A very extensive summary of Robert K. Yin’s famous book "Case Study Research: design and methods." 4-th edition, 2009. Advise: Read the book first before this summary.
(Een zeer uitgebreide samenvatting van Robert K. Yin's boek "Research: design and methods." 4-th edition, 2009)
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Very extensive summary Case Study Research, Yin
Yin distinguishes the following activities when doing a case study research:
3. Prepare (and share your preparation)
4. Collect (sometimes going back to Design when collecting data)
Chapter 1: How to Know Whether and When to Use Case Studies as a Research Method
Your goal is to design good case studies and to collect, present and analyse data fairly. A further goal is tob ring the case study to closure by writing a compelling report or book. Important is to follow a rigorous methodological path. Equally important is a dedication to formal and explicit procedures when doing your research. Also be aware of tha fact that different social science research methods fill different needs and situations for investigating social topics.
A case study is relevant the more your research questions seek to explain some present circumstances: how and why some social phenomenon works or if your research questions require an “in-depth” sedcription of some social phenomenon. The focus is non understanding these social phenomenons.
A common misinterpretation is that the various research methods should be arrayed hierarchically. Many social scientist still believe that case studies are only appropriate for the descriptive phase, that surveys and histories are appropriate for the descriptive phase, and that experiments are the only way for doing explanatory or causal inquiries. So case studies are only a preliminary research method and can not be used to describe or test propositions.
This hierarchical view, however, may be questioned. Some of the best and most famous case studies have been explanatory case studies (f.i. Street Corner Society by Williman F. Whyte).
When to use each method?
|Method||Form of Research Question||Requires Control of Behaviour Events?||Focusses on Contemporary Events?|
|Survey||Who, what, where, how many, how much?||no||Yes|
|Archival Analysis||who, what, where, how many, how much||no||Yes/no|
|Case Study||How, why?||no||Yes|
If research focusses on what questions, either of two positions arises.
- Explanatory for example what can be learned from a study from a start of startup business?
- What as a form of ‘how many?’. What have been the way’s……
Who and where (or how much or how many) questions are more likely to favor survey methods or the analysis of archival data, as in economic studies. They are advantageous when the research goal is to describe the prevalence of a certain phenomenon or to be predictive of a certain outcome.
In contrast ‘how’ and ‘why’ questions are more explanatory and likely to lead us to the use of case studies, histories and experiments as the preferred research methods.
The key is to understand that your research questions have both substance – for example what is my study about and form for example am I asking a who, what, where, why or how question.
Assuming that the ‘how’ and ‘why’ questions are to be the focus of the study, a further distinction among history, case study and experiment is the extent of the investigator’s control over and access to actual behavioral events.
Histories are preferred when there is virtually no access or control, and can of course be done about contemporary events: in this situation the method begins to overlap with that of the case study.
Experiments are done when an investigator can manipulate behavior directly, precisely and systematically.
The case study is preferred in examining contemporary events, but when the relevant behaviors can not be manipulated.
So in general the case study has a general advantage when a ‘how’ or ‘why’ question is being asked about a contemporary set of events over which the investigator has little or no control.
Perhaps the greatest concern has been the lack of rigor of case study research. To many times,the case study researcher has been sloppy, has not followed systematically procedures, or has allowed equivocal evidence or biased views to influence the directions of the findings of the conclusions.
A second concern is that they provide little basis for scientific generalization. The short answer is that case studies, like experiments, are generalizable to theoretical propositions and not to populations or universes.
A third concern is that case studies take to long. This incorrectly confuses the case study method with a specific method of data collection, such as ethnography or participant observation.
Case studies are a form of inquiry that does not depend solely on ethnographic or participant observer data. You could even do a high level case study without leaving the telephone or the internet.
A fourth possible objection to case studies has seemingly emerged with the renewal emphasis on randomized field trials or ‘true experiments’, to establish causal relations. Overlooked has been the possibility that case studies can offer important evidence to complement experiments.
