Discussion Post

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Thousand Oaks, CA: Sage.

· Chapter 2, “Designing Case Studies: Identifying Your Case(s) and Establishing the Logic of Your Case Study” (pp. 25-80)

General Approach To Designing Case Studies

Chapter 1 has shown when you might choose to do case study research, as opposed to other types of research, to carry out a new study. The next step is to design your case study. For this purpose, as in designing any other type of research, you need a research design.

The research design will call for careful craftwork. Unlike other research methods, a standard catalog of case study designs has yet to emerge. There are no textbooks, like those in the biological and psychological sciences, covering such design considerations as the assignment of subjects to different groups, the selection of different stimuli or experimental conditions, or the identification of various response measures (see Cochran & Cox, 1992; Fisher, 1990; Sidowski, 1966). In an experiment, each of these choices reflects an important logical connection to the issues being studied. Nor have any common case study designs emerged—such as the panel studies, for example—used in surveys (see Kidder & Judd, 1986, chap. 6).

One pitfall to be avoided, however, is to consider case study designs as a subset or variant of the research designs used for other methods, such as quasi-experiments (e.g., Campbell & Stanley, 1966; Cook & Campbell, 1979). For a long time, scholars incorrectly thought that the case study was but one type of quasi-experimental design (the “one-shot post-test-only” design—Campbell & Stanley, 1966, pp. 6–7). Although the misperception lingers to this day, it was later corrected when one of the original authors made the following statement in the revision to his original work on quasi-experimental designs:

Certainly the case study as normally practiced should not be demeaned by identification with the one-group post-test-only design. (Cook & Campbell, 1979, p. 96)

Tip: How should I select the case(s) for my case study?

You need sufficient access to the data for your potential case—whether to interview people, review documents or records, or make field observations. Given such access to more than a single candidate case, you should choose the case(s) that will most likely illuminate your research questions. Absent sufficient access, you may want to consider changing your research questions, hopefully leading to new candidates to which you do have access.

Do you think access should be so important?

In other words, the one-shot, posttest-only design as a quasi-experimental design still may be flawed, but case studies have now been recognized as something different, with their own research designs.

Unfortunately, case study designs have not been codified. The following chapter therefore expands on the ground broken by earlier editions of this book and describes a basic set of research designs for doing single- and multiple-case studies. Although these designs will need to be modified and improved in the future, they will nevertheless help you to design more rigorous and methodologically sound case studies.

Definition of Research Designs

Every type of empirical research study has an implicit, if not explicit, research design. In the most elementary sense, the design is the logical sequence that connects the empirical data to a study’s initial research questions and, ultimately, to its conclusions. Colloquially, a research design is a logical plan for getting from here to there, where here may be defined as the set of questions to be addressed, and there is some set of conclusions about these questions. Between here and there may be found a number of major steps, including the collection and analysis of relevant data. As a summary label, another textbook has labeled a research design as a logical model of proof (Nachmias & Nachmias, 2014).

Another way of thinking about a research design is as a “blueprint” for your research, dealing with what questions to study, what data are relevant, what data to collect, and how to analyze the results (Philliber, Schwab, & Samsloss, 1980).

Note that a research design is more than a work plan. The design’s main purpose is to avoid the situation in which the evidence does not address the research questions. In this sense, the design deals with a logical, not a logistical, problem. For example, suppose you want to study a single organization. Your research questions have to do with the organization’s competitive or collaborative relationships with other organizations. You can properly address such questions only if you collect information from the other organizations, not just the one you started with. If you examine the relationships from the vantage point of only one organization, you cannot draw unbiased conclusions. This is a flaw in your research design, not in your work plan.

Components of Research Designs

In case study research, five components of a research design are especially important:

1. A case study’s questions;

2. Its propositions, if any;

3. Its case(s);

4. The logic linking the data to the propositions; and

5. The criteria for interpreting the findings.

Study questions.

This first component has already been described in Chapter 1, which suggested that the form of the question—in terms of “who,” “what,” “where,” “how,” and “why”—provides an important clue regarding the most relevant research method to be used. Case study research is most likely to be appropriate for “how” and “why” questions, so your initial task is to clarify precisely the nature of your study questions in this regard.

More troublesome may be your having to come up with the substance of the questions. Many students take an initial stab, only to be discouraged when they find the same question(s) already well covered by previous research. Other less desirable questions focus on too trivial or minor parts of an issue.

A helpful hint is to move in three stages. In the first, try to use the literature to narrow your interest to a key topic or two, not worrying about any specific research questions. In the second, examine closely—even dissect—a few key studies on your topic of interest. Identify the questions in those few studies and whether they conclude with new questions or loose ends for future research. These may then stimulate your own thinking and imagination, and you may find yourself articulating some potential questions of your own. In the third stage, examine another set of studies on the same topic. They may reinforce the relevance and importance of your potential questions or even suggest ways of sharpening them.

As a brief reminder, Chapter 1 also mentioned that, even in the absence of defining your research questions, you could start with some fieldwork first. What’s going on in the field might then suggest relevant questions for study. However, be careful about this alternative. You may be unduly swayed by transient conditions that won’t lead to insightful research questions. Also, a lot is going on in the field, so knowing where to focus your attention may be no easier than culling the literature to identify good questions.

Study propositions.

As for the second component, each proposition directs attention to something that should be examined within the scope of study. For instance, assume that your research, on the topic of interorganizational partnerships, began with the following question: How and why do organizations collaborate with one another to provide joint services (e.g., a manufacturer and a retail outlet collaborating to sell certain computer products)? These “how” and “why” questions, capturing what you are really interested in addressing, led you to case study research as the appropriate method in the first place. Nevertheless, these “how” and “why” questions may not sufficiently point to what you should study.

Only if you are forced to state some propositions will you move in the right direction. For instance, you might think that organizations collaborate because they derive mutual benefits. This proposition, besides reflecting an important theoretical issue (that other incentives for collaboration do not exist or are unimportant), also begins to tell you where to look for relevant evidence (i.e., to define and ascertain the extent of specific benefits to each organization).

At the same time, exploratory studies may have a legitimate reason for not having any propositions. Every exploration, however, should still have some purpose. Instead of propositions, the design for an exploratory study should state this purpose, as well as the criteria by which an exploration will be judged successful (or not). One successful outcome might include the identification of the propositions to be examined in the later study. Consider the analogy in BOX 5 for exploratory case studies. Can you imagine how you would ask for support from Queen Isabella to do your exploratory study?

Box 5 “Exploration” as an Analogy for an Exploratory Case Study

When Christopher Columbus went to Queen Isabella to ask for support for his “exploration” of the New World, he had to have some reasons for asking for three ships (Why not one? Why not five?), and he had some rationale for going westward (Why not south? Why not south and then east?). He also had some (mistaken) criteria for recognizing the Indies when he actually encountered them. In short, his exploration began with some rationale and direction, even if his initial assumptions might later have been proved wrong (Wilford, 1992). This same degree of rationale and direction should underlie even an exploratory case study.

For an example of an exploratory case study, see Application 1 at the end of this chapter.

The “case.”

This third component deals with your identifying the “case” to be studied—a problem that rightfully confronts many researchers at the outset of their case studies (e.g., Ragin & Becker, 1992). You will need to consider at least two different steps: defining the case and bounding the case.

In defining the case, the classic case studies usually focus on an individual person as the case (e.g., Bromley, 1986, p. 1). Jennifer Platt (1992) has noted how the early case studies by scholars in the Chicago school of sociology were life histories of such persons as juvenile delinquents or derelict men. You also can imagine case studies of clinical patients (e.g., Brice, Wallace, & Brice, 2014; Johansen, Tavakoli, Bjelland, & Lumley, 2017), exemplary students (e.g., Jett, Curry, & Vernon-Jackson, 2016; Schmitt & Goebel, 2015), teachers (e.g., Parsons, 2012), or different leaders. In each situation, an individual person is the case being studied. Information about the relevant individual would be collected, and several such individuals or “cases” might be included in a multiple-case study.

You would still need study questions and study propositions to help identify the relevant information to be collected about this individual or individuals. Without such questions and propositions, you might be tempted to cover “everything” about the individual(s), which is impossible to do. For example, the propositions in studying these individuals might be limited to the influence of early childhood or the role of peer relationships. Such seemingly general topics nevertheless represent a vast narrowing of the relevant scope and subsequent need for data. The more a case study contains specific questions and propositions, the more it will stay within feasible limits.

Of course, the “case” also can be some event or entity other than a single person. Case studies have been done about a broad variety of topics, including small groups such as families (e.g., Kindell, Sage, Wilkinson, & Keady, 2014), citizen participation (e.g., Frieling, Lindenberg, & Stokman, 2014; Wang & Breyer, 2012), communities, decisions, programs (e.g., Gavaravarapu & Pavarala, 2014), nonprofit organizations (e.g., Kohl-Arenas, 2016), organizational learning (e.g., Ohemeng & Owusu, 2015), schools (e.g., Dimartino & Jessen, 2016), and events such as social movements (e.g., Vos & Wagenaar, 2014) and disaster recovery efforts (e.g., Chung, 2017; Downey, 2016). Feagin et al. (1991) also contains some classic examples of these single-cases in sociology and political science.

Beware of these types of cases—none is easily defined in terms of the beginning or end points of the “case.” For example, a case study of a specific program may reveal (a) variations in program definition, depending on the perspective of different actors, and (b) program components that preexisted the formal designation of the program. Any case study of such a program would therefore have to clarify whether these conditions form part of the case (or not). Similarly, you might at first identify a specific locale, such as a “city,” as your case. However, your research questions and data collection might in fact be limited to tourism in the city, city policies, or city government. These choices would differ from defining the geographic city and its population as your case.

As a general clue, the tentative definition of your case can derive from the way you define your initial research question(s). Suppose, for example, you want to study the role of the United States in the global economy. Years ago, Peter Drucker (1986) wrote a provocative essay (but not a case study) about fundamental changes in the world economy, including the importance of “capital movements” independent of the flow of goods and services. If you were interested in doing a case study on this topic, Drucker’s work would only serve as a starting point. You would still need to define the research question(s) of interest to you, and each question might point to a different type of case. Depending on your question(s), the appropriate case might be a country’s economy, an industry in the world marketplace, an economic policy, or the trade or capital flow between countries. Each case and its related questions and propositions would call for a different case study, each having its own research design and data collection strategy.

If your research questions do not lead to the favoring of one case over another, your questions may be too vague or too numerous—and you may have trouble doing a case study. However, when you eventually arrive at a definition of your case(s), do not consider closure permanent. Your case definition, as with other facets of your research design, can be revisited as a result of discoveries during your data collection (see discussion and cautions about maintaining an adaptive posture, throughout this book and at the end of this chapter).

Sometimes, the case may have been defined one way, even though the phenomenon being studied actually follows a different definition. For instance, investigators might have confused case studies of neighborhoods with case studies of small groups. How a geographic area such as a neighborhood copes with racial transition, upgrading, and other phenomena can be quite different from how a small group copes with these same phenomena. For instance, two classic case studies, Street Corner Society (Whyte, 1943/1993; see BOX 2A in Chapter 1 of this book) and Tally’s Corner (Liebow, 1967; see BOX 9, this chapter), frequently have been mistaken for being case studies of neighborhoods when in fact they are case studies of small groups (note that in neither book is the neighborhood geography described, even though the small groups lived in a small area with clear neighborhood definitions if not boundaries). In contrast, BOX 6 presents a good example of how cases can be defined in a more discriminating manner—in the field of world trade.

Box 6 Defining the Case

Ira Magaziner and Mark Patinkin’s (1989) book, The Silent War: Inside the Global Business Battles Shaping America’s Future, presents nine individual case studies. Each case study helps the reader to understand a real-life situation of international economic competition.

