There is a thoughtful review of the informationist literature and detailed descriptions of the institutional context and the process of gaining access to and participating in the new role. However, the motivating question in the abstract does not seem to be fully addressed through analysis from either the reflective perspective of the author as the research participant or consideration of other streams of data from those involved in the informationist experience. All of these publications are well written and useful for their intended audiences, but in general, they are much shorter and much less rich in depth than case studies published in social sciences research.
It may be that the authors have been constrained by word counts or page limits. Problem-focused or question-driven case study research would benefit from the space provided for Original Investigations that employ any type of quantitative or qualitative method of analysis. One of the best examples in the JMLA of an in-depth multiple case study that was authored by a librarian who published the findings from her doctoral dissertation represented all the elements of a case study.
In eight pages, she provided a theoretical basis for the research question, a pilot study, and a multiple case design, including integrated data from interviews and focus groups [ 19 ]. We have distinguished between case reports and case studies primarily to assist librarians who are new to research and critical appraisal of case study methodology to recognize the features that authors use to describe and designate the methodological approaches of their publications.
For researchers who are new to case research methodology and are interested in learning more, Hancock and Algozzine provide a guide [ 20 ]. We hope that JMLA readers appreciate the rigor of well-executed case study research. We also hope that authors feel encouraged to pursue submitting relevant case studies or case reports for future publication.
Designs and Methods (3rd Ed.)
National Center for Biotechnology Information , U. J Med Libr Assoc. Published online Jan 1. Kristine M. Article notes Copyright and License information Disclaimer. Received Oct 1; Accepted Oct 1. Articles in this journal are licensed under a Creative Commons Attribution 4. Abstract The purpose of this editorial is to distinguish between case reports and case studies. Leading authors diverge in their definitions of case study, but a qualitative research text introduces case study as follows: Case study research is defined as a qualitative approach in which the investigator explores a real-life, contemporary bounded system a case or multiple bound systems cases over time, through detailed, in-depth data collection involving multiple sources of information, and reports a case description and case themes.
Open in a separate window. Figure 1. References 1. Akers KG, Amos K. Publishing case studies in health sciences librarianship [editorial] J Med Libr Assoc. Creswell JW. Yin RK. Case study research: design and methods. Research design: qualitative, quantitative and mixed methods approaches. Case study research and applications: design and methods. Stake RE. The art of case study research. Merriam SB. Qualitative research and case study applications in education.
Yazan B. Three approaches to case study methods in education: Yin, Merriam, and Stake. Qual Rep. Yin offers advice on issues common to many researchers and research methods, such as when to start composing the report, how to deal with anonymity concerns, and identifying who should review the draft report and how their input should be incorporated into the text. Yin concludes with hallmarks of good case study research. While these are not new, they are worth repeating. First, case studies should be significant; that is, they should be of general interest and should deal with important issues.
Second, they should be complete. Third, case studies should consider alternative perspectives to avoid presenting only one side of a story. Fourth, they must display sufficient evidence. Finally, case studies should be composed in an engaging manner so as to draw readers in. Case Study Research: Designs and Methods provides a useful and straightforward guide for those considering case study research.
Importance of a Case Study
By including samples of case studies throughout the book, Yin helps readers gain solid footing in how to conceive and conduct this research. A case study may be understood as the intensive study of a single case for the purpose of understanding a larger class of cases a population. Case study research may incorporate several cases. However, at a certain point it will no longer be possible to investigate those cases intensively. At the point where the emphasis of a study shifts from the individual case to a sample of cases we shall say that a study is cross-case.
Evidently, the distinction between a case study and cross-case study is a continuum. The fewer cases there are, and the more intensively they are studied, the more a work merits the appellation case study. Even so, this proves to be a useful distinction, for much follows from it. An observation is the most basic element of any empirical endeavor. Conventionally, the number of observations in an analysis is referred to with the letter N. Confusingly, N may also be used to designate the number of cases in a study, a usage that I shall try to avoid.
