participant and between-participant variability, and is typically used to get an across-case estimator for a standardized change in level. The advantage of using the HPS across-case effect size estimator is that it is directly comparable with Cohen’s d for group comparison research, thus enabling the use of Cohen’s (1988) benchmarks. 53

Valuable recommendations on SC data analyses have recently been provided. 54 , 55 They suggest that a specific SC study data analytic technique can be chosen based on: (1) the study aims and the desired quantification (e.g., overall quantification, between-phase quantifications, randomization, etc.), (2) the data characteristics as assessed by visual inspection and the assumptions one is willing to make about the data, and (3) the knowledge and computational resources. 54 , 55 Table 1 lists recommended readings and some commonly used resources related to the design and analysis of single-case studies.

Recommend readings and resources related to the design and analysis of single-case studies.

Quality Appraisal Tools for Single-Case Design Research

Quality appraisal tools are important to guide researchers in designing strong experiments and conducting high-quality systematic reviews of the literature. Unfortunately, quality assessment tools for SC studies are relatively novel, ratings across tools demonstrate variability, and there is currently no “gold standard” tool. 56 Table 2 lists important SC study quality appraisal criteria compiled from the most common scales; when planning studies or reviewing the literature, we recommend readers consider these criteria. Table 3 lists some commonly used SC quality assessment and reporting tools and references to resources where the tools can be located.

Summary of important single-case study quality appraisal criteria.

Quality assessment and reporting tools related to single-case studies.

When an established tool is required for systematic review, we recommend use of the What Works Clearinghouse (WWC) Tool because it has well-defined criteria and is developed and supported by leading experts in the SC research field in association with the Institute of Education Sciences. 18 The WWC documentation provides clear standards and procedures to evaluate the quality of SC research; it assesses the internal validity of SC studies, classifying them as “Meeting Standards”, “Meeting Standards with Reservations”, or “Not Meeting Standards”. 1 , 18 Only studies classified in the first two categories are recommended for further visual analysis. Also, WWC evaluates the evidence of effect, classifying studies into “Strong Evidence of a Causal Relation”, “Moderate Evidence of a Causal Relation”, or “No Evidence of a Causal Relation”. Effect size should only be calculated for studies providing strong or moderate evidence of a causal relation.

The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 is another useful SC research tool developed recently to improve the quality of single-case designs. 57 SCRIBE consists of a 26-item checklist that researchers need to address while reporting the results of SC studies. This practical checklist allows for critical evaluation of SC studies during study planning, manuscript preparation, and review.

Single-case studies can be designed and analyzed in a rigorous manner that allows researchers strength in assessing causal relationships among interventions and outcomes, and in generalizing their results. 2 , 12 These studies can be strengthened via incorporating replication of findings across multiple study phases, participants, settings, or contexts, and by using randomization of conditions or phase lengths. 11 There are a variety of tools that can allow researchers to objectively analyze findings from SC studies. 56 While a variety of quality assessment tools exist for SC studies, they can be difficult to locate and utilize without experience, and different tools can provide variable results. The WWC quality assessment tool is recommended for those aiming to systematically review SC studies. 1 , 18

SC studies, like all types of study designs, have a variety of limitations. First, it can be challenging to collect at least five data points in a given study phase. This may be especially true when traveling for data collection is difficult for participants, or during the baseline phase when delaying intervention may not be safe or ethical. Power in SC studies is related to the number of data points gathered for each participant so it is important to avoid having a limited number of data points. 12 , 58 Second, SC studies are not always designed in a rigorous manner and, thus, may have poor internal validity. This limitation can be overcome by addressing key characteristics that strengthen SC designs ( Table 2 ). 1 , 14 , 18 Third, SC studies may have poor generalizability. This limitation can be overcome by including a greater number of participants, or units. Fourth, SC studies may require consultation from expert methodologists and statisticians to ensure proper study design and data analysis, especially to manage issues like autocorrelation and variability of data. 2 Fifth, while it is recommended to achieve a stable level and rate of performance throughout the baseline, human performance is quite variable and can make this requirement challenging. Finally, the most important validity threat to SC studies is maturation. This challenge must be considered during the design process in order to strengthen SC studies. 1 , 2 , 12 , 58