Two of the cases appear similar but in fact represent different types of cases. One case covers a firm—the Korean firm Samsung—and the critical policies that make it competitive. Understanding Korean economic development is part of the context, and the case study also contains a nested entity—Samsung’s development of the microwave oven as an illustrative product. The other case covers a country—Singapore—and the policies that make it competitive. Within the country case study also is a nested unit—the development of an Apple computer factory in Singapore, serving as an illustrative example of how the national policies influence foreign investments.

To reduce the confusion and ambiguity in defining your case, one recommended practice is to discuss your potential case selection with a colleague. Try to explain to that person what questions you are trying to address and why you have chosen a specific case or group of cases as a way of addressing those questions. This may help you to avoid incorrectly identifying your case.

Once you have defined your case, other clarifications—sometimes called bounding the case—become important. For instance, if the case is a small group, the persons to be included within the group (they will become the immediate topic of your case study) must be distinguished from those who are outside of it (they will become part of the context for your case study). Similarly, if the case is about the local services in a specific geographic area, you need to decide which services to cover. Also desirable, for almost any topic that might be chosen, are the specific time boundaries to define the estimated beginning and ending of the case, for the purposes of your study (i.e., whether to include the entire or only some part of the life cycle of the entity that will become the case). Bounding the case in these ways will help to determine the scope of your data collection and, in particular, how you will distinguish data about the subject of your case study (the “phenomenon”) from data external to the case (the “context”). The bounding also should tighten the connection between your case and your research questions and propositions.

Exercise 2.1 Defining the Boundaries of a Case

Select a topic for a case study you would like to do. Identify some research questions to be answered or propositions to be examined by your case study. Does the naming of these questions or propositions clarify the boundaries of your case with regard to the time period covered by the case study; the relevant social group, organization, or geographic area; the type of evidence to be collected; and the priorities for data collection and analysis? If not, should you sharpen the original questions?

These latter cautions regarding the need for spatial, temporal, and other explicit boundaries underlie a key but subtle aspect in defining your case. The desired case should be a real-world phenomenon that has some concrete manifestation. The case cannot simply be an abstraction, such as a claim, an argument, or even a hypothesis. These abstractions could rightfully serve as the starting points for research studies using other kinds of methods and not just case study research. To justify doing case study research when only starting with an abstraction, you need to go one step further: You need to define a specific, real-world “case” to be the concrete manifestation of any abstraction. (For examples of more concrete and less concrete case study topics, see Figure 2.1.)

Figure 2.1 Illustrative Cases for Case Studies

Source: Clip Art © Jupiter Images.

Take the concept of “neighboring.” Alone, it could be the subject of research studies using methods other than the case study method. The other methods might include a survey of the relationships among neighbors, a history of the evolution of the sense of neighboring and the creation of neighborhood boundaries, or an experiment in which young children do tasks next to each other to determine the distracting effects, if any, of their “neighbors” in a classroom. These examples show how the abstract concept of “neighboring” does not alone produce the grounds for a case study. However, the concept could readily become a case study topic if it were accompanied by your selecting a specific neighborhood (“case”) to be studied and posing study questions and propositions about the neighborhood in relation to the concept of “neighboring.” (For a discussion of how the “case” was defined to start a case study, see Application 2 at the end of this chapter.)

One final point pertains to the role of the available research literature. Most researchers will want to conclude their case studies by comparing their findings with previous research. For this reason, the key definitions used at the outset of your case study should not be unknowingly idiosyncratic. Rather, the terminology used to define the case should be relatable to those previously studied by others—or should innovate in clear, operationally defined ways. In this manner, the previous literature also can become a guide for defining the case, whether you are trying to emulate or to deviate from the literature.

Exercise 2.2 Defining the “Case” for a Case Study

Examine Figure 2.1. Discuss each subject, which illustrates a different kind of case. Find a published case study on at least one of these subjects, indicating the specific case that was studied. Understanding that each subject involves the selection of different cases to be studied, do you think that the more concrete units might be easier to define than the less concrete ones? Why?

Linking data to propositions.

The fourth component has been increasingly better developed in doing case study research. The component foreshadows the data analysis steps in your case study. Chapter 5 covers these steps and the various analytic techniques and choices in detail. However, during the design stage, you need to be aware of the choices and how they might suit your case study. In this way, your research design can create a more solid foundation for the later analysis.

All the analytic techniques in Chapter 5 represent ways of linking data to propositions: pattern matching, explanation building, time-series analysis, logic models, and cross-case synthesis. The actual analyses will require that you combine or assemble your case study data as a direct reflection of your study propositions. For instance, knowing that some or all of your propositions cover a temporal sequence would mean that you might eventually use some type of time-series analysis. If you note this strong likelihood during the design phase, you might make sure that your planned data collection includes the collection of appropriate time markers as part of the case being studied.

As a caution, if you have had limited experience in conducting empirical studies, at the design stage you may not easily identify the likely analytic technique(s) or anticipate the needed data to use the techniques to their full advantage. Even more experienced researchers may find that they have either (a) collected too much data that was not later used in any analysis, or (b) collected too little data that prevented the proper use of a desired analytic technique. Sometimes, the latter situation may force researchers to return to their data collection phase (if they can), to supplement the original data. The more you can avoid either of these situations, the better off you will be.

Criteria for interpreting the strength of a case study’s findings.

For other research methods, a common illustration of this fifth component arises when statistical analyses are relevant. For instance, by convention, quantitative studies consider a p level of less than .05 to demonstrate that observed differences are “statistically significant” and therefore associated with more robust findings. In other words, the statistical benchmarks serve as the criteria for interpreting the findings. However, much case study analysis will not rely on statistics, leading to the need to find other ways of thinking about such criteria.

When doing case study research, a major and important alternative strategy is to identify and address rival explanations for your findings. Addressing such rivals becomes a criterion for interpreting the strength of your findings: The more rivals that have been addressed and rejected, the stronger will be your findings. Again, Chapter 5 discusses this strategy and how it works. At the design stage of your work, the challenge is to anticipate and enumerate the potentially important rivals. You will then want to include data about them as part of your data collection. If you think of rival explanations only after data collection has been completed, your thinking will help 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.

Summary.

A research design should include five components. The first three components—that is, defining your study’s questions, propositions, and case(s)—will lead your research design into identifying the data that are to be collected. The last two components—that is, defining the logic linking the data to the propositions and the criteria for interpreting the findings—will lead the design into anticipating your case study analysis, suggesting what is to be done after the data have been collected.

The Role Of Theory In Research Designs

Covering the preceding five components of research designs can happen to move you toward constructing some preliminary theory or theoretical propositions related to your topic of study. At the same time, and as suggested previously, you may want to do some preliminary fieldwork before trying to specify any theory or propositions in greater detail. However, and also as pointed out previously, starting with some fieldwork first also has its perils. For instance, you cannot start as a true tabula rasa. You already will have some implicit theoretical orientation in deciding whom to contact in the field, in your opening perspective about what’s going on in the field, and in choosing what to observe and how to converse with participants. Without these predilections, you may get lost in your preliminary fieldwork. However, ignoring them can lead to a bias in your case study. As a result, you may at least want to acknowledge some preliminary theoretical considerations first.

Theory Development

The needed theory can be plain and simple. For example, a case study on the implementation of a new management information system (MIS) started with the following straightforward theoretical statement:

The case study will show why implementation only succeeded when the organization was able to re-structure itself, and not just overlay the new MIS on the old organizational structure. (Markus, 1983)

The statement presents the nutshell of a theory of MIS implementation—that is, that implementing an MIS goes beyond adding a new technology to an existing organization but requires some organizational restructuring to work.

The same MIS case study then added the following theoretical statement:

The case study will also show why the simple replacement of key persons was not sufficient for successful implementation. (Markus, 1983)

This second statement presents the nutshell of a rival theory—that is, that successful MIS implementation mainly calls for overcoming individuals’ resistance to change (and not any organizational restructuring), leading to the rival theory that the replacement of such people will permit implementation to succeed.

You can see that elaborating these two initial statements can help to shape the upcoming case study. The stated ideas will increasingly cover the questions, propositions, specifications for defining and bounding the case, logic connecting data to propositions, and criteria for interpreting the findings—that is, the five components of the needed research design. In this sense, the research design can come to embrace a “theory” of what is being studied.

The desired theory should by no means be considered with the formality of grand theory in social science. Nor are you being asked to be a masterful theoretician. Rather, the simple goal is to have a sufficient blueprint for your study, usefully noted by Sutton and Staw (1995) as “a [hypothetical] story about why acts, events, structure, and thoughts occur” (p. 378). However, you also should be prepared to heed Diane Vaughan’s (1992) wise words of caution:

The paradox of theory is that at the same time it tells us where to look, it can keep us from seeing. (p. 195)

Your theoretical propositions can represent key issues from the research literature. Alternatively, they can represent practical matters, such as differing types of instructional leadership styles or interpersonal relationships in a study of families and social groups.

Ultimately, the propositions will lead to a complete research design—and will provide surprisingly explicit ideas for determining the data to collect and the strategies for analyzing the data. For this reason, some theory development prior to the collection of any fieldwork is desirable. Paul Rosenbaum notes that, for nonexperimental studies more generally, the preferred theoretical statements should elaborate a complex pattern of expected results—the more complex the better (Rosenbaum, 2002, pp. 5–6 and 277–279). The benefit of the complexity will be a more articulated design and a heightened ability to interpret your eventual data.

However, theory development in case study research takes time and can be difficult (Eisenhardt, 1989; Rule & John, 2015). For some topics, existing works may provide a rich theoretical framework for designing a specific case study. Alternatively, if you desire your propositions to fill mainly descriptive functions (rather than trying to do an explanatory case study), your concern should focus on such issues as (a) the purpose of the descriptive effort, (b) the full but realistic range of topics that might be considered a “complete” description of what is to be studied, and (c) the likely topic(s) that will be the essence of the description. Good answers to these questions, including the rationales underlying the answers, will help you go a long way toward developing the needed theoretical base—and research design—for your study.

For some topics, the existing knowledge base may be poor, and neither the available literature nor the prevailing practical experiences will provide any conceptual ideas or hypotheses of note. Such a knowledge base does not lend itself to the development of good theoretical statements, and you should not be surprised if your new study ends up being an exploratory study. Nevertheless, as noted earlier with the illustrative case in BOX 5, even an exploratory case study should be preceded by statements about what is to be explored, the purpose of the exploration, and the criteria by which the exploration will be judged successful (or not).

Overall, you may want to gain a richer understanding of how theory is used in case studies by reviewing specific case studies that have been successfully completed. You can do this either by examining the completed case studies for their initial propositions or, as a more daring venture, by trying to understand the significance of the case study’s findings and conclusions. The findings and conclusions should be couched within some theoretically important issues, even if they may not have been openly stated at the outset of the case study.

Illustrative Topics for Theories

In general, to overcome the barriers to theory development, you should try to prepare for your case study by doing such things as reviewing the literature related to what you would like to study (e.g., see Cooper, 1984), discussing your topic and ideas with colleagues or teachers, and asking yourself challenging questions about what you are studying, why you are proposing to do the study, and what you hope to learn as a result of the study.

As a further reminder, you should be aware of the full range of theories that might be relevant to your study. For instance, note that the earlier MIS example illustrated MIS “implementation” theory and that this is but one type of theory that can be the subject of study. Other types of theories for you to consider include the following:

· Individual theories—for example, theories of individual development, cognitive behavior, personality, learning and disability, individual perception, and interpersonal interactions;

· Group theories—for example, theories of family functioning, informal groups, work teams, supervisory-employee relations, and interpersonal networks;

· Organizational theories—for example, theories of bureaucracies, organizational structure and functions, excellence in organizational performance, and interorganizational partnerships; and

· Social justice theories—for example, theories of housing segregation, international conflicts, cultural assimilation, uneven access to technologies, and marketplace inequities.

