Case studies are a popular research method in business area. Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization.
According to its design, case studies in business research can be divided into three categories: explanatory, descriptive and exploratory.
Explanatory case studies aim to answer ‘how’ or ’why’ questions with little control on behalf of researcher over occurrence of events. This type of case studies focus on phenomena within the contexts of real-life situations. Example: “An investigation into the reasons of the global financial and economic crisis of 2008 – 2010.”
Descriptive case studies aim to analyze the sequence of interpersonal events after a certain amount of time has passed. Studies in business research belonging to this category usually describe culture or sub-culture, and they attempt to discover the key phenomena. Example: “Impact of increasing levels of multiculturalism on marketing practices: A case study of McDonald’s Indonesia.”
Exploratory case studies aim to find answers to the questions of ‘what’ or ‘who’. Exploratory case study data collection method is often accompanied by additional data collection method(s) such as interviews, questionnaires, experiments etc. Example: “A study into differences of leadership practices between private and public sector organizations in Atlanta, USA.”
Advantages of case study method include data collection and analysis within the context of phenomenon, integration of qualitative and quantitative data in data analysis, and the ability to capture complexities of real-life situations so that the phenomenon can be studied in greater levels of depth. Case studies do have certain disadvantages that may include lack of rigor, challenges associated with data analysis and very little basis for generalizations of findings and conclusions.
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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