SC studies can be particularly useful for rehabilitation research. They allow researchers to closely track and report change at the level of the individual. They may require fewer resources and, thus, can allow for high-quality experimental research, even in clinical settings. Furthermore, they provide a tool for assessing causal relationships in populations and settings where large numbers of participants are not accessible. For all of these reasons, SC studies can serve as an effective method for assessing the impact of interventions.


This research was supported by the National Institute of Health, Eunice Kennedy Shriver National Institute of Child Health & Human Development (1R21HD076092-01A1, Lobo PI) and the Delaware Economic Development Office (Grant #109).

Some of the information in this manuscript was presented at the IV Step Meeting in Columbus, OH, June 2016.

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why use case study design

Case Study Research Design

The case study research design have evolved over the past few years as a useful tool for investigating trends and specific situations in many scientific disciplines.

This article is a part of the guide:

  • Research Designs
  • Quantitative and Qualitative Research
  • Literature Review
  • Quantitative Research Design
  • Descriptive Research

Browse Full Outline

  • 1 Research Designs
  • 2.1 Pilot Study
  • 2.2 Quantitative Research Design
  • 2.3 Qualitative Research Design
  • 2.4 Quantitative and Qualitative Research
  • 3.1 Case Study
  • 3.2 Naturalistic Observation
  • 3.3 Survey Research Design
  • 3.4 Observational Study
  • 4.1 Case-Control Study
  • 4.2 Cohort Study
  • 4.3 Longitudinal Study
  • 4.4 Cross Sectional Study
  • 4.5 Correlational Study
  • 5.1 Field Experiments
  • 5.2 Quasi-Experimental Design
  • 5.3 Identical Twins Study
  • 6.1 Experimental Design
  • 6.2 True Experimental Design
  • 6.3 Double Blind Experiment
  • 6.4 Factorial Design
  • 7.1 Literature Review
  • 7.2 Systematic Reviews
  • 7.3 Meta Analysis

The case study has been especially used in social science, psychology, anthropology and ecology.

This method of study is especially useful for trying to test theoretical models by using them in real world situations. For example, if an anthropologist were to live amongst a remote tribe, whilst their observations might produce no quantitative data, they are still useful to science.

why use case study design

What is a Case Study?

Basically, a case study is an in depth study of a particular situation rather than a sweeping statistical survey . It is a method used to narrow down a very broad field of research into one easily researchable topic.

Whilst it will not answer a question completely, it will give some indications and allow further elaboration and hypothesis creation on a subject.

The case study research design is also useful for testing whether scientific theories and models actually work in the real world. You may come out with a great computer model for describing how the ecosystem of a rock pool works but it is only by trying it out on a real life pool that you can see if it is a realistic simulation.

For psychologists, anthropologists and social scientists they have been regarded as a valid method of research for many years. Scientists are sometimes guilty of becoming bogged down in the general picture and it is sometimes important to understand specific cases and ensure a more holistic approach to research .

H.M.: An example of a study using the case study research design.

Case Study

The Argument for and Against the Case Study Research Design

Some argue that because a case study is such a narrow field that its results cannot be extrapolated to fit an entire question and that they show only one narrow example. On the other hand, it is argued that a case study provides more realistic responses than a purely statistical survey.

The truth probably lies between the two and it is probably best to try and synergize the two approaches. It is valid to conduct case studies but they should be tied in with more general statistical processes.

For example, a statistical survey might show how much time people spend talking on mobile phones, but it is case studies of a narrow group that will determine why this is so.

The other main thing to remember during case studies is their flexibility. Whilst a pure scientist is trying to prove or disprove a hypothesis , a case study might introduce new and unexpected results during its course, and lead to research taking new directions.