Other examples cut across these illustrative types. Decision-making theory (Carroll & Johnson, 1992), for instance, can involve individuals, organizations, or social groups. As another example, a common topic of case study research is the evaluation of publicly supported programs, such as federal, state, or local programs. In this situation, the development of a theory of how a program is supposed to work is essential to the design of the evaluation. In this situation, Bickman (1987) reminds us that the theory needs to distinguish between the substance of the program (e.g., how to make education more effective) and the process of program implementation (e.g., how to install an effective program). The distinction would avoid situations where policy makers might want to know the desired substantive remedies (e.g., findings about a newly effective curriculum) but where an evaluation unfortunately focused on managerial issues (e.g., the need to hire a good project director). Such a mismatch can be avoided by giving closer attention to the substantive theory of interest.

Using Theory to Generalize From Case Studies

Besides making it easier to design your case study, having some theory or theoretical propositions will later play a critical role in helping you to generalize the lessons learned from your case study. This role of theory has been characterized throughout this book as the basis for analytic generalization and has been contrasted with another way of generalizing the results from empirical studies, known as statistical generalization. Understanding the distinction between these two types of generalization may be your most notable accomplishment in doing case study research.

Let us first take the more commonly recognized way of generalizing—statistical generalization—although it is the less relevant one for doing case study research. In statistical generalization, an inference is made about a population (or universe) on the basis of empirical data collected from a sample from that universe. This is shown graphically as a Level One inference in Figure 2.2.1 This method of generalizing is commonly followed when doing surveys (e.g., Fowler, 2014; Lavrakas, 1993) or analyzing archival data such as in studying housing or employment trends. As another example, political polls need to generalize their findings beyond their sample of respondents and to apply to the larger population, and research investigators readily follow statistical procedures to determine the confidence with which such extrapolations can be made.

A fatal flaw in doing case studies is to consider statistical generalization to be the way of generalizing the findings from your case study. This is because your case or cases are not “sampling units” and also will be too few in number to serve as an adequately sized sample to represent any larger population.

Generalizing from the case study, not from the case(s).

Rather than thinking about your case(s) as a sample, you should think of your case study as the opportunity to shed empirical light on some theoretical concepts or principles. The goal is not unlike the motive of a laboratory investigator in conducting and then learning from a new experiment. In this sense, both a case study and an experiment have an interest in going beyond the specific case or experiment. Both kinds of studies are likely to strive for generalizable findings or lessons learned—that is, analytic generalizations—that go beyond the setting for the specific case or experiment that had been studied. (Also see Tutorial 2.1 on the companion website at study.sagepub.com/yin6e for more detail about defining “analytic generalization.”)

For example, the lessons learned could assume the form of a working hypothesis (Cronbach, 1975), either to be applied in reinterpreting the results of existing studies of other concrete situations (i.e., other case studies or experiments) or to define new research focusing on yet additional concrete situations (i.e., new case studies or experiments). Note that the aim of an analytic generalization is still to generalize to these other concrete situations and not just to contribute to abstract theory building. Also note that the generalizations, principles, or lessons learned from a case study may potentially apply to a variety of situations, well beyond any strict definition of the hypothetical population of “like cases” represented by the original case (Bennett, 2010).

The theory or theoretical propositions that went into the initial design of your case study, as empirically enhanced by your case study’s findings, will have formed the groundwork for your analytic generalization(s). Alternatively, a new generalization may emerge from the case study’s findings alone. In other words, the analytic generalization may be based on either (a) corroborating, modifying, rejecting, or otherwise advancing theoretical concepts that you referenced in designing your case study or (b) new concepts that arose upon the completion of your case study.

The important point is that, regardless of whether the generalization was derived from the conditions you specified at the outset or uncovered at the conclusion of your case study, the generalization will be at a conceptual level higher than that of the specific case (or the subjects participating in an experiment2)—shown graphically as a Level Two inference in Figure 2.2. By moving to this higher conceptual level, also realize that you need to make an analytic generalization as a claim, by providing a supportive argument. Your experience will be far different from simply applying the numeric result emanating from the use of some formulaic procedure, as in making statistical generalizations. However, the implications for your analytic generalization can lead to greater insight about the “how” and “why” questions that you posed at the outset of your case study.

Figure 2.2 Making Inferences: Two Levels

Illustrative examples.

Several prominent case studies illustrate how analytic generalizations can use a case study’s findings to implicate new situations. First, consider how the two initial case studies highlighted in BOXES 1 and 2A of Chapter 1 of this book treated the generalizing function:

· BOX 1 : Allison’s (1971) case is about the Cuban missile crisis, but he relates the three theoretical models from his case study to many other situations, first to other international confrontations, such as between the United States and North Vietnam in the 1960s (p. 258). The later edition of his case study (Allison & Zelikow, 1999) then discusses the models’ relevance to the “rethinking of nuclear threats to Americans today” (p. 397) as well as to the broader challenge of inferring the motives underlying actions taken by a foreign power.

· BOX 2 A: Whyte’s study (1943/1993) is well known for uncovering the relationship between individual performance and group structure, highlighted by a bowling tournament where he directly experienced the impact on his own performance (“as if something larger than myself was controlling the ball”— p. 319) and observed how the gang members’ bowling scores, with one notable exception, emulated their standing in the gang. Whyte generalizes his findings by later commenting that “I believed then (and still believe now) that this sort of relationship may be observed in other group activities everywhere” (p. 319).

Second, BOX 7 contains four additional illustrations. All show how findings from a single-case study nevertheless can be generalized to a broad variety of other situations. The fourth of these case studies has one other notable feature: It demonstrates how an entire case study can be published as a journal article (the first three examples appeared in the form of rather lengthy books).

Analytic generalization can be used whether your case study involves one or several cases, which shall be later referenced as single-case or multiple-case studies. Also to come later in this chapter, the discussion under the topic of external validity adds a further insight about making analytic generalizations. The main point at this juncture is that you should try to aim toward analytic generalizations 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- or multiple-case study were equivalent to respondents in a survey. In other words, again as graphically depicted in Figure 2.2, you should aim for Level Two inferences when generalizing from case studies.

In a like manner, even referring to your case or cases as a “purposive sample” may raise similar conceptual and terminological problems. You may have intended to convey that the “purposive” portion of the term reflects your selection of a case that will illuminate the theoretical propositions of your case study. However, your use of the “sample” portion of the term still risks misleading others into thinking that the case comes from some larger universe or population of like cases, undesirably reigniting the specter of statistical generalization. The most desirable posture may be to state a clear caveat if you have to refer to any kind of sample (purposive or otherwise). (The preferred criteria and terminology for selecting cases, as part of either a single- or a multiple-case study, are discussed later in this chapter under the topic of “case study designs.”) In this sense, case study research directly parallels experimental research: Few if any people would consider that a new experiment should be designed as a sample (of any kind) from a larger population of like experiments—and few would consider that the main way of generalizing the findings from a single experiment would be in reference to a population of like experiments.

Box 7 Generalizing From Single-Case Studies: Four More Examples

7A. A Sociology of “Mistake”

The tragic loss of the space shuttle Challenger in 1986, vividly shown in repeated TV replays of the spaceship’s final seconds, certainly qualifies as a unique case. The causes of this loss became the subject of a Presidential Commission and of a case study by Diane Vaughan (2016). Vaughan’s detailed study shows how the social structure of an organization (the NASA space agency) had, over time, transformed deviance into acceptable and routine behavior.

Vaughan’s ultimate explanation differs markedly from that of the Presidential Commission, which pointed to individual errors by middle managers as the main reasons for failure. In Vaughan’s words, her study “explicates the sociology of mistake”—that “mistakes are systemic and socially organized, built into the nature of professions, organizations, cultures, and structures.” She shows how deviance is transformed into acceptable behavior through the institutionalization of production pressures (originating in the organizational environment), leading to “nuanced, unacknowledged, pervasive effects on decisionmaking.” Her final discussion applies this generalization to a diverse array of other situations. As examples, she cites studies showing the research distortions created by the worldview of scientists, the uncoupling of intimate relationships, and the inevitability of accidents in certain technological systems. All these illustrate the process of making analytic generalizations.

7B. The Origins of Social Class

The second example (which comes from Application 3) is about the uncovering and labeling of a social class structure based on a case study of a medium-sized American city, Yankee City (Warner & Lunt, 1941). This classic case study in sociology made a critical contribution to social stratification theory and an understanding of the social differences among “upper,” “upper-middle,” “middle-middle,” “upper-lower,” and “lower” classes. Over the years, the insights from these differences have applied to a broad range of social structures, by no means limited to other medium-sized cities (or even to cities).

7C. Contribution to Urban Planning

The third example is Jane Jacobs and her famous book, The Death and Life of Great American Cities (1961). The book is based mostly on experiences from a single-case, New York City. The book’s chapters then show how these New York experiences can be used to develop broader theoretical principles in urban planning, such as the role of sidewalks, the role of neighborhood parks, the need for primary mixed uses, the need for small blocks, and the processes of slumming and unslumming.

Jacobs’s book created heated controversy in the planning profession. New empirical inquiries were made about one or another of her rich and provocative ideas. These inquiries helped to test the broader applicability of her principles to other concrete settings, and in this way Jacobs’s work still stands as a significant contribution in the field of urban planning.

7D. Government Management of “Spoiled” National Identity

The fourth example creatively extended Erving Goffman’s well-known sociological theory, regarding the management of stigma by individual people, to an institutional level (Rivera, 2008). A field-based case study of Croatia showed how the stigma created by the wars of Yugoslav secession had demolished the country’s image as a desirable tourist destination, but then how the country successfully used an impression management strategy to revive the tourism. Croatia thus presented “an exciting case of reputation management in action” (p. 618). The author suggests that her adapted theoretical model can be used as “a launching point for understanding the public representation dilemmas faced by other states and organizational actors that have undergone reputation-damaging events” (p. 615). In so doing, the case study has provided another illustration of analytic generalization.

The challenge of making analytic generalizations involves understanding that the generalization is not statistical (or numeric) and that you will be making an argumentative claim. In so doing, you need to give explicit attention to the potential flaws in your claims and therefore discuss your analytic generalizations, not just state them. And to repeat an earlier point, remember that you are generalizing from your case study, not from your case(s).3

Summary

This section has suggested that a complete research design, while including the five components previously described, will benefit from the development of theoretical propositions. A good case study researcher should pursue such propositions and take advantage of this benefit, whether the case study is to be exploratory, descriptive, or explanatory. The use of theory and theoretical propositions in doing case studies can be an immense aid in defining the appropriate research design and data to be collected. Equally important, the same theoretical orientation also will become the main vehicle for generalizing the findings from the case study.

Criteria For Judging The Quality Of Research Designs

Because a research design is supposed to represent a logical set of statements, you also can judge the quality of any given design according to certain logical tests. Four tests have been commonly used to establish the quality of most empirical social research. Because case study research is part of this larger body, the four tests also are relevant to case study research.

An important innovation of this book is the identification of several tactics for dealing with these four tests when doing case study research. Figure 2.3 lists the tests and the recommended tactics, as well as a cross-reference to the phase of research when the tactic is to be used. (Each tactic is described in detail in the chapter of this book referenced in Figure 2.3.)

Because the four tests are common to most social science methods, the tests have been summarized in numerous textbooks (e.g., see Kidder & Judd, 1986, pp. 26–29). The tests also have served as a framework for assessing a large group of case studies in the field of strategic management (Gibbert et al., 2008). The four tests are

· Construct validity:  identifying correct operational measures for the concepts being studied

· Internal validity  (for explanatory or causal studies only and not for descriptive or exploratory studies): seeking to establish a causal relationship, whereby certain conditions are believed to lead to other conditions, as distinguished from spurious relationships

· External validity:  showing whether and how a case study’s findings can be generalized

· Reliability:  demonstrating that the operations of a study—such as its data collection procedures—can be repeated, with the same results

Figure 2.3 Case Study Tactics for Four Design Tests

Each item on this list deserves explicit attention. For case study research, an important revelation is that the several tactics to be used in dealing with these tests should be applied throughout the subsequent conduct of a case study, not just at its beginning. Thus, the “design work” for doing case studies may actually continue beyond the initial design plans.

