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
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The idea of using pattern matching as a rubric for assessing construct validity is an area where I have tried to make a contribution (Trochim, W., (1985). Pattern matching, validity, and conceptualization in program evaluation. Evaluation Review, 9, 5, 575-604 and Trochim, W. (1989). Outcome pattern matching and program theory. Evaluation and Program Planning, 12, 355-366.), although my work was very clearly foreshadowed, especially in much of Donald T. Campbell's writings. Here, I'll try to explain what I mean by pattern matching with respect to construct validity.
The Theory of Pattern Matching
A pattern is any arrangement of objects or entities. The term "arrangement" is used here to indicate that a pattern is by definition non-random and at least potentially describable. All theories imply some pattern, but theories and patterns are not the same thing. In general, a theory postulates structural relationships between key constructs. The theory can be used as the basis for generating patterns of predictions. For instance, E=MC2 can be considered a theoretical formulation. A pattern of expectations can be developed from this formula by generating predicted values for one of these variables given fixed values of the others. Not all theories are stated in mathematical form, especially in applied social research, but all theories provide information that enables the generation of patterns of predictions.
Pattern matching always involves an attempt to link two patterns where one is a theoretical pattern and the other is an observed or operational one. The top part of the figure shows the realm of theory. The theory might originate from a formal tradition of theorizing, might be the ideas or "hunches" of the investigator, or might arise from some combination of these. The conceptualization task involves the translation of these ideas into a specifiable theoretical pattern indicated by the top shape in the figure. The bottom part of the figure indicates the realm of observation. This is broadly meant to include direct observation in the form of impressions, field notes, and the like, as well as more formal objective measures. The collection or organization of relevant operationalizations (i.e., relevant to the theoretical pattern) is termed the observational pattern and is indicated by the lower shape in the figure. The inferential task involves the attempt to relate, link or match these two patterns as indicated by the double arrow in the center of the figure. To the extent that the patterns match, one can conclude that the theory and any other theories which might predict the same observed pattern receive support.
It is important to demonstrate that there are no plausible alternative theories that account for the observed pattern and this task is made much easier when the theoretical pattern of interest is a unique one. In effect, a more complex theoretical pattern is like a unique fingerprint which one is seeking in the observed pattern. With more complex theoretical patterns it is usually more difficult to construe sensible alternative patterns that would also predict the same result. To the extent that theoretical and observed patterns do not match, the theory may be incorrect or poorly formulated, the observations may be inappropriate or inaccurate, or some combination of both states may exist.
All research employs pattern matching principles, although this is seldom done consciously. In the traditional two-group experimental context, for instance, the typical theoretical outcome pattern is the hypothesis that there will be a significant difference between treated and untreated groups. The observed outcome pattern might consist of the averages for the two groups on one or more measures. The pattern match is accomplished by a test of significance such as the t-test or ANOVA. In survey research, pattern matching forms the basis of generalizations across different concepts or population subgroups. In qualitative research pattern matching lies at the heart of any attempt to conduct thematic analyses.
While current research methods can be described in pattern matching terms, the idea of pattern matching implies more, and suggests how one might improve on these current methods. Specifically, pattern matching implies that more complex patterns, if matched, yield greater validity for the theory. Pattern matching does not differ fundamentally from traditional hypothesis testing and model building approaches. A theoretical pattern is a hypothesis about what is expected in the data. The observed pattern consists of the data that are used to examine the theoretical model. The major differences between pattern matching and more traditional hypothesis testing approaches are that pattern matching encourages the use of more complex or detailed hypotheses and treats the observations from a multivariate rather than a univariate perspective.
Pattern Matching and Construct Validity
While pattern matching can be used to address a variety of questions in social research, the emphasis here is on its use in assessing construct validity.