The argument between case study and statistical method also appears to be one of scale. Whilst many 'physical' scientists avoid case studies, for psychology, anthropology and ecology they are an essential tool. It is important to ensure that you realize that a case study cannot be generalized to fit a whole population or ecosystem.

Finally, one peripheral point is that, when informing others of your results, case studies make more interesting topics than purely statistical surveys, something that has been realized by teachers and magazine editors for many years. The general public has little interest in pages of statistical calculations but some well placed case studies can have a strong impact.

How to Design and Conduct a Case Study

The advantage of the case study research design is that you can focus on specific and interesting cases. This may be an attempt to test a theory with a typical case or it can be a specific topic that is of interest. Research should be thorough and note taking should be meticulous and systematic.

The first foundation of the case study is the subject and relevance. In a case study, you are deliberately trying to isolate a small study group, one individual case or one particular population.

For example, statistical analysis may have shown that birthrates in African countries are increasing. A case study on one or two specific countries becomes a powerful and focused tool for determining the social and economic pressures driving this.

In the design of a case study, it is important to plan and design how you are going to address the study and make sure that all collected data is relevant. Unlike a scientific report, there is no strict set of rules so the most important part is making sure that the study is focused and concise; otherwise you will end up having to wade through a lot of irrelevant information.

It is best if you make yourself a short list of 4 or 5 bullet points that you are going to try and address during the study. If you make sure that all research refers back to these then you will not be far wrong.

With a case study, even more than a questionnaire or survey , it is important to be passive in your research. You are much more of an observer than an experimenter and you must remember that, even in a multi-subject case, each case must be treated individually and then cross case conclusions can be drawn .

How to Analyze the Results

Analyzing results for a case study tends to be more opinion based than statistical methods. The usual idea is to try and collate your data into a manageable form and construct a narrative around it.

Use examples in your narrative whilst keeping things concise and interesting. It is useful to show some numerical data but remember that you are only trying to judge trends and not analyze every last piece of data. Constantly refer back to your bullet points so that you do not lose focus.

It is always a good idea to assume that a person reading your research may not possess a lot of knowledge of the subject so try to write accordingly.

In addition, unlike a scientific study which deals with facts, a case study is based on opinion and is very much designed to provoke reasoned debate. There really is no right or wrong answer in a case study.

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Martyn Shuttleworth (Apr 1, 2008). Case Study Research Design. Retrieved Nov 13, 2023 from Explorable.com: https://explorable.com/case-study-research-design

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When and How to Use a Case Study for Research

May 17, 2021 (Updated: May 4, 2023)

why use case study design

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What Is Case Study Research?

Types of case studies, when should you use a case study, case study benefits, case study limitations, how to write a case study.

Imagine your company receives a string of negative reviews online. You notice a few common themes among the complaints, but you still aren’t quite sure what went wrong. Or suppose an old blog post suddenly went viral, and you’d like to know why and how to do it again. In both of these situations, a case study could be the best way to find answers.

A case study is a process whereby researchers examine a specific subject in a thorough, detailed way. The subject of a case study could be an individual, a group, a community, a business, an organization, an event, or a phenomenon. Regardless of the type of subject, case studies are in-depth investigations designed to identify patterns and cause-and-effect relationships. Case studies are often used by researchers in the field of psychology , medicine, business, social work, anthropology, education, or political science.

Because they are singular in their focus and often rely on qualitative data, case studies tend to be highly subjective. The results of a single case study cannot always be generalized and applied to the larger population. However, case studies can be valuable tools for developing a thesis or illustrating a principle. They can help researchers understand, describe, compare, and evaluate different aspects of an issue or question.

why use case study design

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Case studies can be classified according to their purpose or their subject. For instance, a case study can focus on any of the following:

  • A person:  Some case studies focus on one particular person. Often, the subject will be an individual with some rare characteristic or experience.
  • A group:  Group case studies could look at a family, a group of coworkers, or a friend group. It could be people thrown together by circumstance or who share some bond or relationship. A group case study could even focus on an entire community of people.
  • An organization:  An organizational case study could focus on a business, a nonprofit, an institution, or any other formal entity. The study could look at the people in the organization, the processes they use, or an incident at the organization.
  • A location:  An event case study focuses on a specific area. It could be used to study environmental and population changes or to examine how people use the location.
  • An event:  Event case studies can be used to cover anything from a natural disaster to a political scandal. Often, these case studies are conducted retrospectively, as an investigation into a past event.