A single observation may be understood as containing several dimensions, each of which may be measured across disparate observations as a variable. Where the proposition is causal, these may be subdivided into dependent Y and independent X variables. The dependent variable refers to the outcome of an investigation. The independent variable refers to the explanatory causal factor, that which the outcome is supposedly dependent on.
This would be true, for example, in a cross-sectional analysis of multiple cases. In a case study, however, the case under study always provides more than one observation. These may be constructed diachronically by observing the case or some subset of within-case units through time or synchronically by observing within-case variation at a single point in time. This is a clue to the fact that case studies and cross-case usually operate at different levels of analysis. The case study is typically focused on within-case variation if p. The cross-case study, as the name suggests, is typically focused on cross-case variation if there is also within-case variation, it is secondary in importance.
They have the same object in view—the explanation of a population of cases—but they go about this task differently. A sample consists of whatever cases are subjected to formal analysis; they are the immediate subject of a study or case study. Confusingly, the sample may also refer to the observations under study, and will be so used at various points in this narrative.
But at present, we treat the sample as consisting of cases. Technically, one might say that in a case study the sample consists of the case or cases that are subjected to intensive study. However, usually when one uses the term sample one is implying that the number of cases is rather large. Case studies, like large-N samples, seek to represent, in all ways relevant to the proposition at hand, a population of cases.
A series of case studies might therefore be referred to as a sample if they are relatively brief and relatively numerous; it is a matter of emphasis and of degree. The more case studies one has, the less intensively each one is studied, and the more confident one is in their representativeness of some broader population , the more likely one is to describe them as a sample rather than a series of case studies.
For practical reasons—unless, that is, a study is extraordinarily long—the case study research format is usually limited to a dozen cases or less. A single case is not at all unusual. The sample rests within a population of cases to which a given proposition refers. The population of an inference is thus equivalent to the breadth or scope of a proposition. I use the terms proposition , hypothesis , inference , and argument interchangeably.
Note that most samples are not exhaustive; hence the use of the term sample, referring to sampling from a population. Occasionally, however, the sample equals the population of an inference; all potential cases are studied. For those familiar with the rectangular form of a dataset it may be helpful to conceptualize observations as rows, variables as columns, and cases as either groups of observations or individual observations. Case Study versus Cross-case Analysis. I have argued that the case study approach to research is most usefully defined as the intensive study of a single unit or a small number of units the cases , for the purpose of understanding a larger class of similar units a population of cases.
This p. These will be understood as methodological affinities flowing from a minimal definition of the concept. Table Case study and cross-case research designs: affinities and trade-offs. The case study research design exhibits characteristic strengths and weaknesses relative to its large-N cross-case cousin.
These tradeoffs derive, first of all, from basic research goals such as 1 whether the study is oriented toward hypothesis generating or hypothesis testing, 2 whether internal or external validity is prioritized, 3 whether insight into causal mechanisms or causal effects is more valuable, and 4 whether the scope of the causal inference is deep or broad.
These tradeoffs also hinge on the shape of the empirical universe, i. Along each of these dimensions, case study research has an affinity for the first factor and cross-case research has an affinity for the second, as summarized in Table To clarify, these tradeoffs represent methodological affinities , not invariant laws. Exceptions can be found to each one.
Even so, these general tendencies are often noted in case study research and have been reproduced in multiple disciplines and subdisciplines over the course of many decades. Case studies are more useful for generating new hypotheses, all other things being equal. Ceteris are not always paribus.
One should not jump to conclusions about the research design appropriate to a given setting without considering the entire range of issues involved—some of which may be more important than others. The conjectural element of social science is usually dismissed as a matter of guesswork, inspiration, or luck—a leap of faith, and hence a poor subject for methodological reflection.
Their classic status derives from the introduction of a new idea or a new perspective that is subsequently subjected to more rigorous and refutable analysis. Indeed, it is difficult to devise a program of falsification the first time a new theory is proposed.