Construct Validity

This first test is especially challenging in case study research. People who have been critical of case studies often point to the fact that a case study researcher fails to develop a sufficiently operational set of measures and that “subjective” judgments—ones tending to confirm a researcher’s preconceived notions (Flyvbjerg, 2006; Ruddin, 2006)—are used to collect the data.4 Take an example such as studying “neighborhood change”—a common case study topic (e.g., Bradshaw, 1999; Keating & Krumholz, 1999): Over the years, concerns have arisen over how certain urban neighborhoods have changed their character. Any number of case studies have examined the types of changes and their consequences. However, without any prior specification of the significant, operational events that constitute “change,” a reader cannot tell whether the claimed changes in a case study genuinely reflect the events in a neighborhood or whether they happen to be based on a researcher’s impressions only.

Neighborhood change can cover a wide variety of phenomena: racial turnover, housing deterioration and abandonment, changes in the pattern of urban services, shifts in a neighborhood’s economic institutions, or the turnover from low- to middle-income residents in revitalizing neighborhoods. The choice of whether to aggregate blocks, census tracts, or larger areas also can produce different results (Hipp, 2007).

To meet the test of construct validity, an investigator must be sure to cover two steps:

1. Define neighborhood change in terms of specific concepts (and relate them to the original objectives of the study) and

2. Identify operational measures that match the concepts (preferably citing published studies that make the same matches).

For example, suppose you satisfy the first step by stating that you plan to study neighborhood change by focusing on trends in neighborhood crime. The second step now demands that you select a specific measure, such as police-reported crime (which happens to be the standard measure used in the FBI Uniform Crime Reports) as your measure of crime. The literature will indicate certain known shortcomings in this measure, mainly that unknown proportions of crimes are not reported to the police. You will then need to discuss how the shortcomings nevertheless will not bias your study of neighborhood crime and hence neighborhood change.

As previously shown in Figure 2.3, three tactics are available to increase construct validity when doing case studies. The first is the use of multiple sources of evidence, in a manner encouraging convergent lines of inquiry, and this tactic is relevant during data collection (see Chapter 4). A second tactic is to establish a chain of evidence, also relevant during data collection (also Chapter 4). The third tactic is to have the draft case study report reviewed by key informants (a procedure described further in Chapter 6).

Internal Validity

This second test has been given the greatest attention in experimental and quasi-experimental research (see Campbell & Stanley, 1966; Cook & Campbell, 1979). Numerous “threats” to internal validity have been identified, mainly dealing with spurious effects. Because so many textbooks already cover this topic, only two points need to be made here.

First, internal validity is mainly a concern for explanatory case studies, when an investigator is trying to explain how and why event x led to event y. If the investigator incorrectly concludes that there is a causal relationship between x and y without knowing that some third event—z—may actually have caused y, the research design has failed to deal with some threat to internal validity. Note that this logic is inapplicable to descriptive or exploratory studies (whether the studies are case studies, surveys, or experiments), which are not concerned with this kind of causal situation.

Second, the concern over internal validity, for case study research, extends to the broader problem of making inferences. Basically, a case study involves an inference every time an event cannot be directly observed. An investigator will “infer” that a particular event resulted from some earlier occurrence, based on interview and documentary evidence collected as part of the case study. Is the inference correct? Have all the rival explanations and possibilities been considered? Is the evidence convergent? Does it appear to be airtight? A research design that has anticipated these questions has begun to deal with the overall problem of making inferences and therefore the specific problem of internal validity.

However, the specific tactics for achieving this result are difficult to identify when doing case study research. Figure 2.3 (previously shown) suggests four analytic tactics. All are described further in Chapter 5 because they take place during the analytic phase of doing case studies: pattern matching, explanation building, addressing rival explanations, and using logic models.

External Validity

The third test deals with the problem of knowing whether a study’s findings are generalizable beyond the immediate study. For case studies, the issue relates directly to the earlier discussion of analytic generalization and the reference to Level Two in Figure 2.2. To repeat a key point from the earlier discussion, referring to statistical generalization and any analogy to samples and populations would be misguided.

Another insight on this issue derives from observing the form of the original research question(s) posed in doing your case study. The form of the question(s) can help or hinder the preference for seeking generalizations—that is, striving for external validity.

Recall that the decision to favor case study research should have started with the posing of some “how” and “why” question(s). For instance, many descriptive case studies deal with the “how” of a situation, whereas many explanatory case studies deal with the “why” of situations. However, if a case study has no pressing “how” or “why” questions—such as a study merely wanting to document the social trends in a neighborhood, city, or country or the employment trends in an organization (and essentially posing a “what” question)—arriving at an analytic generalization may be more difficult. To avoid this situation, augmenting the study design with “how” and “why” questions (and collecting the additional data) can be extremely helpful. (Alternatively, if a study’s research interest is entirely limited to documenting social trends and has no “how” or “why” questions, using some method other than case study research might serve the study’s objectives better.)

In this manner, the form of the initial research question(s) can directly influence the strategies used in striving for external validity. These research question(s) should have been settled during the research design phase of your case study. For this reason, Figure 2.3 as previously shown points to the research design phase, with the identification of appropriate theory or theoretical propositions, as being the most appropriate time for establishing the groundwork to address the external validity of your case study.

Reliability

Most people are probably already familiar with this final test. The objective is to be sure that, if a later researcher follows the same procedures as described by an earlier researcher and conducts the same study over again, the later investigator will arrive at the same findings and conclusions. To follow this procedure in case study research means studying the same case over again, not just replicating the results of the original case study by studying another case. The goal of reliability is to minimize the errors and biases in a study.

In reality, opportunities for repeating a case study rarely occur. However, you should still position your work to reflect a concern over reliability, if only in principle. The general need is to document the procedures followed in your case study. Without such documentation, you could not even repeat your own work (which is another way of dealing with reliability). In the past, case study research procedures were poorly documented, making external reviewers suspicious of the reliability of the case study method.5 To overcome these suspicions, and going beyond sheer documentation, Figure 2.3 previously suggested two highly desirable tactics—the use of a case study protocol to deal with the documentation problem in detail (discussed in Chapter 3) and the development of a case study database (discussed in Chapter 4).

The general way of approaching the reliability problem is to make as many procedures as explicit as possible and to conduct research as if someone were looking over your shoulder. Accountants and bookkeepers always are aware that any calculations must be capable of being audited. In this sense, an auditor also is performing a reliability check and must be able to produce the same results if the same procedures are followed. A good guideline for doing case studies is therefore to conduct the research so that an auditor could in principle repeat the procedures and hopefully arrive at the same results.

Summary

Four tests may be considered relevant in judging the quality of a research design. In designing and doing case studies, various tactics are available to deal with these tests, though not all of the tactics occur at the design phase in doing a case study. In fact, most of the tactics occur during the data collection, data analysis, or compositional phases of the research and are therefore described in greater detail in the subsequent chapters of this book.

Exercise 2.3 Defining the Criteria for Judging the Quality of Research Designs

Define the four criteria for judging the quality of research designs: (a) construct validity, (b) internal validity, (c) external validity, and (d) reliability. Give an example of each type of criterion in a case study you might want to do.

Case Study Research Designs

Traditional case study research has not usually included the idea of having formal designs, as might be found when doing survey or experimental research. You still may successfully conduct a new case study without any formal design. However, attending to the potential case study research designs can make your case studies stronger and, possibly, easier to do. You might therefore find the remainder of this section to be useful. It covers four types of designs, based on the 2 × 2 matrix in Figure 2.4.

The matrix first shows that every type of design will include the desire to analyze contextual conditions in relation to the “case,” with the dotted lines between the two signaling the likely blurriness between the case and its context. The matrix then shows that single- and multiple-case studies reflect different design situations and that, within these two variants, there also can be unitary or multiple units of analysis. The resulting four types of designs for case studies are (Type 1) single-case (holistic) designs, (Type 2) single-case (embedded) designs, (Type 3) multiple-case (holistic) designs, and (Type 4) multiple-case (embedded) designs. The rationale for these four types of designs is as follows.

Figure 2.4 Basic Types of Designs for Case Studies

Source: COSMOS Corporation.

What Are the Potential Single-Case Designs (Types 1 and 2)?

Five rationales for single-case designs.

A primary distinction in designing case studies is between single- and multiple-case study designs. This means the need for a decision, prior to any data collection, on whether you are going to have a single-case or multiple cases in your case study.

The single-case study is an appropriate design under several circumstances, and five single-case rationales—that is, having a criical, unusual, common, revelatory, or longitudinal case—are given below. Recall that a single-case study is analogous to a single experiment, and many of the same conditions that justify choosing a single experiment also can justify a single-case study.

Recall, too, that the selection of your case should be related to your theory or theoretical propositions of interest. These form the substantive context for each of the five rationales. Thus, the first rationale for a single-case—selecting a critical case—would be critical to your theory or theoretical propositions (again, note the analogy to the critical experiment). The theory should have specified a clear set of circumstances within which its propositions are believed to be true. You can then use the single-case to determine whether the propositions are correct or whether some alternative set of explanations might be more relevant. In this manner, like Graham Allison’s comparison of three theories and the Cuban missile crisis (described in Chapter 1, BOX 1), the single-case can represent a significant contribution to knowledge and theory building by confirming, challenging, or extending the theory. Such a study even can help to refocus future investigations in an entire field. (See BOX 8 for another example, in the field of organizational innovation.)

Box 8 The Critical Case as a Single-Case Study

One rationale for selecting a single-case rather than a multiple-case design is that the single-case can represent the critical test of a significant theory. Gross, Bernstein, and Giacquinta (1971) used such a design by focusing on a single school in their book, Implementing Organizational Innovations (also see BOX 20B, Chapter 4).

The school was selected because it had a history of innovation and could not be claimed to suffer from “barriers to innovation.” In the prevailing theories, such barriers had been prominently cited as the major reason that innovations failed. Gross et al. (1971) showed that, in this school, an innovation also failed but that the failure could not be attributed to any barriers. Implementation processes, rather than barriers, appeared to account for the failure.

In this manner, the book, though limited to a single-case, represented a watershed in organizational innovation theory. Prior to the study, analysts had focused on the identification of barriers to innovation; since the study, the literature has been much more dominated by studies of the implementation process, not only in schools but also in many other types of organizations.

A second rationale for a single-case arises when the case represents an extreme case or an unusual case, deviating from theoretical norms or even everyday occurrences. For instance, such cases can occur in clinical psychology, where a specific injury or disorder may offer a distinct opportunity worth documenting and analyzing. In clinical research, a common research strategy calls for studying these unusual cases because the findings may reveal insights about normal processes (e.g., Corkin, 2013). In this manner, the value of a case study can be connected to a large number of people, well beyond those suffering from the original clinical syndrome.

Conversely, a third rationale for a single-case is the common case. Here, the objective is to capture the circumstances and conditions of an everyday situation—again because of the lessons it might provide about the social processes related to some theoretical interest. In this manner, a street scene and its sidewalk vendors can become the setting for learning about the potential social benefits created by informal entrepreneurial activity (e.g., Duneier, 1999), and the social and institutional structure within a single, low-income urban neighborhood can provide insights into the relationship between poverty and social capital (e.g., Small, 2004).