In addition to different types of subjects, case studies often have different designs or purposes. Here are a few of the most common types of case studies:

  • Explanatory:  An explanatory case study tries to explain the why or how behind something. This type of case study works well when studying an event or phenomenon, like an airplane crash or unexpected power outage.
  • Descriptive:  A descriptive, or illustrative, case study is designed to shed light on an unfamiliar subject. Case studies like this provide in-depth, real-world examples of whatever the researcher wants to help the audience understand. For instance, a descriptive case study could focus on the experience of a mother with postpartum depression or on a young adult who has aged out of the foster care system.
  • Exploratory:  An exploratory case study, or pilot case study, often serves as the first step in a larger research project. Researchers may use a case study to help them narrow their focus, draft a specific research question, and guide the parameters of a formal, large-scale study.
  • Intrinsic:  An intrinsic case study has no goal beyond a deeper understanding of its subject. In this type of study, researchers are not trying to make generalized conclusions, challenge existing assumptions, or make any compare-and-contrast connections. The most interesting thing about the study is the subject itself.
  • Critical Instance:  A critical instance case study is similar to an explanatory or intrinsic study. Like an intrinsic study, it may have no predetermined purpose beyond investigating the subject. Like an explanatory study, it may be used to explain a cause-and-effect relationship. A critical instance case study may also be used to call into question a commonly held assumption or popular theory.
  • Instrumental:  An instrumental case study is the opposite of an intrinsic study because it serves a purpose beyond understanding the immediate subject. In this type of study, researchers explore a larger question through an individual case or cases. For instance, researchers could use a handful of case studies to investigate the relationship between social media use and happiness.
  • Cumulative:  A cumulative, or collective, case study uses information from several past studies as the basis for a new study. Because it takes into account multiple case studies, a cumulative study allows for greater generalization than a single case study. It can also be a more time- and cost-effective option since it makes use of existing research.

Case studies are often used in the exploratory phase of research to gather qualitative data. They can also be used to create, support, or refute a hypothesis and guide future research. For instance, a marketing professional might conduct a case study to discover why a viral ad campaign was so successful . They can then take any lessons they glean from the case study and apply them to future marketing efforts. A psychologist could use a case study to form a theory about the best way to treat a specific disorder. That theory could then be tested later through a large-scale controlled study.

Case studies are a good way to explore a real-world topic in-depth, illustrate a point, discuss the implications or meaning of an event, or compare the experiences of different individuals. A trainer may use a case study to bring to life what would otherwise be an abstract series of recommended action steps or to spark a conversation about how to respond in a specific scenario. Similarly, professors can use case studies to highlight key concepts from a lecture and pose questions to test students’ understanding of the material.

In some situations, case studies are the only way to compile quantitative data in an ethical manner. For instance, many of the recommendations that doctors make regarding what is or is not safe during pregnancy are based on case studies. It wouldn’t be ethical to conduct a controlled study that exposes pregnant women to potentially harmful substances, so doctors rely on the anecdotal evidence provided by case studies to find correlations and draw their conclusions.