Path-breaking research, almost by definition, is protean. Subsequent research on that topic tends to be more definitive insofar as its primary task is limited: to verify or falsify a pre-existing hypothesis. Thus, the world of social science may be usefully divided according to the predominant goal undertaken in a given study, either hypothesis generating or hypothesis testing. There are two moments of empirical research, a lightbulb moment and a skeptical moment, each of which is essential to the progress of a discipline.
Case studies enjoy a natural advantage in research of an exploratory nature. Several millennia ago, Hippocrates reported what were, arguably, the first case studies ever p. They were fourteen in number. Piaget formulated his theory of human cognitive development while watching his own two children as they passed from childhood to adulthood. Evidently, the sheer number of examples of a given phenomenon does not, by itself, produce insight. It may only confuse. How many times did Newton observe apples fall before he recognized the nature of gravity?
This is an apocryphal example, but it illustrates a central point: case studies may be more useful than cross-case studies when a subject is being encountered for the first time or is being considered in a fundamentally new way. Let us briefly explore why this might be so. Traditionally, scientific methodology has been defined by a segregation of conjecture and refutation. One should not be allowed to contaminate the other. Inspiration is more likely to occur in the laboratory than in the shower. The circular quality of conjecture and refutation is particularly apparent in case study research.
Each of these topics entails a different population and a different set of causal factors. A good deal of authorial intervention is necessary in the course of defining a case study topic, for there is a great deal of evidentiary leeway. It is the very fuzziness of case studies that grants them an advantage in research at the exploratory stage, for the single-case study allows one to test a multitude of hypotheses in a rough-and-ready way. The relationships discovered among different elements of a single case have a prima facie causal connection: they are all at the scene of the crime. This is revelatory when one is at an early stage of analysis, for at that point there is no identifiable suspect and the crime itself may be difficult to discern.
The fact that A , B , and C are present at the expected times and places relative to some outcome of interest is sufficient to establish them as independent variables. Proximal evidence is all that is required. A large-N cross-study, by contrast, generally allows for the testing of only a few hypotheses but does so with a somewhat greater degree of confidence, as is appropriate to work whose primary purpose is to test an extant theory.
There is less room for authorial intervention because evidence gathered from a cross-case research design can be interpreted in a limited number of ways. It is therefore more reliable. Another way of stating the point is to say that while case studies lean toward Type 1 errors falsely rejecting the null hypothesis , cross-case studies lean toward Type 2 errors failing to reject the false null hypothesis.
This explains why case studies are more likely to be paradigm generating, while cross-case studies toil in the prosaic but highly structured field of normal science. I do not mean to suggest that case studies never serve to confirm or disconfirm hypotheses. Evidence drawn from a single case may falsify a necessary or sufficient hypothesis, as discussed below.
However, general theories rarely offer the kind of detailed and determinate predictions on within-case variation that would allow one to reject a hypothesis through pattern matching without additional cross-case evidence. Thus, one is unlikely to reject a hypothesis, or to consider it definitively proved, on the basis of the study of a single case. Harry Eckstein himself acknowledges that his argument for case studies as a form of theory confirmation is largely hypothetical.
At the time of writing, several decades ago, he could not point to any social science study where a crucial case study had performed the heroic role assigned to it. Indeed, it is true even of experimental case studies in the natural sciences. When one finds, for example, that competent observers advocate strongly divergent points of view, it seems likely on a priori grounds that both have observed something valid about the natural situation, and that both represent a part of the truth.
The stronger the controversy, the more likely this is. Thus we might expect in such cases an experimental outcome with mixed results, or with the balance of truth varying subtly from experiment to experiment. The more mature focus…avoids crucial experiments and instead studies dimensional relationships and interactions along many degrees of the experimental variables. The tradeoff between hypothesis generating and hypothesis testing helps us to reconcile the enthusiasm of case study researchers and the skepticism of case study critics.
They are both right, for the looseness of case study research is a boon to new conceptualizations just as it is a bane to falsification. Questions of validity are often distinguished according to those that are internal to the sample under study and those that are external i. Cross-case research is always more representative of the population of interest than case study research, so long as some sensible procedure of case selection is followed presumably some version of random sampling.