A fourth rationale for a single-case study is the revelatory case. This situation exists when a researcher has an opportunity to observe and analyze a phenomenon previously inaccessible to social science inquiry, such as Whyte’s (1943/1993) Street Corner Society, previously described in Chapter 1, BOX 2A. Another example is Phillippe Bourgois’s (2003) study of crack and the drug-dealing marketplace in Spanish Harlem, a neighborhood in New York City. The author gained the trust and long-term friendship of two dozen street dealers and their families, revealing a lifestyle that few had been able to study up to that time. For another example, see Elliot Liebow’s (1967) famous case study of unemployed men, Tally’s Corner (BOX 9). When researchers have similar types of opportunities and can uncover some prevalent phenomenon previously inaccessible to social scientists, such conditions justify the use of a single-case study on the grounds of its revelatory nature.

Box 9 The Revelatory Case as a Single-Case Study

Another rationale for selecting a single-case is that the researcher has access to a situation previously inaccessible to empirical study. The case study is therefore worth conducting because the descriptive information alone will be revelatory.

Such was the situation in Elliot Liebow’s (1967) sociological classic, Tally’s Corner. The book is about a single group of African American men living in a poor, inner-city neighborhood. By befriending these men, the author was able to learn about their lifestyles, their coping behavior, and in particular their sensitivity to unemployment and failure. The book provided insights into socioeconomic conditions that have prevailed in many U.S. cities for a long time, but that had been only vaguely understood. The single-case showed how investigations of such topics could be done, thus stimulating much further research and eventually the development of needed public policy actions.

A fifth rationale for a single-case study is the longitudinal case: studying the same single-case at two or more different points in time. The theory of interest would likely specify how certain conditions and their underlying processes change over time. The desired time intervals would presumably reflect the anticipated stages at which the changes would most likely reveal themselves. They may be prespecified time intervals, such as prior to and then after some critical event, following a before-and-after logic. Alternatively, they might not deal with specific time intervals but cover trends over an elongated period of time, following a developmental course of interest. Under exceptional circumstances, the same case might be the subject of two consecutive case studies, such as occurred with Middletown (Lynd & Lynd, 1929) and Middletown in Transition (Lynd & Lynd, 1937). Whatever the time intervals or periods of interest, the processes being studied should nevertheless reflect the theoretical propositions posed by the case study.

These five serve as major rationales for selecting a single-case study. There are other situations in which the single-case study may be used as a pilot case that might be the beginning of a multiple-case study. However, in this latter situation, the single-case portion of the study would not be regarded as a complete case study on its own.

Whatever the rationale for doing single-case studies (and there may be more than the five mentioned here), a potential vulnerability of the single-case design is that a case may later turn out not to be the case it was thought to be at the outset. Single-case designs therefore require careful investigation of the candidate case, to minimize the chances of misrepresentation and to maximize the access needed to collect the case study evidence. A fair warning is not to commit yourself to any single-case study until these major concerns have been covered.

Holistic versus embedded single-case studies.

The same single-case study may involve units of analysis at more than one level. This occurs when, within a single-case (the first level), attention is also given to a subunit or subunits (a second level)—see BOX 10. For instance, even though a case study might be about a single organization, such as a hospital and the nature of its service culture, the analysis might include systematic data from some element within the hospital (e.g., a survey of the hospital’s staff). In an evaluation study, the single-case might be a single public program that nevertheless involves large numbers of funded projects—which would then be the embedded subunits (see Appendix B for more details). In either situation, these embedded subunits can be selected through sampling or cluster techniques (McClintock, 1985). No matter how the subunits are selected, the resulting design would be called an embedded case study design (see Figure 2.4, Type 2).

Box 10 An Embedded, Single-Case Design

Union Democracy (1956) is a highly regarded case study by three distinguished academicians—Seymour Martin Lipset, Martin Trow, and James Coleman. The case study is about the inside politics within a single, large, but complex entity, the International Typographical Union. The case study had several subunits of analysis. The main unit was the organization as a whole (the “case”), and the smallest unit was the individual member. In addition to these two units, the case study also collected data about several intermediary units (in ascending order): the leaders among the individuals; the “shops” to which specific groups of members belonged; and the “locals,” or union chapters. Different data came from different sources of evidence, including member surveys, leader interviews, shop records, voting histories of the locals, and union archives.

As an important caveat, however, note that the embedded subunits need to be within (or part of) the original single-case. A mistake would be to consider other cases, similar to the original single-case, as if they were the embedded subunits in a single-case study. In that situation, all the cases in fact would rightfully be considered part of a multiple-case design, receiving equal empirical treatment (see upcoming discussion of multiple-case designs), compared with the data collection differences between a case and its subunits in a truly embedded, single-case design.

In contrast to the embedded case study design, if a single-case study only examined the global nature of an organization or of a program, a holistic design would have been used (see Figure 2.4, Type 1). The embedded and holistic designs both have their strengths and weaknesses. The holistic design is advantageous when no logical subunits can be identified or when the relevant theory underlying the case study is mainly of a holistic nature. Potential problems arise, however, when a global approach is too holistic (e.g., studying a “good” organization), allowing a researcher to avoid operationalizing the relevant data. Thus, a typical problem with the holistic design is that the entire case study may be conducted at an unduly abstract level, lacking sufficiently clear measures.

A further problem with the holistic design is that the entire nature of the case study may shift, unbeknownst to the researcher, during the course of the study. The initial study questions may have reflected one orientation, but as the data collection proceeds, the original case study unwittingly assumes a different orientation, with the evidence gradually addressing different research questions (e.g., what started as a study of the “good” organization shifts to being a study of the “promising” organization).

Although some people have claimed such flexibility to be a strength of case study research, in fact the largest criticism of case studies arises when this type of shift occurs unknowingly (see Yin, Bateman, & Moore, 1985). Because of this problem, you need to avoid such unsuspected slippage. If the relevant research questions really do change in a desirable way, as in producing a case study with different insights and new discoveries, you need to recognize the shift openly (see the discussion under “Staying Adaptive” in Chapter 3). Having acknowledged the shift, you should try to start over again with a new research design and a fair data collection plan.

One way to increase the awareness of such slippage is to have a set of subunits. Thus, an embedded case study design can serve as an important device for maintaining a case study’s focus. An embedded design, however, also has its pitfalls. A major one occurs when the case study focuses only on the subunit level and fails to return to the larger unit of analysis, or the original “case.” For instance, an evaluation of an education program consisting of multiple school projects may include the projects’ characteristics as subunits of analysis. The project-level data may even be highly quantitative if there are many projects. However, the original evaluation becomes a school project study (i.e., either a multiple-case study of different projects or even a survey study of the projects) if little investigating is done at the level of the original program, such as completing an in-depth inquiry about its goals, implementation, and outcomes. A likely result, differing entirely from the intent of the original case study about an education program, would be migration to a study of school projects, with some scanty information about the program serving as the background information in the migrated study.

Similarly, a study of organizational climate may involve individual employees as subunits of study. However, if the resulting findings only draw upon the aggregated employee data, the study may in fact migrate and become an employee but not an organizational study. In both examples (an embedded case study of either an education program or of organizational climate), what has happened is that the original case—that is, the original phenomenon of interest (a program or an organization)—has become the context for and not the target of the study.

Summary.

Single-case studies are a common design for doing case study research, and two variants have been described: those using holistic designs and those using embedded units of analysis. Overall, the single-case design is eminently justifiable under certain conditions—where the case represents (a) a critical test of existing theory, (b) an extreme or unusual circumstance, or (c) a common case, or where the case serves a (d) revelatory or (e) longitudinal purpose.

A major step in designing and conducting a single-case study is defining the case itself. An operational definition is needed, and some caution must be exercised—before a total commitment to the whole case study is made—to ensure that the case to be studied is in fact relevant to the original issues and questions of interest.

Subunits of analyses may be incorporated within the single-case study, thereby creating a more complex (or embedded) design. The subunits can often add significant opportunities for extensive analysis, enhancing the insights into the single-case. However, if too much attention is given to these subunits, and if the larger, holistic aspects of the original case begin to be ignored, the case study itself will have shifted its orientation and changed its nature. If the shift is justifiable, you need to address it explicitly and indicate its relationship to the originally intended inquiry.

What Are the Potential Multiple-Case Study Designs (Types 3 and 4)?

The same case study may contain more than a single-case. When this occurs, the case study has used a multiple-case study design, and such designs have increased in frequency in recent years. A common example is a case study of a small group of public versus private hospitals. Each hospital would be the subject of its own fieldwork, and the multiple-case study would first cover each hospital as a single-case study before arriving at findings and conclusions across the individual case studies.

Multiple- versus single-case designs.

In some fields, multiple-case studies have been considered a different methodology from single-case studies. For example, both anthropology and political science have developed one set of rationales for doing single-case studies and a second set for doing what have been considered “comparative” (or multiple-case) studies (see Eckstein, 1975; Lijphart, 1975).

This book, however, considers single- and multiple-case study designs to be variants within the same methodological framework. No broad distinction is made between the so-called classic (i.e., single) case study and multiple-case studies. The choice is considered one of research design, with both being included as a part of case study research.

Multiple-case study designs have distinct advantages and disadvantages in comparison with single-case study designs. The evidence from multiple cases is often considered more compelling, and the overall multiple-case study is therefore regarded as being more robust (Herriott & Firestone, 1983). At the same time, the rationale for single-case designs cannot usually be satisfied by the multiple cases. By definition, the unusual or extreme case, the critical case, and the revelatory case all are likely to involve only single-case studies. Moreover, the conduct of a multiple-case study can require extensive resources and time beyond the means of a single student or independent research investigator. Therefore, the decision to undertake a multiple-case study cannot be taken lightly.

Selecting the multiple cases also raises a new set of questions. Here, a major insight is to consider multiple-case studies as one would consider multiple experiments—that is, to follow a “replication” design. This is far different from the misleading analogy that incorrectly considers the multiple cases to be similar to the multiple respondents in a survey (or to the multiple subjects within an experiment)—that is, to follow a “sampling” design. The methodological differences between these two views are revealed by the different rationales underlying the replication as opposed to sampling designs.

Replication, not sampling logic, for multiple-case studies.

The replication logic is directly analogous to that used in multiple experiments (see Barlow, Nock, & Hersen, 2008). For example, upon uncovering a significant finding from a single experiment, an ensuing and pressing priority would be to replicate this finding by conducting a second, third, and even more experiments. Some of the replications might attempt to duplicate the exact conditions of the original experiment. Other replications might alter one or two experimental conditions considered challenges to the original finding, to see whether the finding can still be duplicated. With both kinds of replications, the original finding would be strengthened.

The design of multiple-case studies follows an analogous logic. Each case must be carefully selected so that the individual case studies either (a) predict similar results (a literal replication) or (b) predict contrasting results but for anticipatable reasons (a theoretical replication). The ability to conduct 6 or 10 individual 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 case studies (2 or 3) might aim at being literal replications, whereas a few other case studies (4 to 6) might be designed to pursue two different patterns of theoretical replications. If all the individual case studies turn out as predicted, these 6 to 10 cases, in the aggregate, would have provided compelling support for the initial set of propositions pertaining to the overall multiple-case study.6 If the individual case studies are in some way contradictory, the initial propositions must be revised and retested with another set of case studies. Again, this logic is similar to the way researchers deal with conflicting experimental findings.

The logic underlying these replication procedures also should reflect some theoretical interest, not just a prediction that two cases should simply be similar or different (e.g., in a health care setting, see Dopson, Ferlie, Fitzgerald, & Locock, 2009). As another example, consider the problem of advice-giving to city governments, on the part of external expert groups. The typical experience is for an expert group to conduct some research and then to present its advice in a report to a city agency. However, the common outcome is for such reports to receive little attention, much less to lead to any appropriate action. BOX 11 describes how a multiple-case study addressed this issue.

Box 11 A Multiple-Case, Replication Design

Peter Szanton’s (1981) book, Not Well Advised, reviewed the experiences of numerous attempts by university and nonuniversity research groups to advise city officials. The book is an excellent example of a multiple-case replication design.