Case studies can also be used to gather data that would be otherwise impossible or impractical to obtain. Students often use case studies for their thesis or dissertation when they lack the time or resources to conduct large-scale research. Zoologists might use existing case studies to determine the success rate of reintroducing rehabilitated animals into the wild. A historian could use case studies to explore the strategies used by dictators to gain and maintain power.

why use case study design

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Case studies can be used on their own or as a complement to other research methods, depending on the situation. The examples above are just a few instances where case studies can be useful. Case studies also work well for the following:

Providing Insight Through Qualitative Data

Case studies generally provide more qualitative data as opposed to quantitative data , and that makes them an invaluable tool for gathering insight into complex topics. Psychologists, for instance, use case studies to better understand human behavior. Crafting theories on the motives behind human actions would be difficult with quantitative data alone. The information gleaned through case studies may be subjective, but so is much of what makes us human. As individuals, we each have a unique blend of emotions, attitudes, opinions, motivations, and behaviors. Objective quantitative data is rarely the best way to identify and explain these nuances.

By their very nature, case studies allow more more intensive, in-depth study than other research methods. Rather than aiming for a large sample size, case studies follow a single subject. Often case studies are conducted over a longer period of time, and the narrow focus allows researchers to gather more detail than would be possible in a study of thousands of people. The information gleaned may not be representative of the broader population, but it does provide richer insight into the subject than other research methods.

Identifying Avenues for Future Research

Case studies are often used as the first step in a larger research project. The results of a case study cannot necessarily be generalized, but they can help researchers narrow their focus. For instance, researchers in the medical field might conduct a case study on a patient who survived an injury that typically proves fatal.

Over the course of the study, researchers may identify two or three ways in which this patient’s situation differed from others they have seen. Perhaps they identify something unique in the patient’s DNA or lifestyle choices or in the steps doctors took to treat the injury. Letting that information guide them, researchers could use other methods to deepen their understanding of those factors and perhaps develop new treatments or preventative recommendations.

Case studies can also be used in the fields of social work, politics, and anthropology to draw attention to a widespread problem and spur more research. A detailed narrative about one person’s experience will inspire more compassion than an academic paper filled with quantitative data. Stories often have a greater impact than statistics.

Challenging, Testing, or Developing Theories

Case studies can be particularly useful in the process of forming and testing theories. A case study may lead researchers to form a new theory or call into a question an existing one. They are an invaluable tool for identifying exceptions to a rule or disproving conventional wisdom.

For instance, a medical professional may write a case study about a patient who exhibited atypical symptoms to assert that the list of symptoms for a condition should be expanded. A psychologist could use a case study to determine whether the new treatment they devised for depression is effective, or to demonstrate that existing treatment methods are flawed. As the result of a case study, a marketing professional could suggest that consumers values have changed and that marketing best practices should be updated accordingly.

Enabling the Study of Unique Subjects

Some subjects would be impossible, impractical, or unethical to study through other research methods. This is true in the case of extremely rare phenomenon, many aspects of human behavior, and even some medical conditions.

Suppose a medical professional would like to gather more information about multiple-birth pregnancies with four or more fetuses. More information would be helpful because we have less information about them, but the reason we have less information is because they are so rare. Conducting case studies of a few women who are currently pregnant with multiples or have given birth to multiples in the past may be the only practical way to research them.

Case studies can also be used to gain insight into historical events and natural phenomenon — things we are not able to repeat at will. Case studies have also been used to study subjects such as a feral child , child prodigies, rare psychological conditions, crisis response, and more.

Helping People Better Understand Nuanced Concepts

Educators incorporate case studies into their lectures for a reason. Walking students through a detailed case study can make the abstract seem more real and draw out the nuances of a concept. Case studies can facilitate engaging discussions, spark thoughtful questions, and give students a chance to apply what they have learned to real-world situations.

Outside the classroom, case studies can be used to illustrate complex ideas. For instance, a well-constructed case study can highlight the unintended consequences of a new piece of legislation or demonstrate that depression does not always manifest in an obvious way. Case studies can help readers and listeners understand and care about an issue that does not directly affect them.

Despite their benefits, case studies do come with a few limitations. Compared to other research methods, case studies are often at a disadvantage in terms of the following:


In most cases, scientists strive to create experiments that can be repeated by others. That way, other scientists can perform their own research and compare their results to those of the initial study. Assuming these other scientists achieve similar results, the replicability of the experiment lends credibility to the findings and theories of the original researchers.