Different kind of case studies but a common definition
The essence of a case study, the central tendency among all types of case study, is that it tries to illuminate a decision or set of decisions: why they were taken, how they were implemented, and with what result (Schramm, 1971, emphasis added)
This definition thus cites cases of “decisions” as the major focus of case studies. Other common cases include “individuals,” “organisations,” “processes,” “programs,” “neighborhoods,” “institutions,” and even “events.”
A case study is an empirical inquiry that:
• Investigates a contemporary phenomenon in depth and within its real-life context, especially when
• The boundaries between phenomenon and context are not clearly evident.
In other words you use the case study method because you want to understand a real-life phenomenon in depth, but such understanding encompasses important contextual conditions – because they were highly pertinent to your phenomenon of study (e.g. Yin & Davis, 2007)
However a definition of case studies as a research method is necessary.
Because phenomenon and context are not always distinguishable in real life situations, other technical characteristics, including data collection and data analysis strategies, become the second part of our technical definition of case studies:
The case study inquiry:
• copes with the technical distinctive situation in which there will be many more variables of interest than data points (f.i. compared with experiments), and as one result
• Relies on multiple sources of evidence, with data needing to converge in a triangular fashion, and as another result
• Benefits from the prior development of theoretical propositions to guide data collection and data analysis.
Case studies include both single and multiple-case studies.
Some case study research goes beyond being a type of qualitative research, by using a mix of quantitative and qualitative evidence.
Case studies have a distinctive place in evaluation research.
• The most important is to explain the presumed causal links in real-life events that are too complex for the survey or experimental strategies
• A second application is to describe an intervention and the real-life context in which it occurred.
• Third, case studies can illustrate certain topics within an evaluation, again in a descriptive mode
• Fourth, the case study strategy may be used to enlighten those situations in which the intervention being evaluated has no clear single set of outcomes.
Also case studies can be conducted and written with many different motives. These motives vary from the simple presentation of individual cases to desire to arrive at broad generalizations based on case study evidence but without presenting any of the case studies separately.
Chapter 2: Designing Case Studies
The next task is to design your case study. For this purpose you need a plan or research design.
The case study is a separate research method that has its own research design.
A research design is a logical plan for getting from here to there, where here may be defined as the initial set of questions to be answered and there is some set of conclusions (answers) about these questions.
Between “here” and “there” may be found a number of major steps, including the collection and analysis of relevant data.
A research plan guides the investigator in the process of collecting, analyzing and interpreting observations. It is a logical proof that allows the researcher to draw inferences concerning causal relations among the variables under investigation (Nachmias & Nachmias, 1992)
Another way of thinking about a research design is a “blueprint” for your research dealing with at least four problems:
• What questions to study
• What data are relevant
• What data to collect
• How to analyse the results
Components of research design
For case studies five components of a research design are especially important:
1. a study’s question.
2. its propositions, if any.
Only if you are forced to state some propostions will you move in the right direction. For instance, you might think that organisations collaborate because they derive mutual benefits. This proposition begins to tell you where to look for relevant evidence.
At the same time some studies have a legitimate reason for not having any propositions. This is the condition-which exists in experiments, surveys and the other research methods alike – which a topic is the subject of exploration.
3. Its unit(s) of analysis.
This is the defining of what the “case” is. Keep also in mind that each unit of analysis and its related questions and propositions would call for a slightly different research design and data collection strategy.
There is often also a need for spatial, temporal, and other concrete boundaries. The desired case should be a real life phenomenon, not an abstraction. If you want to compare your findings with previous research, the key definitions in your study should not be idiosyncratic.
4. The logic linking the data to the propositions.
How will you link the data to the propositions? Techniques are for instance pattern matching, explanation building, time-series analysis, logic models, and cross-case synthesis.
5. The criteria for interpreting the findings.
A major and important alternative strategy is to identify and address rival; explanations for your findings. If you only think of rival explanations after data collection has been completed, you will be starting to justify and design a future study, but you will not be helping to complete your current case study. For this reason, specifying important rival explanations is a part of a case study’s research design work.