Szanton starts with eight case studies, showing how different university groups produced credible research but nevertheless all failed to help city governments. The eight cases are sufficient “replications” to convince the reader of a general phenomenon—the typical supposition being that the differences between the academic and public policy cultures create an insurmountable communication barrier. Szanton then provides five more case studies, in which nonuniversity groups also failed, concluding that failure was therefore not necessarily inherent in the academic enterprise. Yet a third group of cases shows how university groups have, in contrast, successfully and repeatedly advised sectors other than city government, such as businesses and engineering firms. A final set of three cases shows that those few groups able to help city government were concerned with implementation and not just with submitting a research report containing new research-based ideas. The findings from all these case studies led to Szanton’s major conclusion, which is that city governments may have peculiar needs in receiving advice but then also putting it into practice.

Within each of the four groups of case studies, Szanton has illustrated the principle of literal replication. Across the four groups, he has illustrated theoretical replication. This potent case study design can and should be applied to many other topics.

The replication logic, whether applied to experiments or to case studies, must be distinguished from the sampling logic commonly used in surveys. The sampling logic requires an operational estimation of the entire universe or pool of potential respondents and then a statistical procedure for selecting a specific subset of respondents to be surveyed. The resulting data from the sample that is actually surveyed are assumed to reflect the entire universe or pool, with inferential statistics used to establish the confidence intervals for presuming the accuracy of this representation. The entire procedure is commonly used when a researcher wishes to determine the prevalence or frequency of a particular phenomenon.

Any application of this sampling logic to case study research would be misplaced. First, case studies are not the best method for assessing the prevalence of phenomena. Second, each individual case study would have to cover both the phenomenon of interest and its context, yielding a large number of potentially relevant variables (see Appendix B for a more detailed discussion). In turn, this would require an impossibly large sample of cases—too large to allow more than a superficial examination of any given case.

Third, if a sampling logic had to be applied to all types of research, many important topics could not be empirically investigated, such as the following problem: Your investigation deals with the role of the presidency of the United States, and you are interested in doing a multiple-case study of (a few) presidents to test your theory about presidential leadership. However, the complexity of your topic means that your choice of a small number of cases could not adequately represent all the 45 presidents since the beginning of the Republic. Critics using a sampling logic might therefore deny the acceptability of your study. In contrast, if you use a replication logic, a study is eminently feasible.

The replication approach to multiple-case studies is illustrated in Figure 2.5. The figure indicates that the initial step in designing the study should preferably consist of theory development and then shows that case selection and the definition of specific measures are important steps in the design and data collection process. Each individual case becomes the subject of a whole case study, in which convergent evidence is sought regarding the findings and conclusions for the study; each case study’s conclusions are then considered to be the information needing replication by the other individual case studies. Both the individual case studies and the multiple-case results can and should be the focus of a summary report. For each individual case study, the report should indicate how and why a particular proposition was demonstrated (or not demonstrated). Across case studies, the report should indicate the extent of the replication logic and why certain case studies were predicted to have certain results, whereas other case studies, if any, were predicted to have contrasting results.

An important part of Figure 2.5 is the dashed-line feedback loop. The loop represents the situation where important discovery occurs during the study of one of the individual cases (e.g., one of the cases deviated unexpectedly from the original design). Such a discovery may require you to reconsider one or more of the multiple-case study’s original theoretical propositions. At this point, “redesign” should take place before proceeding further. Such redesign might involve the selection of alternative cases or changes in the case study protocol (see Chapter 3). Without such redesign, you risk being accused of distorting or ignoring the discovery, just to accommodate the original design. This condition leads quickly to a further accusation—that you have been selective in reporting your data, to suit your preconceived ideas (i.e., the original theoretical propositions).

Overall, Figure 2.5 depicts a different logic from that of a sampling design. The logic as well as its contrast with a sampling design may be difficult to follow and is worth extensive discussion with colleagues before proceeding with any multiple-case study.

When using a multiple-case design, a further question you will encounter has to do with the number of cases deemed necessary or sufficient for your study. However, because a sampling logic should not be used, the typical criteria regarding the use of a power analysis to determine the desired sample size (e.g., Lipsey, 1990) also are irrelevant. Instead, you should think of the number of case replications—both literal and theoretical—that you need or would like to have in your study.

Figure 2.5 Multiple-Case Study Procedure

Source: Cosmos Corporation.

Your judgment will be a discretionary, not formulaic, one. Such discretionary judgments are not peculiar to case study research. They also occur in non–case study research, such as in setting the criterion for defining a “significant effect” in experiments. Thus, designating a “p < .05” or “p < .01” likelihood of detection, to set the confidence level for accepting or rejecting the null hypothesis, is not based on any formula but is a matter of a discretionary, judgmental choice. Note that when patient safety and well-being are at stake, as in a clinical trial, investigators will usually not settle for a “p < .01” significance level but may choose to attain a “p < .0001” or even more stringent level.

Analogously, designating the number of replications depends upon the certainty you want to have about your multiple-case results. For example, you may want to settle for two or three literal replications when your theory is straightforward and the issue at hand does not demand an excessive degree of certainty. However, if your theory is subtle or if you want a higher degree of certainty, you may press for five, six, or more replications.

In deciding upon the number of replications, an important consideration also is related to your sense of the strength and importance of rival explanations. The stronger the rivals, the more additional cases you might want, each case showing a different but predicted result when some rival explanation had been taken into account. For example, your original hypothesis might be that summer reading programs improve students’ reading scores, and you already might have shown this result through two to three programs whose case studies served as literal replications. A rival explanation might be that parents also work more closely with their children during the summer and that this circumstance can account for the improved reading scores. You would then find another case, with parent participation but no summer reading program, and in this theoretical replication, you would predict that the scores would not improve. Having two such theoretical replications would provide even greater support for your findings.

Rationale for multiple-case designs.

In short, the rationale for multiple-case designs derives directly from your understanding of literal and theoretical replications (refer again to BOX 11). The simplest multiple-case design would be the selection of two or more cases that are believed to be literal replications, such as a set of case studies with exemplary outcomes in relation to some evaluation question, such as “how and why a particular intervention has been implemented smoothly.” Selecting such cases requires prior knowledge of the outcomes, with the multiple-case inquiry focusing on how and why the exemplary outcomes might have occurred and hoping for literal (or direct) replications of these conditions from case to case.7

More complicated multiple-case designs would likely result from the number and types of theoretical replications you might want to cover. For example, investigators have used a “two-tail” design in which cases from both extremes (of some important theoretical condition, such as extremely good and extremely bad outcomes) have been deliberately chosen. Multiple-case rationales also can derive from the prior hypothesizing of different types of conditions and the desire to have subgroups of cases covering each type. These and other similar designs are more complicated because the study should still have at least two individual cases within each of the subgroups, so that the theoretical replications across subgroups are complemented by literal replications within each subgroup.

Multiple-case studies: Holistic or embedded.

The fact that a design calls for multiple-case studies does not eliminate the variation identified earlier with single-case studies: Each individual case study may still be holistic or contain embedded subunits. In other words, a multiple-case study may consist of multiple holistic cases (see Figure 2.4, Type 3) or of multiple embedded cases (see Figure 2.4, Type 4). The difference between these two variants depends upon the type of phenomenon being studied and your research questions. In an embedded multiple-case design, a study even may call for the conduct of a survey at each case study site.

For instance, suppose a study is concerned with the impact of the training curriculum adopted by different nursing schools. Each nursing school may be the topic of a case study, with the theoretical framework dictating that nine such schools be included as case studies, three to replicate a direct result (literal replication) and six others to deal with contrasting conditions (theoretical replications).

For all nine schools, an embedded design is used because surveys of the students (or, alternatively, examination of students’ archival records) are needed to address research questions about the performance of the schools. However, the results of each survey will not be pooled across schools. Rather, the survey results will be part of the findings for the individual case study of each nursing school. The results may be highly quantitative and even involve statistical tests, focusing on the attitudes and behavior of individual students, and the data will be used along with information about the school to interpret the success and operations with the training curriculum at that particular school. If, in contrast, the survey data are pooled across schools, a replication design is no longer being used. In fact, the study has now become a mixed-methods study (see discussion of mixed-methods designs at the end of this chapter), the collective survey providing one set of evidence and the nine case studies providing a separate set. Such a turn of events would create a pressing need to discard the original multiple-case design. The newly designed mixed-methods study would require a complete redefinition of the main unit of analysis and entail extensive revisions to the original theories and propositions of interest.

Summary.

This section has dealt with situations in which the same investigation calls for multiple cases and their ensuing case studies. These types of designs are becoming more prevalent, but they are more expensive and time-consuming to conduct.

Any use of multiple-case designs should follow a replication, not a sampling, logic, and a researcher must choose each case carefully. The cases should serve in a manner similar to multiple experiments, with similar results (a literal replication) or contrasting results (a theoretical replication) predicted explicitly at the outset of the investigation.

The individual cases within a multiple-case study design may be either holistic or embedded. When an embedded design is used, each individual case study may in fact include the collection and analysis of quantitative data, including the use of surveys within each case study.

Exercise 2.4 Defining a Case Study Research Design

Select one of the case studies described in the BOXES of this book, reviewing the entire case study (not just the material in the BOX). Describe the research design of this case study. How did it justify the relevant evidence to be sought, given the main research questions to be answered? What methods were used to identify the findings, based on the evidence? Is the design a single- or multiple-case design? Is it holistic or does it have embedded units of analysis?

Modest Advice In Selecting Case Study Designs

Now that you know how to define case study designs and are prepared to carry out design work, you might want to consider three pieces of advice.

Single- or Multiple-Case Designs?

The first word of advice is that, although all designs can lead to successful case studies, when you have the choice (and resources), multiple-case designs may be preferred over single-case designs. If you can do even a “two-case” case study, your chances of doing a good case study will be better than using a single-case design. Single-case designs are vulnerable if only because you will have put “all your eggs in one basket.” More important, the analytic benefits from having two (or more) cases may be substantial.

To begin with, even with two cases, you have the possibility of direct replication. Analytic conclusions independently arising from two cases, as with two experiments, will be more powerful than those coming from a single-case (or single experiment) alone. Alternatively, you may have deliberately selected your two cases because they offered contrasting situations, and you were not seeking a direct replication. In this design, if the subsequent findings support the hypothesized contrast, the results represent a strong start toward theoretical replication—again strengthening your findings compared with those from a single-case study alone (e.g., Eilbert & Lafronza, 2005; Hanna, 2005; also see BOX 12).

Box 12 Two, “Two-Case” Case Studies

12A. Contrasting Cases for Community Building

Chaskin (2001) used two case studies to illustrate contrasting strategies for capacity building at the neighborhood level. The author’s overall conceptual framework, which was the main topic of inquiry, claimed that there could be two approaches to building community capacity—using a collaborative organization to (a) reinforce existing networks of community organizations or (b) initiate a new organization in the neighborhood. After thoroughly airing the framework on theoretical grounds, the author presents the two case studies, showing the viability of each approach.

12B. Contrasting Strategies for Educational Accountability

In a directly complementary manner, Elmore, Abelmann, and Fuhrman (1997) chose two case studies to illustrate contrasting strategies for designing and implementing educational accountability (i.e., holding schools accountable for the academic performance of their students). One case represented a lower cost, basic version of an accountability system. The other represented a higher cost, more complex version.

In general, criticisms about single-case studies usually reflect fears about the uniqueness or artifactual conditions surrounding the case (e.g., special access to a key informant). As a result, the criticisms may turn into skepticism about your ability to do empirical work beyond having done a single-case study. Having two cases can begin to blunt such criticism and skepticism. Having more than two cases will produce an even stronger effect. In the face of these benefits, having at least two cases should be your goal. If you do use a single-case design, you should be prepared to make an extremely strong argument in justifying your choice for the case.