Case study research suffers problems of representativeness because it includes, by definition, only a small number of cases of some more general phenomenon. Are the men chosen by Robert Lane typical of white, immigrant, working-class, American males?
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This means that case study research is generally weaker with respect to external validity than its cross-case cousin. The corresponding virtue of case study research is its internal validity. Often, though not invariably, it is easier to establish the veracity of a causal relationship pertaining to a single case or a small number of cases than for a larger set of cases. Case study researchers share the bias of experimentalists in this regard: they tend to be more disturbed by threats to within-sample validity than by threats to out-of-sample validity.
Thus, it seems appropriate to regard the tradeoff between external p. A third tradeoff concerns the sort of insight into causation that a researcher intends to achieve.
Two goals may be usefully distinguished. The first concerns an estimate of the causal effect ; the second concerns the investigation of a causal mechanism i. By causal effect I refer to two things: a the magnitude of a causal relationship the expected effect on Y of a given change in X across a population of cases and b the relative precision or uncertainty associated with that point estimate. Evidently, it is difficult to arrive at a reliable estimate of causal effects across a population of cases by looking at only a single case or a small number of cases. The one exception would be an experiment in which a given case can be tested repeatedly, returning to a virgin condition after each test.
But here one faces inevitable questions about the representativeness of that much-studied case. It is now well established that causal arguments depend not only on measuring causal effects, but also on the identification of a causal mechanism. Moreover, without a clear understanding of the causal pathway s at work in a causal relationship it is impossible to accurately specify the model, to identify possible instruments for the regressor of interest if there are problems of endogeneity , or to interpret the results.
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In the task of investigating causal mechanisms, cross-case studies are often not so illuminating. It has become a common criticism of large-N cross-national research—e. We learn, for example, that infant mortality is strongly correlated with state failure; 23 but it is quite another matter to interpret this finding, which is consistent with a number of different causal mechanisms. Sudden increases in infant mortality might be the product of famine, of social unrest, of new disease vectors, of government repression, and of countless other factors, some of which might be expected to impact the stability of states, and others of which are more likely to be a result of state instability.
Case studies, if well constructed, may allow one to peer into the box of causality to locate the intermediate factors lying between some structural cause and its purported effect. Thus, Dennis Chong uses in-depth interviews with a very small sample of respondents in order to better understand the process by which people reach decisions about civil liberties issues.
Chong comments:. One of the advantages of the in-depth interview over the mass survey is that it records more fully how subjects arrive at their opinions. While we cannot actually observe the underlying mental process that gives rise to their responses, we can witness many of its outward manifestations.
The way subjects ramble, hesitate, stumble, and meander as they formulate their answers tips us off to how they are thinking and reasoning through political issues. Dietrich Rueschemeyer and John Stephens offer an example of how an examination of causal mechanisms may call into question a general theory based on cross-case evidence. The thesis of interest concerns the role of British colonialism in fostering democracy among postcolonial regimes. But, critically, we argued on the basis of the contrast with Central America, British colonialism did prevent the local plantation elites from controlling the local state and responding to the labor rebellion of the s with massive repression.
Against the adamant opposition of that elite, the British colonial rulers responded with concessions which allowed for the growth of the party— union complexes rooted in the black middle and working classes, which formed the backbone of the later movement for democracy and independence.
Thus, the narrative histories of these cases indicate that the robust statistical relation between British colonialism and democracy is produced only in part by diffusion. The interaction of class forces, state power, and colonial policy must be brought in to fully account for the statistical result. To be sure, causal mechanisms do not always require explicit attention.
They may be quite obvious. And in other circumstances, they may be amenable to cross-case investigation. For example, a sizeable literature addresses the causal relationship between trade openness and the welfare state. The usual empirical finding is that more open economies are associated with higher social welfare spending. The question then becomes why such a robust correlation exists. What are the plausible interconnections between trade openness and social welfare spending?
One possible causal path, suggested by David Cameron, 29 is that increased trade openness leads to greater domestic economic vulnerability to external shocks due, for instance, to changing terms of trade. As it happens, the correlation is not robust and this leads some commentators to doubt whether the putative causal mechanism proposed by David Cameron and many others is actually at work.