One limitation of case studies is that they are often difficult, if not impossible, to replicate. Although this fact does not diminish the value of case studies, it does demonstrate that case studies are not a good fit for every research problem — at least, not on their own. Additional research would have to be performed to corroborate the results and prove or disprove any generalized theories generated by a case study.


Generalization is another area in which case studies cannot match other research methods. A case study can help us challenge existing theories and form new ones, but its results cannot necessarily be generalized. The data we gather from a case study is only valid for that specific subject, and we cannot assume that our conclusions apply to the broader population.

Researchers or readers can attempt to apply the principles from a particular case to similar situations or incorporate the results into a more comprehensive theory. However, a case study by itself can only prove the existence of certain possibilities and exceptions, not a general rule.


The reliability of case studies may be called into question for two reasons. The first objection centers on the fallibility of human memory and the question of whether subjects are being honest. Many case studies rely on subjects to self-report biographical details, their state of mind, their thoughts and feelings, or their behaviors.

The second issue is the Hawthorne effect, which refers to the tendency of individuals to modify their behavior when they know they are being observed. This effect makes it nearly impossible for researchers to ensure that the observations and conclusions of their case study are reliable.

Researcher Bias

Researcher bias is another potential issue with case studies. The results of a case study are by nature subjective and qualitative rather than objective and qualitative, and any findings rely heavily on the observations and narrative provided by the researcher. Even the best researchers are still human, and no matter how hard they try to remain objective, they will not be able to keep their findings completely free of bias.

Researchers may have biases they are not even aware of. A researcher may over-identify with the subject and lose the benefit of a dispassionate outside perspective. If the researcher already has an opinion on the subject, they may subconsciously overlook or discount facts that contradict their pre-existing assumptions. Researcher bias can affect what the researcher observes and records, as well as how they interpret and apply their observations.

Case studies can be time-consuming and expensive to conduct. Crafting a thorough case study can be a lengthy project due to the intensive, detailed nature of this type of research. Plus, once the information has been gathered, it must be interpreted. Between the observation and analysis, a case study could take months or even years to complete. Researchers will need to be heavily involved in every step of the process, putting in a lot of time, energy, focus, and effort to ensure that the case study is as informative as possible.

Now that you understand the benefits, limitations, and types of case studies, you can follow these steps to write your own:

  • Determine your objective.  Write out your research problem, question, or goal. If you aren’t sure, ask yourself questions like, “What am I trying to accomplish? What do I need to know? What will success look like?” Be clear and specific. Your answers will help you choose the right type of case study for your needs.
  • Review the research.  Before delving into your case study, take some time to review the research that is already available. The information you gather during this preliminary research can help guide your efforts.
  • Choose a subject.  Decide what or who the subject of your case study will be. For instance, if you are conducting a case study to find out how businesses have been affected by new CDC guidelines, you will need to choose a specific restaurant or retailer. In some cases, you may need to draft a release form for the subject to sign so that you will be able to publish your study.
  • Gather information.  Case studies about a person, organization, or group may rely on questionnaires or interviews to gather information. If you are studying an event, you might use a combination of academic research and witness interviews. In some cases, you will record your own observations as part of the study.
  • Write a report.  Most case studies culminate in a written report, similar to a research paper. Most case studies include five sections : an introduction, a literature review, an explanation of your methods, a discussion of your findings and the implications, followed by a conclusion.
  • Publish your findings.  Once you’ve written your case study, consider the most engaging way to present your findings. A well-written research article is a good place to start, but going a step further will maximize the impact of your research. For instance, you could design an infographic to highlight key findings or commission an animated video to turn your case study into a visual narrative.

Whether research is your primary occupation or only an incidental part of your job, you can benefit from a solid understanding of what case studies are, how they work, and when to use them. Use the information and steps above to design and write a case study that will provide the answers you’re looking for.

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What is design thinking?