Exercise 2.5 Establishing the Rationale for a Multiple-Case Study

Develop some preliminary ideas about a “case” for your case study. Alternatively, focus on one of the single-case studies presented in the BOXES in this book. In either situation, now think of a companion “case” that might augment the single-case. In what ways might the companion case’s findings supplement those of the first case? Could the data from the second case fill a gap left by the first case or respond better to some obvious shortcoming or criticism of the first case? Would the two cases together comprise a stronger case study? Could yet a third case make the findings even more compelling?

Closed or Adaptive Designs?

Another word of advice is that, despite this chapter’s details about design choices, you should not think that a case study’s design cannot be modified by new information or discovery during data collection. Such revelations can be enormously important, leading to your altering or modifying your original research design.

As examples, in a single-case study, what was thought to be a critical or unusual case might have turned out not to be so, just after initial data collection had started; ditto a multiple-case study, where what was thought to be parallel cases for literal replication turn out not to be so. With these revelations, you have every right to conclude that your initial design needs to be modified. However, you should undertake any alterations only given a serious caution. The caution is to understand precisely the nature of the alteration: Are you merely going to select different cases, or are you going to change your original theoretical propositions and objectives? The point is that the needed adaptiveness should not lessen the rigor with which case study procedures are followed.

Mixed-Methods Designs: Mixing Case Studies With Other Methods?

Researchers have given increasing attention to mixed-methods research—a “class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts or language into a single study” (Johnson & Onwuegbuzie, 2004, p. 17, emphasis added). Avid interest in mixed-methods research over the past decade or two has led to a large and still growing literature, as well as the formation of new and active professional groups in many social science fields (e.g., Hesse-Biber & Johnson, 2015).

Confinement to a single study forces the methods being mixed into an integrated mode. The mode differs from the conventional situation whereby different methods are used in separate studies that may later be synthesized. In effect, the single study forces the methods to share the same research questions, to collect complementary data, and to conduct counterpart analyses (e.g., Yin, 2006b).

As such, mixed-methods research can permit researchers to address more complicated research questions and collect a richer and stronger array of evidence than can be accomplished by any single method alone. Depending upon the nature of your research questions and your ability to use different methods, mixed-methods research opens a class of research designs that deserve your attention (e.g., Yin, 2015b).

The earlier discussion of embedded case study designs in fact points to the fact that certain kinds of case studies already may represent a form of mixed-methods research: Embedded case studies may rely on holistic data collection strategies for studying the main case and then call upon surveys or other quantitative techniques to collect data about the embedded subunit(s) of analysis. In this situation, other research methods are embedded within case study research.

The opposite relationship also can occur. Your case study may be part of a larger, mixed-methods study. The main investigation may rely on a survey or other quantitative techniques, and your case study may help to investigate the conditions within one of the entities being surveyed.

The contrasting relationships (survey within case or case within survey) are illustrated in Figure 2.6 (also see Chapter 6, pp. 235–236; in addition, Appendix Bdiscusses these two arrangements in relation to evaluation studies).

Figure 2.6 Mixed Methods: Two Nested Arrangements

At the same time, mixed-methods research need not include the use of case study research at all. For instance, a clinical study could be combined with historical work that embraces the quantitative analysis of archival records, such as newspapers and other file material. Going even further, two scholars claim that mixed-methods research need not be limited to combinations of quantitative and qualitative methods but could employ a mix of two quantitative methods: a survey to describe certain conditions, complemented by an experiment that tries to manipulate some of those conditions (e.g., Berends & Garet, 2002).

By definition, studies using mixed-methods research are more difficult to execute than studies limited to single methods. However, mixed-methods research can enable you to address broader or more complicated research questions than case studies alone. As a result, mixing case study research with other methods should be among the possibilities meriting your consideration.

Notes to Chapter 2

1. Figure 2.2 focuses only on the formal research design process, not on data collection activities. For all three types of research (survey, case study, and experiment), data collection techniques might be depicted as the level below Level One in the figure. For example, for case study research, this might include using multiple sources of evidence, as described further in Chapter 4. Similar data collection techniques can be described for surveys or experiments—for example, questionnaire design for surveys or stimulus presentation strategies for experiments.

2. Whether experiments also need to address statistical generalizations has been the topic of sharp debate in psychology. According to the statistical argument, the human subjects in an experiment should be considered a population sample, with the experimental results therefore limited to the universe of the same population. The debate began over the excessive use of college sophomores in behavioral research (e.g., Cooper, McCord, & Socha, 2011; Gordon, Slade, & Schmitt, 1986; McNemar, 1946; Peterson, 2001; Sears, 1986) and has since extended to an awareness that the subjects in most behavioral research have been White males from industrialized countries (Henrich, Heine, & Norenzayan, 2010), even though the experimental findings are intended to apply as “the norm for all human beings” (Prescott, 2002, p. 38).

3. Mary Kennedy (1979) may have been the first to call attention to the analogous process in the field of law: Interpretations made from a single legal case may be used as precedents (i.e., generalizations) for future cases. Indeed, the body of legal knowledge appears to grow in this manner. However, the interpretations (i.e., generalizations) are about the ideas or principles established by the case, not about the case and its potentially idiosyncratic demographic features itself. Obviously, whether a case would be accepted as precedent-setting then becomes the subject of legal claims and debate.

4. One of the anonymous reviewers of the third edition of this book pointed out that construct validity also has to do with whether interviewees understand what is being asked of them.

5. For other suggested guidelines for reviewers of case study proposals or manuscripts, see Yin (1999).

6. Although this modestly large array of cases may at first appear difficult to garner, Small (2009) calls attention to the situation in which a survey study might originally have planned to conduct open-ended interviews of 20 to 30 people, only to find later that—from a survey standpoint—the sample size was too small. However, he points out that if the same number of interviewees happened to suit a multiple-case study replication design, such a number would be more than adequate in arriving at some important findings and conclusions—given appropriate adjustments to the research design and data collection procedures.

7. Strictly quantitative studies that select cases with known outcomes follow the same design and have alternatively been called “case-control,” “retrospective,” or “case referent” studies (see Rosenbaum, 2002, p. 7).

Body Exercise icon by Gan Khoon Lay (https://thenounproject.com/icon/637461/) licensed under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/us/) is used in the Exercise boxes throughout the chapter.

Application #1: An Exploratory Case Study: How New Organizational Practices Become Routinized

Inappropriate impressions of case study research can result from the overly informal use of exploratory case studies. However, even they should follow a methodic procedure. Application 1  shows how an exploratory case study was conducted in such a manner, leading to the development of a conceptual framework and data collection procedures for a later case study.

Every organization engages in a broad variety of practices. They cover the full range of the organization’s activities, ranging from (a) hiring and other human resource procedures, to (b) the methods for producing its products and services, and even to (c) routine logistical arrangements. In public service organizations, such as schools, police departments, and fire departments, a notable challenge has been to put new technologies, such as computers or other specialized equipment, into practice.

At first, the public services adopt these new practices as “innovations.” The organization may later stop using some of the innovations, but other innovations become a part of the organization’s core fabric. At this later stage, the practices are no longer innovations but might be considered as having become “routinized” or “sustained.” However, remarkably little is known about how a new practice or innovation, once adopted by an organization, eventually becomes a routine practice. In short, how does routinization occur?

Equally challenging is the problem of how to study such a process. It may be a gradual transition that takes place over a period of years, and the signs of becoming routinized or achieving routinization may not be readily recognized. As a result, how to study the transitions can remain difficult. An exploratory study may be one way of figuring out how to do the desired study.

Application 1 involved such an exploratory effort.1. One purpose was to identify the specific practices that were to be covered by the later study. Another purpose was to operationalize the actual organizational changes that mark a routinization process. The organizational changes were to go beyond an alternative approach, commonly found in the literature of that time, on people’s perceptions of whether routinization has occurred or not. However, these inquiries about perceptions did not try to identify whether any actual organizational changes had occurred. Finally, the exploratory study needed to specify the data collection procedures to be used in the later study. In short, the goal of the exploratory study was to develop the conceptual framework for the final study.

1. This application, with minor edits, originally appeared as part of Chapter 3 in Yin (2012a), Applications of Case Study Research.

A field-based protocol for the exploratory study.

In the exploratory study, the study team spent an extended time collecting data from seven cases (none of which were used in the final study). A key procedure was the use of a special pilot protocol that elaborated alternative features about the life cycle of an innovation. The study team understood that adoption-implementation-routinization potentially constituted the entire life cycle but had not developed specific hypotheses or measures of the organizational changes, to facilitate empirical study. In this sense, the protocol fostered the development of operational concepts, not just methodological issues.

The study team modified this pilot protocol after every pilot site study was completed. The iterative process forced the team to address several questions repeatedly: Had sufficient information been learned that an existing exploratory question could now be dropped? Had new problems emerged, requiring the framing of a new question? Did an existing question need to be modified? The team also deliberately explored a variety of innovations, ultimately leading to the selection of the final six technologies (two in each of three urban services, which included the use of breathalyzers by law enforcement agencies, computer-assisted instruction by schools, and mobile intensive care units by fire departments). More important, the pilot study helped refine the conceptual framework for the final study. Ultimately, the research questions and instrumentation for studying the routinization process emerged.

Illustrative results and key lessons.

The exploratory study led to identifying the feasibility of studying the six technologies. A second important result of the exploratory study was the development of operational measures for the hypothesized routinization process. Measurable organizational events related to each of the practices at any given site became identified as “cycles” or “passages,” as illustrated in Exhibit App. 1.1.

A third important result was the formation of tentative hypotheses about an innovation’s life history and the sequence of these cycles and passages—as some were hypothesized to occur earlier in the routinization process and others later. Based on the actual findings from the later study—which covered case studies of 12 innovative practices and a telephone survey of 90 practices at other sites—Exhibit App. 1.2 shows the way that the life history of an innovation can be depicted. This exhibit should be read in the following manner: (1) The two axes suggest that an innovation can move from left to right (as time passes) and from bottom to top (as it becomes routinized); (2) moving in both directions at the same time produces a diagonal direction, reflecting an innovation passing through an “improvisation stage” (bottom left of the exhibit), to an “expansion stage” (middle), and finally to a “disappearance stage” (top right), with the attainment of the latter two stages defined by the passages and cycles listed in each box; (3) the diagonal movement is spurred by the initiatives and conditions listed next to the vertical arrows pointing to each of the three stages; and (4) during this entire process, a preexisting practice, now being displaced by the innovative one, declines in the opposite diagonal direction.

For Class Discussion or Written Assignment

Using Specialized Terminologies in Case Study Protocols

The six practices in Application 1 covered three urban services that differed strongly in their organizational cultures, procedures, personnel—and terminologies. Although the case study dwelled on the same routinization processes in each service, the diversity of the services called for different data collection protocols. This was especially true in conducting the telephone survey, where the three services’ terminology and procedures were sufficiently different that a generic set of questions could not be used. This realization created much unanticipated work for the study team; in fact, the team resisted the finding throughout the exploratory study because of the known consequences in workload. However, no single questionnaire would work.

Examine the protocols that you might have developed in your own previous or ongoing studies. Highlight key words or terms that appear to be specialized in some sense that might confuse people unfamiliar with your topic of study. Is your protocol sufficiently cast in terms of “plain English,” or do the specialized terms appear with some frequency? If frequent, what would be the trade-offs if you replaced them with more generic terms? Would your fieldwork now suffer more?

Exhibit App. 1.1 Organizational Passages and Cycles Related to Routinization

Exhibit App. 1.2 Complete Life History of a Local Service Innovation

Source: Yin (1981c).

Application #2: Defining the “Case” in a Case Study: Linking Job Training and Economic Development Initiatives at the Local Level

How to define the case(s) to be studied in a case study can require some careful thinking. Sometimes, the candidate cases are known beforehand. In many situations, however, you may have to struggle conceptually to define the cases.  Application 2  shows how the procedure for identifying the actual candidate cases took place for one case study.

Application 2 called for a case study that would investigate how local initiatives might explicitly coordinate job training (for the hard-to-employ) with economic development objectives.1This kind of initiative offered an attractive dual benefit.

