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. 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. 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.
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. 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). 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), 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.
Example of epistemological approaches that may be used in case study research
How are case studies conducted?
Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.
Defining the case
Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[8,12]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7). 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.
Example of a checklist for rating a case study proposal
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. 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. 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. 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. 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. 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). 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  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). 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. 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).
Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme. 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. 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). 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).
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. 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). 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).
Potential pitfalls and mitigating actions when undertaking case study research
Stake's checklist for assessing the quality of a case study report
The theoretical framework is one of the more infamous components of a dissertation. A good theoretical framework gives you a strong scientific research base and provides support for the rest of your dissertation. But what exactly is a theoretical framework? And how do you write one?
The goal of a theoretical framework
After you have identified your problem statement and research question(s), it is important to determine what theories and ideas exist in relation to your chosen subject.
By presenting this information, you ‘frame’ your research and show that you are knowledgeable about the key concepts, theories, and models that relate to your topic.
The definitions and models you select also give your research direction, as you will continue to build on these choices in different stages of your project.
The theoretical framework also provides scientific justification for your investigation: it shows that your research is not just coming “out of the blue,” but that it is both grounded in and based on scientific theory.
How to determine the contents of a theoretical framework
As noted above, it is important that you cite existing theories and ideas that are relevant to your chosen topic within the theoretical framework. This includes defining key terms from your problem statement and research questions/hypotheses. An important first step is therefore to identify these concepts.
1. Select key concepts
Sample problem statement and research questions: Company X is struggling with the problem that many online customers do not return to make subsequent purchases. Management wants to increase customer loyalty and believes that improved customer satisfaction will play a major role in achieving this goal. To investigate this problem, you have identified and plan to focus on the following problem statement, objective, and research questions:
Problem: Many online customers do not return to make subsequent purchases.
Objective: To increase customer loyalty and thereby generate more revenue.
Research question: ‘How can the satisfaction of company X’s online customers be improved in order to increase customer loyalty?’
- ‘What is the relationship between customer loyalty and costumer satisfaction?’
- ‘How satisfied and loyal are company X’s online costumers currently?’
- ‘What factors affect the satisfaction and loyalty of company X’s online costumers?’
The concepts of “customer loyalty” and “customer satisfaction” are critical to this study and will be measured as part of the research. As such they are key concepts to define within the theoretical framework.
2. Define and evaluate relevant concepts, theories, and models
A literature review is first used to determine how other researchers have defined these key concepts. You should then critically compare the definitions that different authors have proposed. The last step is to choose the definition that best fits your research and justify why this is the case.
It is also important to indicate if there are any notable links between these concepts.
Apart from that, you should describe any relevant theories and models that relate to your key concepts and argue why you are or are not applying them to your own research.
3. Consider adding other elements to your theoretical framework
Depending on your topic or discipline, a comprehensive review of the state of affairs in relation to your research topic may also be helpful to include in your theoretical framework.
Here it’s important to understand the expectations of your supervisor or program in advance. Theoretical problems are more likely to require a “state of affairs” overview than more practical problems.
Analyzing the research field will give you an idea of what similar studies have looked at and found regarding the problem. This will clarify the position of your research in relation to existing knowledge in the field.
Following these steps will help to ensure that you are presenting a solid overview:
- Describe what discussions on the subject exist within the literature.
- Explain what methods, theories, and models other authors have applied. In doing so, always argue why a particular theory or model is or is not appropriate for your own research.
- Analyze the similarities and differences between your own research and earlier studies.
- Explain how your study adds to knowledge that already exists on the subject.
What kinds of research questions can you answer within a theoretical framework?
The theoretical framework can be used to answer descriptive research questions that only require literature (or desk) research. For example, theory alone is sufficient to answer the research question: ‘What is the relationship between customer loyalty and customer satisfaction?’.
In contrast, sub-questions such as ‘How satisfied are company X’s online customers currently?’ cannot be answered in the theoretical framework, given that field research is needed.
The theoretical framework (and the literature review that serves as its backbone) can also be used to further analyze existing findings and hypotheses. It may also be used to formulate and evaluate hypotheses of your own, which you can later test during the qualitative or quantitative research of your study.
The structure of the theoretical framework
There are no fixed rules for structuring a theoretical framework. The important thing is to create a structure that is logical. One way to do this is to draw on your research questions/hypotheses and some of your key terms.
For example, you could create a section or paragraph that looks at each question, hypothesis, or key concept. Within that text, you could then explore the theories and models that are relevant to that particular item.
How long should the theoretical framework be?
The rules about length are not clear either, but a theoretical framework is on average three to five pages long.
To makes things clearer, you might find it useful to include models or other graphics within the theoretical framework. However, if you are concerned about space, you can place these illustrations in an appendix (which you can then refer to in the main text).
Sample theoretical framework
We have prepared a sample theoretical framework that will give you a sense of what this important part of a dissertation may look like.
Sample theoretical framework