Even so, the opportunities for investigating causal pathways are generally more apparent in a case study format. Consider the contrast between formulating a standardized survey for a large group of respondents and formulating an in-depth interview with a single subject or a small set of subjects, such as that undertaken by p. In the latter situation, the researcher is able to probe into details that would be impossible to delve into, let alone anticipate, in a standardized survey. She may also be in a better position to make judgements as to the veracity and reliability of the respondent.
Tracing causal mechanisms is about cultivating sensitivity to a local context. Often, these local contexts are essential to cross-case testing. Yet, the same factors that render case studies useful for micro-level investigation also make them less useful for measuring mean average causal effects. It is a classic tradeoff. The utility of a case study mode of analysis is in part a product of the scope of the causal argument that a researcher wishes to prove or demonstrate.
Arguments that strive for great breadth are usually in greater need of cross-case evidence; causal arguments restricted to a small set of cases can more plausibly subsist on the basis of a single-case study. Propositional breadth and evidentiary breadth generally go hand in hand. Granted, there are a variety of ways in which single-case studies can credibly claim to provide evidence for causal propositions of broad reach—e. Even so, a proposition with a narrow scope is more conducive to case study analysis than a proposition with a broad purview, all other things being equal.
The breadth of an inference thus constitutes one factor, among many, in determining the utility of the case study mode of analysis. By the same token, one of the primary virtues of the case study method is the depth of analysis that it offers. One may think of depth as referring to the detail, richness, completeness, wholeness, or the degree of variance in an outcome that is accounted for by an explanation. Otherwise stated, cross-case studies are likely to explain only a small portion of the variance with respect to a given outcome. They approach that outcome at a very p.
Whether to strive for breadth or depth is not a question that can be answered in any definitive way. All we can safely conclude is that researchers invariably face a choice between knowing more about less, or less about more. The case study method may be defended, as well as criticized, along these lines. The case study researcher who feels that cross-case research on a topic is insensitive to context is usually not arguing that nothing at all is consistent across the chosen cases.
Indeed, I believe that a number of traditional issues related to case study research can be understood as the product of this basic tradeoff.
Case Study Research: Design and Methods (Applied Social Research Methods)
For example, case study research is often lauded for its holistic approach to the study of social phenomena in which behavior is observed in natural settings. Cross-case research, by contrast, is criticized for its construction of artificial research designs that decontextualize the realm of social behavior by employing abstract variables that seem to bear little relationship to the phenomena of interest. The choice between a case study and cross-case style of analysis is driven not only by the goals of the researcher, as reviewed above, but also by the shape of the empirical universe that the researcher is attempting to understand.
Consider, for starters, that the logic of cross-case analysis is premised on some degree of cross-unit comparability unit homogeneity. Cases must be similar to each other in whatever respects might affect the causal relationship that the writer is investigating, or such differences must be controlled for. Case study researchers are often suspicious of large-sample research, which, they suspect, contains heterogeneous cases whose differences cannot easily be modeled. Deterrence, in their view, has many independent causal paths causal equifinality , and these paths may be obscured when a study lumps heterogeneous cases into a common sample.
Another example, drawn from clinical work in psychology, concerns heterogeneity among a sample of individuals. Michel Hersen and David Barlow explain:. Descriptions of results from 50 cases provide a more convincing demonstration of the effectiveness of a given technique than separate descriptions of 50 individual cases. The major difficulty with this approach, however, is that the category in which these clients are classified most always becomes unmanageably heterogeneous. When cases are described individually, however, a clinician stands a better chance of gleaning some important information, since specific problems and specific procedures are usually described in more detail.
When one lumps cases together in broadly defined categories, individual case descriptions are lost and the ensuing report of percentage success becomes meaningless. Under circumstances of extreme case heterogeneity, the researcher may decide that she is better off focusing on a single case or a small number of relatively homogeneous cases. Within-case evidence, or cross-case evidence drawn from a handful of most-similar cases, may be more useful than cross-case evidence, even though the ultimate interest of the investigator is in a broader population of cases.