Discover what is design thinking and why it’s important, including the five stages of design thinking. Deep dive into a few case studies and learn how to apply design thinking.

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Design thinking is a mindset that breeds innovation. While it’s based on the design process, anyone in any profession can use it when they’re trying to come up with creative solutions to a problem. 

In this guide, we’ll walk you through what design thinking is and why it’s important, including the five stages of design thinking. Then we’ll present a couple of design thinking case studies and wrap up with a primer on how to apply design thinking. And don’t worry, this guide is broken down into easily digestible chunks, as follows:

Let’s get started!

What is design thinking? A definition

Design thinking is an approach used for problem-solving. Both practical and creative, it’s anchored by human-centred design.

Design thinking is extremely user-centric in that it focuses on your users before it focuses on things like technology or business metrics. 

Design thinking is also solution-based, looking for effective solutions to problems, not problem-based, which looks at the problem itself and tends to focus on limitations. 

Design thinking is all about getting hands-on with solutions. The aim is to quickly turn your ideas into testable products so you can see what works and what doesn’t.


Why is design thinking important? 

Design thinking is important because it challenges assumptions and fosters innovation. While many ways of thinking rely on the habits and experiences we’ve formed, they can limit us when it comes to thinking of design solutions. Design thinking, however, encourages us to explore new ideas. 

It’s an actionable technique that allows us to tackle “wicked problems,” or problems that are ill-defined. For example, achieving sustainable growth or maintaining your competitive edge in business count as wicked problems, and on a broader scale, poverty and climate change are wicked problems too. Design thinking uses empathy and human-centred thinking to tackle these kinds of problems.

Who uses design thinking?

The short answer? Everyone! Design thinking can help you in whatever your role or industry. People in business, government, entertainment, health care, and every other industry can benefit from using design thinking to come up with innovative solutions. 

The most important thing design thinking does is help people focus on their customers or end users. Instead of focusing on problems to fix, design thinking keeps things user-centric, which boosts customer engagement. 

What are the 5 stages of design thinking?

According to the Hasso Plattner Institute of Design at Stanford University (known as d-school), the five stages of design thinking are: 

Although these stages appear to be linear, following one after the other, design thinking isn’t a linear process. Stages are often run in parallel or out of order, or repeated when necessary.

Phase 1: Empathise 

Your goal here is to research your users’ needs to gain an empathic understanding of the problems they face. You’ll get to know your users and their wants and needs so you can make sure your solutions put them front and centre. This means setting aside your own assumptions and getting to know your users on a psychological and emotional level. You’ll observe, engage, watch and listen. 

Phase 2: Define

Here you state your users’ needs by compiling the information you gathered during the Empathise phase and then analysing it until you can define the core problem your team has identified. 

You do this by asking questions like: what patterns do you see in the data? What user issues need to be resolved? The conclusion of this phase comes when you’ve figured out a clear problem statement that is defined by the users’ needs. For example, “Bank customers in Glasgow need…”

You can learn more about how to write a problem statement in this guide.

Phase 3: Ideate

In this phase, you’ll generate ideas and solutions. You and your team will hold ideation sessions where you can come up with as many ideas as possible. No idea is too silly for this stage. The important thing is getting all ideas out on the table. There are a variety of techniques you can use, like brainstorming and mind mapping, to come up with solutions. This phase ends when you’ve managed to narrow down your ideas to just a few of the best ones.

Phase 4: Prototype

Your goal in this phase is to find the best solution to the problem by prototyping —that is, producing scaled down versions of the product or its features found in the previous phase. You’ll put each solution to the test by improving, redesigning, accepting, or rejecting it.

Phase 5: Test

Here you’ll try out the solutions you arrived at in the previous phases by user testing them. However, while this is the final stage of design thinking in theory, it’s rarely the final stage in reality. Design thinking often includes going back to previous phases to find other solutions or to further iterate or refine your existing solution.


Design thinking examples and case studies

Now that you understand the theory and process of design thinking, let’s look at some examples in action where design thinking had a real-world impact.