The accompanying figure shows the pattern matching structure for an example involving five measurement constructs -- arithmetic, algebra, geometry, spelling, and reading. In this example, we'll use concept mapping to develop the theoretical pattern among these constructs. In the concept mapping we generate a large set of potential arithmetic, algebra, geometry, spelling, and reading questions. We sort them into piles of similar questions and develop a map that shows each question in relation to the others. On the map, questions that are more similar are closer to each other, those less similar are more distant. From the map, we can find the straight-line distances between all pair of points (i.e., all questions). This is the matrix of interpoint distances. We might use the questions from the map in constructing our measurement instrument, or we might sample from these questions. On the observed side, we have one or more test instruments that contain a number of questions about arithmetic, algebra, geometry, spelling, and reading. We analyze the data and construct a matrix of inter-item correlations.
What we want to do is compare the matrix of interpoint distances from our concept map (i.e., the theoretical pattern) with the correlation matrix of the questions (i.e., the observed pattern). How do we achieve this? Let's assume that we had 100 prospective questions on our concept map, 20 for each construct. Correspondingly, we have 100 questions on our measurement instrument, 20 in each area. Thus, both matrices are 100x100 in size. Because both matrices are symmetric, we actually have (N(N-1))/2 = (100(99))/2 = 9900/2 = 4,950 unique pairs (excluding the diagonal). If we "string out" the values in each matrix we can construct a vector or column of 4,950 numbers for each matrix. The first number is the value comparing pair (1,2), the next is (1,3) and so on to (N-1, N) or (99, 100). Now, we can compute the overall correlation between these two columns, which is the correlation between our theoretical and observed patterns, the "pattern matching correlation." In this example, let's assume it is -.93. Why would it be a negative correlation? Because we are correlating distances on the map with the similarities in the correlations and we expect that greater distance on the map should be associated with lower correlation and less distance with greater correlation.
The pattern matching correlation is our overall estimate of the degree of construct validity in this example because it estimates the degree to which the operational measures reflect our theoretical expectations.
Advantages and Disadvantages of Pattern Matching
There are several disadvantages of the pattern matching approach to construct validity. The most obvious is that pattern matching requires that you specify your theory of the constructs rather precisely. This is typically not done in applied social research, at least not to the level of specificity implied here. But perhaps it should be done. Perhaps the more restrictive assumption is that you are able to structure the theoretical and observed patterns the same way so that you can directly correlate them. We needed to quantify both patterns and, ultimately, describe them in matrices that had the same dimensions. In most research as it is currently done it will be relatively easy to construct a matrix of the inter-item correlations. But we seldom currently use methods like concept mapping that enable us to estimate theoretical patterns that can be linked with observed ones. Again, perhaps we ought to do this more frequently.
There are a number of advantages of the pattern matching approach, especially relative to the multitrait-multimethod matrix (MTMM). First, it is more general and flexible than MTMM. It does not require that you measure each construct with multiple methods. Second, it treats convergence and discrimination as a continuum. Concepts are more or less similar and so their interrelations would be more or less convergent or discriminant. This moves the convergent/discriminant distinction away from the simplistic dichotomous categorical notion to one that is more suitably post-positivist and continuous in nature. Third, the pattern matching approach does make it possible to estimate the overall construct validity for a set of measures in a specific context. Notice that we don't estimate construct validity for a single measure. That's because construct validity, like discrimination, is always a relative metric. Just as we can only ask whether you have distinguished something if there is something to distinguish it from, we can only assess construct validity in terms of a theoretical semantic or nomological net, the conceptual context within which it resides. The pattern matching correlation tells us, for our particular study, whether there is a demonstrable relationship between how we theoretically expect our measures will interrelate and how they do in practice. Finally, because pattern matching requires a more specific theoretical pattern than we typically articulate, it requires us to specify what we think about the constructs in our studies. Social research has long been criticized for conceptual sloppiness, for re-packaging old constructs in new terminology and failing to develop an evolution of research around key theoretical constructs. Perhaps the emphasis on theory articulation in pattern matching would encourage us to be more careful about the conceptual underpinnings of our empirical work. And, after all, isn't that what construct validity is all about?
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