The Role of Theory in Design Work
Covering these preceding five components of research design will effectively force you to begin constructive a preliminary theory related to your topic of study. Be aware of the differences with methods such as ethnography and grounded theory. These related methods deliberately avoid specifying any theoretical propositions at the outset of an inquiry. As a result, students confusing these methods with case studies wrongly think that, by having selected the case study method, they can proceed quickly into the data collection phase of their work, and they may have been encouraged to make their “field contacts” as possible. No guidance could be more misleading. Among other considerations, the relevant field contacts depend upon an understanding – or theory – of what is being studied.
Having a research question or questions theory development is an essential part of the design phase.
The simplest ingredient of a theory is a statement such as follows:
“The case study will show why implementation of Management Information System X only succeeds when the organization was able to re-structure itself, and not just overlay the new MIS on the old organization structure”.
An additional ingredient could be:
“The case study will also show why the simple replacement of key persons was not sufficient for successful implementation”
Keep in mind that this second statement presents the nutshell of a ‘rival theory’.
The stated ideas / ingredient will increasingly cover the questions, propositions, units of analysis, logic connecting data to propositions , and criteria for interpreting the findings.
The simple goal is to have a sufficient blueprint for your study, and this requires theoretical propositions, usefully noted by Sutton and Staw (1995) as “a (hypothetical) story about why acts, events, structure and thoughts occur.”
Illustrative types of theories
* implementation theories;
* individual theories (individual development, cognitive behavior etc.);
* group theories (family functioning, informal groups etc.)
* organizational theories (theories of bureaucracies, organizational structure and functioning etc.);
* societal theories (theories of urban development, cultural institutions etc.)
Other theories cut across these illustrative types. Decision-making theoryfor instance can involve individuals, organizations and social groups
Generalizing from case study to theory
Theory development does not only facilitate the collection phase of the ensuing case study. The appropriate developed theory also is the level at which the generalization of the case study results will occur.
The role of theory has been characterized throughout this book as “analytical generalization” and has been contrasted with another way of generalizing results, known as “statistical generalization”.
In statistical generalization, an inference is made about a population (or universe) is made on the basis of empirical data collected about a sample from that universe.
A fatal flaw in doing case studies is to conceive of statistical generalization as the method of generalizing the results of your case study. This is because your cases are not “sampling units” and should not be chosen for this reason.
Analytical generalization can be used whether your case study involves one or several cases, which shall be later referenced as single or multiple case studies. You should try to aim towards analytical generalization in doing case studies and you should avoid thinking in such confusing terms as “the sample of cases” or “the small sample size of cases,” as if a single – case study were like a single respondent in a survey or a single subject in an experiment. The replication logic, whether applied to experiments or to case studies, must also be distinguished from the sampling logic commonly used in surveys.
The reasons are:
1. Case studies are not the best method for assessing the prevalence of phenomena
2. A case study would have to cover both the phenomenon of interest and its context, yielding a large number of potentially relevant variables. This would require an impossible large number of cases – too large to allow any statistical consideration of the relevant variables.
3. If a sampling logic had to be applied to all types of research, many important problems could not ne empirically investigated.
The methodological differences between these two views are revealed by the different rationales underlying the replication as opposed to sampling design
Replication logic not sampling logic
Multiple cases resemble multiple experiments. So you need replication logic, not sampling logic, for multiple-case studies. That means that each case must be carefully selected so that it (a) predict similar (a literal replication) or (b) predicts contrasting results but for anticipatable reasons (a theoretical replication). The ability to conduct 6 or 10 case studies, arranged effectively within a multiple-case design, is analogous to the ability to conduct 6 to 10 experiments on related topics. A few cases (2 or 3) would be literal replications, whereas a few other cases (4 to 6) might be design to pursue two different patterns of theoretical replications.
For more information about the book: Yin, R.K (2009) Case Study Research: Design and Methods. London: Sage
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