1. A version of this application originally appeared as part of Chapter 3 in Yin (2012a), Applications of Case Study Research.

For the training participants in such an initiative, the potential advantage is that placement is more likely to occur in jobs in economically growing industries and occupations, resulting in more enduring job placements. Conversely, for employers in growing lines of business, such programs might produce a larger pool of appropriately trained employees, thereby making recruitment easier. In contrast, when job training or economic development efforts occur in isolation of each other, neither of the preceding benefits is likely to be realized: Job training efforts alone can easily lead to placements in low-growth and transient jobs for the hard-to-employ; economic development efforts alone can focus too heavily on employers’ facilities and capital needs, overlooking their potential employment needs.

The purpose of the case study was to examine the coordinated type of initiative, to determine how the desired combination of outcomes is produced. However, although coordination was straightforward in concept, it was difficult to define operationally. What kinds of cases would be relevant?

An initial requirement was to define the “case.” The study team readily understood that the case would not necessarily be a single organization or initiative. To study coordination, a joint organizational effort (between two or more organizations) or joint initiatives (job training and economic development) would likely be the “case.” The identification of such joint efforts, therefore, became the first task, before any case selection was possible.

Optional choices.

A troubling characteristic involved the optional ways of organizing such joint efforts. At the local level, the efforts can be represented by at least three different options: a joint project, a joint program, or an interorganizational arrangement. Illustrative joint projects include a community college offering a course focusing on the skills needed for the entry-level jobs of specific local firms in a high-growth industry, in collaboration with those firms. The study team found numerous examples of these joint projects in the published literature. Joint programs included statewide training programs for dislocated workers. In general, these programmatic efforts were more sustained than single projects, with many states undertaking such initiatives. In contrast, interorganizational arrangements did not necessarily focus on a single project or program. Rather, the qualifying criterion was that two or more organizations had joined in some arrangement—by forming a joint venture, initiating a consortium, or using interagency agreements among existing organizations—to coordinate training and economic development activities.

With regard to these three options, both theory and policy relevance played the critical role in the study team’s final choice. First, the existing literature indicated that the three options were different—cases of one were not to be confused with cases of the others. For instance, programs call for more significant outlays than projects, and interorganizational arrangements may be the most troublesome but can then result in multiple programs and projects.

Second, the literature had given less attention to interorganizational arrangements, even though these had more promise of local capacity building in the long run. Thus, a local area with a workable interorganizational arrangement may sustain many initiatives and may not be as vulnerable to the sporadic nature of single projects or programs.

Third, the study team was interested in doing a case study that would advance knowledge about interorganizational arrangements. Over the years, increasing attention was being devoted to “public-private partnerships,” not just in employment and economic development but also in many services for specific population groups (e.g., in housing, education, social services, health care, mental health care, and community development). Yet, the available literature was shallow with regard to the workings of interorganizational arrangements—how they are formed, what makes them thrive, and how to sustain them.

Finally, a study of interorganizational arrangements also could cover component programs or projects—within the arrangements—as embedded units of analysis. In this way, the study could still touch on the other two options. For all these reasons, the study team selected the interorganizational arrangement as the definition of the case to be studied.

Screening for eligible cases.

At the same time, this definition created a challenge in identifying and screening candidate cases. Interorganizational arrangements do not announce themselves in any prominent way, leading to a troublesome risk: What might at first appear to be such an arrangement might later turn out to be a complex but nevertheless single organization and not a partnership of multiple organizations. Some extended effort is needed, prior to doing the case study, to confirm the desired disposition of each “case.” Yet, if not properly controlled, the screening of any given candidate can become too extensive. The amount of screening data would begin to resemble the amount used in the actual case study—which would be far too much (you cannot do a case study of every candidate case). Nevertheless, proper screening requires the collection and analysis of actual empirical data at this preliminary stage.

The study team began its screening process by contacting numerous individuals in the field and consulting available reports and literature. These sources were used to suggest candidates who fit the selection criteria, resulting in 62 nominees. The study team then attempted to contact these nominees, both in writing and by phone. The team obtained information on 47 of them.

The screening information included the responses to a structured interview of about 45 minutes, using a formal instrument. Each of the candidate arrangements also was encouraged to submit written materials and reports about its operations. The final review determined that 22 of the 47 candidates were eligible for further consideration. From these 22, the study team then selected a final group of 6, based on the thoroughness of the documentation and accessibility of the site.

For Class Discussion or Written Assignment

Defining and Bounding the “Case” in Doing Case Studies

The “cases” in a case study can appear to be more straightforward (e.g., individual people, groups of people, organizations, and neighborhoods) or more fluid (e.g., decisions, processes, social relationships, and sequences of events, such as political campaigns). Enumerate some of the cases that have appeared in an array of case studies that appeared in the BOXES in this book. Discuss the possibility that cases are not readily bounded but may have blurry definitions. For instance, even studying the relationship between two people as a “case” might involve defining how different time periods and social situations will be recognized as falling either within the case or outside of it. Given the potential complexities, do you find that strong differences persist between the type of cases that initially appear straightforward and those that appear fluid?

Application #3: How “Discovery” Can Occur in the Field: Social Stratification in a Midsized Community

In doing case study research, the initial fieldwork may challenge some original assumption about the study design. Such an occurrence needs to be reviewed carefully, because the challenge may lead to some important revelation, benefiting the case study.  Application 3  discusses the field evidence that led a case study team to revisit its original thinking about social stratification, and their work has become a now-classic case study.

Nearly every social group—whether a family, a community, or an organization—has a social structure, however organized or disorganized. The components of this social structure, such as family members, community groups, or organizational units, have arrayed themselves in some informal order. In a pluralistic arrangement, all members have equal statuses. In a hierarchical arrangement, some of the members assume more superordinate positions and other members remain in more subordinate positions. These arrangements are but two of many possible arrangements and can be a way of characterizing a group’s social structure. In studying communities, research on social structure remains of great interest to this day.

Application 3 is based on a study of the social structure of Yankee City. The original study appeared as a five-volume series in the mid-20th century and represents one of the best-known sociological case studies.1 The community was situated at the mouth of a large river in New England, just north of Boston. At the time, the community had a population of 17,000. Slightly over 50% of the residents were born in or near Yankee City, 24% were foreign born, and the rest were born elsewhere in the United States. About one fourth of the employable people were in the shoe industry, with other smaller economic activities in silverware manufacturing, the building trades, transport, and electric shops.

1. Warner, W. L., & Lunt, P. S. (1941). The social life of a modern community. New Haven, CT: Yale University Press. This application is the present author’s summary excerpt from the original text, which first appeared as Chapter 4 in Yin (2004), The Case Study Anthology.

When the research on Yankee City began, the research team explicitly hypothesized that the social structure of the community would largely revolve around an economic order. The team believed that such an order represented “the fundamental structure of our society . . . and that the most vital and far-reaching value systems which motivate Americans are to be ultimately traced to an economic order” (Warner & Lunt, 1941, p. 81).

The interviews in the initial fieldwork tended to support this hypothesis. Interviewees considered bankers, large property owners, people with high salaries, and those in professional occupations as being of high status, whereas interviewees considered laborers, ditchdiggers, and low-wage earners as being of low status. However, “other evidences began to accumulate which made it difficult to accept a simple economic hypothesis” (p. 81).

For instance, people with similar professional backgrounds were not always accorded the same status. Some physicians had a higher status than others who were nevertheless recognized as being better physicians, and similar inequalities of status were found among ministers, lawyers, and bankers, as well as in the business and industrial world. Occupation and wealth seemed to contribute greatly to the rank status of an individual, but other conditions also prevailed. Something else was at work, leading the research team to develop a “class” hypothesis: “two or more orders of people who are believed to be, and who are accordingly ranked by the members in the community, in socially superior and inferior positions” (p. 82).

The research team found that people tended to marry within their own class, with the children being born into the same status as their parents. Society appeared to distribute rights and privileges, as well as duties and obligations, unequally among the classes. However, unlike a system of castes, the social structure also set the conditions “for movement up and down the social ladder” (p. 82). Overall, the research team now hypothesized that the social structure of Yankee City was dominated by a class order rather than a strictly economic and occupational one.

For instance, the interviewees did not accord the wealthiest man in the town with the highest status because he and his family, though exhibiting acceptable moral behavior, did not “act right” (p. 82) or “do the right things” (p. 83). Conversely, people could be ranked socially high even though they had little money or modest occupational status because they spent their money in the right manner, possibly also belonging to the preferred associations and clubs.

Following this emerging line of thinking, the research team also “made a valuable discovery” (p. 84): In the interviewees’ expressions of the higher and lower valuations, the team “noticed that certain geographical terms were used not only to locate people in the city’s geographical space but also to evaluate their comparative place in the rank order” (p. 84). In sorting out these references, the team concluded that individuals were being designated in the following manner: “Hill Street was roughly equivalent to upper class, Homeville to at least a good section of the middle class, and Riverbrook to the lowest class” (p. 86).

Interestingly, the team also discovered that the class designations and geographic references only matched in an approximate manner. Not all people living on Hill Street were considered “Hill Streeters,” and many people who were considered by class as “Hill Streeters” lived elsewhere in the city. The same pattern existed for Homeville and Riverbrook.

At the same time, the interviews suggested that, within the three main class designations, there existed higher and lower subdivisions. For instance, the interviewees “made frequent references to people of ‘old family’ and to those of ‘new families’” (p. 86). The team labeled these subdivisions as “upper-upper” and “lower-upper” and eventually came to recognize six such subdivisions within the original three classes. (The notions underlying these subdivisions later became a major contribution to the entire social stratification literature.)

Given such a hypothesized class structure, the research team found that membership in various associations could be used as further evidence in classifying the residents within such a structure. For instance, the interviews suggested that “certain clubs . . . were ranked at such extreme heights by people highly placed in the society that most of the lower classes did not even know of their existence, while middle-class people showed that they regarded them as much too high for their expectations” (p. 87).

The diversity of associations within Yankee City, as well as the high rate of participation by the residents, meant that many people belonged to some association, and the people from different classes appeared to belong to different associations. For instance, people designated as “Hill Streeters” did not belong to occupational associations, but Homevillers did. Homevillers also favored fraternal orders and semi-auxiliaries. When the same resident belonged to two or more associations that tended to cross class lines, the research team did a small amount of further interviewing to help clarify an assignment.

The research team used explicit statements in the interviews (e.g., “she does not belong,” or “they belong to our club”—p. 90), the residential patterns, and the association membership patterns as the groundwork for assigning the Yankee City residents into the six classes. The team wanted to make these assignments because it defined the need to make them a precondition for doing “a complete study” (p. 91). At the same time, the team recognized that there were many borderline cases and that shifts between the classes were constantly occurring.

For Class Discussion or Written Assignment

Letting Fieldwork Findings Challenge Your Thinking

The field-based nature of case study research can create a built-in tension. On one hand, the startup of a case study requires some careful planning. Based on reviewing the literature as well as your own interests, you will need to have some preliminary research questions and even possibly a tentative case study design. On the other hand, once you start collecting data, the information from the field may override if not challenge your original thinking. Under that circumstance, you wouldn’t want to miss important new insights or discoveries, as in Application 3’s switching from a straightforward economic to a social class orientation.

The tension occurs when you are not sure of whether the new information should cause you to revise your original thinking, partly because, if you already have been collecting data from the field, by definition you will be midway through your study. You will want to honor the new insights that may have arisen, but at the same time, you won’t want to overreact by unnecessarily disrupting your research procedures. Discuss whether there are ways of distinguishing big surprises from little ones, so that you can give close attention to the big ones but relegate the little ones to some sort of footnote status. Also discuss whether there is a middle ground, whereby you can continue with your original plans but also let the new leads enhance those plans for a little while—that is, until you can decide whether or not to change your original thinking and formally alter your procedures.