Suppose one has a population of very heterogeneous cases, one or two of which undergo quasi-experimental transformations. Probably, one gains greater insight into causal patterns throughout the population by examining these cases in detail than by undertaking some large-N cross-case analysis. By the same token, if the cases available for study are relatively homogeneous, then the methodological argument for cross-case analysis is correspondingly strong.
The inclusion of additional cases is unlikely to compromise the results of the investigation because these additional cases are sufficiently similar to provide useful information. If, in the quest to explain a particular phenomenon, each potential case offers only one observation and also p. There is no point in using cross-case analysis or in extending a two-case study to further cases. If, on the other hand, each additional case is relatively cheap—if no control variables are needed or if the additional case offers more than one useful observation through time —then a cross-case research design may be warranted.
When adjacent cases are heterogeneous additional cases are expensive, for every added heterogeneous element must be correctly modeled, and each modeling adjustment requires a separate and probably unverifiable assumption. The more background assumptions are required in order to make a causal inference, the more tenuous that inference is; it is not simply a question of attaining statistical significance. The ceteris paribus assumption at the core of all causal analysis is brought into question.
In any case, the argument between case study and cross-case research designs is not about causal complexity per se in the sense in which this concept is usually employed , but rather about the tradeoff between N and K in a particular empirical realm, and about the ability to model case heterogeneity through statistical legerdemain. To be sure, one can look—and ought to look—for empirical patterns among potential cases. If those patterns are strong then the assumption of case comparability seems reasonably secure, and if they are not then there are grounds for doubt.
However, debates about case comparability usually concern borderline instances. Consider that many phenomena of interest to social scientists are not rigidly bounded. If one is studying democracies there is always the question of how to define a democracy, and therefore of determining how high or low the threshold for inclusion in the sample should be. Researchers have different ideas about this, and these ideas can hardly be tested in a rigorous fashion.
Similarly, there are longstanding disputes about whether it makes sense to lump poor and rich societies together in a single sample, or whether these constitute distinct populations. Many case study researchers feel that to compare societies with vastly different cultures and historical trajectories is meaningless.
Where do like cases end and unlike cases begin? Because this issue is not, strictly speaking, empirical it may be referred to as an ontological element of research design. An ontology is a vision of the world as it really is, a more or less coherent set of assumptions about how the world works, a research Weltanschauung analogous to a Kuhnian paradigm. What one finds is contingent upon what one looks for, and what one looks for is to some extent contingent upon what one expects to find.
Cross-case researchers, by contrast, have a less differentiated vision of the world; they are more likely to believe that things are pretty much the same everywhere, at least as respects basic causal processes. These basic assumptions, or ontologies, drive many of the choices made by researchers when scoping out appropriate ground for research.
Regardless of whether the population is homogeneous or heterogeneous, causal relationships are easier to study if the causal effect is strong, rather than weak. It invokes both the shape of the evidence at hand and whatever priors might be relevant to an interpretation of that evidence. Where X has a strong effect on Y it will be relatively easy to study this relationship.
Weak relationships, by contrast, are often difficult to discern. This much is commonsensical, and applies to all research designs. For our purposes, what is significant is that weak causal relationships are particularly opaque when encountered in a case study format. Thus, there is a methodological affinity between weak causal relationships and large-N cross-case analysis, and between strong causal relationships and case study analysis.
This point is clearest at the extremes. A necessary and sufficient cause accounts for all of the variation on Y. A sufficient cause accounts for all of the variation in certain instances of Y. A necessary cause accounts, by itself, for the absence of Y. In all three situations, p. There are no exceptions.
It should be clear why case study research designs have an easier time addressing causes of this type. Consider that a deterministic causal proposition can be dis proved with a single case. However, if it is , then it has been decisively refuted by a single case study.
Proving an invariant causal argument generally requires more cases. However, it is not nearly as complicated as proving a probabilistic argument for the simple reason that one assumes invariant relationships; consequently, the single case under study carries more weight. Magnitude and consistency—the two components of causal strength—are usually matters of degree. It follows that the more tenuous the connection between X and Y, the more difficult it will be to address in a case study format.