Case Study 1: American Family Insurance’s Moonrise App

American Family Insurance, a company that offers life, business, auto, and home insurance, came to design company IDEO with the goal of innovating in a way that would help working families. 

Stages 1 & 2: Empathise and Define

While American Family thought their customers might benefit from budgeting tools, IDEO found from their research in the Empathise phase that, actually, people needed a way to build up their savings against unforeseen needs.

They noticed a lot of people had meticulously planned budgets, which made budgeting tools a moot point. But they were living just within their means and an extra expense, like a doctor’s visit or kid’s basketball uniform, could throw their budget off. These people didn’t want to take on debt though, they wanted extra work so they could have a cushion.

Stages 3 & 4: Ideate and Prototype

IDEO took that idea and ran with it, creating Moonrise, an app that matches people looking for work with extra hours and income. Today’s businesses depend on on-demand work but the temp agencies they work with tend to want permanent placements. Moonrise does things differently. It enables companies to find people who are already employed elsewhere for short-term work through a simple text message interface. The employers can list shifts on the platform and workers are paid as soon as they finish their shifts.

Stage 5: Test

To test the app, 11 Moonrisers, six employers, and a team of designers and programmers were assembled for a one week period to work out the kinks in the platform. 

Based on the test’s success, American Family Insurance now owns the startup Moonrise, which launched in Chicago in 2018 and has since expanded to additional states. In 2018, over 7,000 shifts have been fulfilled and over $500,000 has been earned by people on the app.

Case Study 2: GE Healthcare’s Scanning Tools

GE Healthcare has cutting-edge diagnostic imaging tools at its disposal, but for kids they’re an unpleasant experience. 

“The room itself is kind of dark and has those flickering fluorescent lights…. That machine that I had designed basically looked like a brick with a hole in it,” explained Doug Dietz , a designer who worked for GE. How could they make the experience better for kids?

The team at GE began by observing and gaining empathy for children at a daycare centre and talking to specialists who knew what paediatric patients went through. The team then recruited experts from a children’s museum and doctors from two hospitals. This gave them a lot of insight into what children went through when they had to sit for these procedures and what could be done to lessen the children’s stress.

Stages 3, 4 & 5: Ideate, Prototype, and Test

The first prototype of the new and improved “Adventure Series” scanner was invented. Through research and pilot programs, the redesign made imaging machines more child-friendly, making sure they have other things to focus on than the scary looks and sounds of the machine. For example, the Coral City Adventure in the emergency room gives children an underwater experience where they get into a yellow submarine and listen to the sound of harps while their procedure takes place.

Patient satisfaction scores increased to 90% and children no longer suffer such anxiety about their scans. The children hold still for their procedures more easily, making repeats of the scans unnecessary. There’s also less need for anesthesiologists, which improved the bottom line for those hospitals that used the scanning machines because more patients could get scanned each day.

How to apply design thinking 

If you want to apply design thinking in your own work, follow these steps and best practices:

  • Improve design thinking skills. Use training to explain, improve, and practically implement the phases of design thinking. You can do this in several ways such as workshops, online courses, or case studies shared with your team.
  • Identify the correct problem. Listen to users and ask them unbiased questions in order to understand their perspectives. Engage with everyone and stay open-minded, so you can identify the correct problem, not the problem you or your organisation thinks users are having. 
  • Have more debriefs. Be open about what went right and what went wrong in your process. Openly discuss why things succeeded or failed and why. View failure as learning, not as an excuse to give up.
  • Iterate and iterate some more. The goal of design thinking is finding the best answer possible—and that probably won’t come in the first round of iteration. You’ll need to test and iterate as much as possible with new ways to solve the problem.

Design thinking is so popular—and so effective—because it places the user’s needs front and centre. For more user-centric design tips, learn how to incorporate user feedback in product design , get to grips with user research ethics , and learn how to conduct effective user interviews .

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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.

Peer Review reports


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 ].


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

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Sarah Crowe & Anthony Avery

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

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

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