This is because the causal mechanisms connecting X with Y are less likely to be detectable in a single case when the total impact is slight or highly irregular.
Qualitative Research Guide
However, because they tend to covary, and because we tend to conceptualize them in tandem, I treat them as components of a single dimension. Now, let us now consider an example drawn from the other extreme. There is generally assumed to be a weak relationship between regime type and economic performance.
Democracy, if it has any effect on economic growth at all, probably has only a slight effect over the near-to-medium term, and this effect is probably characterized by many exceptions cases that do not fit the general pattern. Because of the diffuse nature of this relationship it will probably be difficult to gain insight by looking at a single case.
Distinguishing case study as a research method from case reports as a publication type
Weak relationships are difficult to observe in one instance. Note that even if there seems to be a strong relationship between democracy and economic growth p. A good deal of criticism has been directed toward studies of this type, where findings are rarely robust. The positive hypothesis, as well as the null hypothesis, is better approached in a sample rather than in a case.
Specifically, we must be concerned with the distribution of useful variation —understood as variation temporal or spatial on relevant parameters that might yield clues about a causal relationship. It follows that where useful variation is rare—i. Where, on the other hand, useful variation is common, a cross-case method of analysis may be more defensible.
Consider a phenomenon like social revolution, an outcome that occurs very rarely. The empirical distribution on this variable, if we count each country-year as an observation, consists of thousands of non-revolutions 0 and just a few revolutions 1. We need to know as much as possible about them, for they exemplify all the variation that we have at our disposal.
In this circumstance, a case study mode of analysis is difficult to avoid, though it might be combined with a large-N cross-case analysis. As it happens, many outcomes of interest to social scientists are quite rare, so the issue is by no means trivial. By way of contrast, consider a phenomenon like turnover, understood as a situation where a ruling party or coalition is voted out of office.
Turnover occurs within most p. There are lots of instances of both outcomes. Under these circumstances a cross-case research design seems plausible, for the variation across cases is regularly distributed. Another sort of variation concerns that which might occur within a given case. Suppose that only one or two cases within a large population exhibit quasi-experimental qualities: the factor of special interest varies, and there is no corresponding change in other factors that might affect the outcome. Clearly, we are likely to learn a great deal from studying this particular case—perhaps a lot more than we might learn from studying hundreds of additional cases that deviate from the experimental ideal.
But again, if many cases have this experimental quality, there is little point in restricting ourselves to a single example; a cross-case research design may be justified. A final sort of variation concerns the characteristics exhibited by a case relative to a particular theory that is under investigation. If no other crucial cases present themselves, then an intensive study of this particular case is de rigueur.
Of course, if many such cases lie within the population then it may be possible to study them all at once with some sort of numeric reduction of the relevant parameters. The general point here is that the distribution of useful variation across a population of cases matters a great deal in the choice between case study and cross-case research designs.
I have left the most prosaic factor for last. This is a practical matter, and is distinct from the actual ontological shape of the world. It concerns, rather, what we know about the former at a given point in time. An evidence-rich environment is one where all relevant factors are measurable, where p. An evidence-poor environment is the opposite. The question of available evidence impinges upon choices in research design when one considers its distribution across a population of cases. If relevant information is concentrated in a single case, or if it is contained in incommensurable formats across a population of cases, then a case study mode of analysis is almost unavoidable.
If, on the other hand, it is evenly distributed across the population—i. I employ data, evidence, and information as synonyms in this section. Consider the simplest sort of example, where information is truly limited to one or a few cases. Accurate historical data on infant mortality and other indices of human development are currently available for only a handful of countries these include Chile, Egypt, India, Jamaica, Mauritius, Sri Lanka, the United States, and several European countries.
Consequently, anyone studying this general subject is likely to rely heavily on these cases, where in-depth analysis is possible and profitable. Indeed, it is not clear whether any large-N cross-case analysis is possible prior to the twentieth century.