Case studies and case selection Gary Goertz Kroc Institute for International Peace Studies University of Notre Dame ggoertz@nd.edu Spring 2018
One uses case studies based on Z to answer the question: “How generalizable is the case study?”
Large-N qualitative testing There are no cross-case comparisons used for causal inference.
Large-N qualitative testing: some examples Target authors: Acemoglu, D., and J. Robinson. 2006. Economic origins of dictatorship and democracy. Cambridge: Cambridge University Press. Critics: Haggard, S. and Kaufman, R. 2012. Inequality and regime change: democratic transitions and the stability of democratic rule. American Political Science Review 106:1–22. Target author: Fearon, J. 1994. Domestic political audiences and the escalation of international disputes. American Political Science Review 88:577–92. Critic: Trachtenberg, M. 2012. Audience costs: an historical analysis. Security Studies 21:3–42. Target authors: Mansfield, E., and J. Synder. 2005. Electing to fight: why emerging democracies go to war. Cambridge: MIT Press. Critics: Narang, V. and Nelson, R. 2009. Who are these belligerent democratizers? Reassessing the impact of democratization on war. I International Organization 63:357–79.
Why large-N qualitative testing is possible: democratization and war Not Demo*Weak Inst. War 111 6 Not war 227 142
Recent work by Carles Boix and Daron Acemoglu and James Robinson has focused on the role of inequality and distributive conflict in transitions to and from democratic rule. We assess these claims through causal process observation, using an original qualitative dataset on democratic transitions and reversions during the “third wave” from 1980 to 2000. (Haggard and Kaufman 2012, 1, complete abstract).
Large-N qualitative testing: basic procedure 1. The target authors are quite clearly identified. 2. The target works are game theoretic or statistical. 3. The methodology of testing is looking for or at the causal mechanism and process tracing in individual cases. 4. Each case is coded as showing the causal mechanism or not. 5. Critics look at “all” cases. 6. They base their conclusions on the percentage of case studies where the causal mechanism is present. Typically the percentage of cases where the causal mechanism is present is low, and ideally close to zero.
Defining scope and populations: audience costs I will be looking at a set of crises – episodes in which there was a significant perceived risk of war – involving great powers, at least one of which was a democracy, and that were settled without war. These criteria were chosen for the following reasons. The cases are all crises because the Fearon theory explicitly deals with crises, but I will be looking only at great power crises for essentially practical reasons. . . . The focus here, moreover, is on crises in which at least one of the contending parties is a democracy, since much of the debate on this issue has to do with whether the audience costs mechanism gives democracies an advantage over non-democratic regimes. This means that the crises to be examined all took place after 1867. . . . Finally, only those crises that did not terminate in war will be examined here. . . . That set of criteria generates a list of about a dozen crises. (Trachtenberg 2012, 5–6)
X-centric strategy: choose on X = 1 column, e.g., where audience costs should be present and working.
Results of large-N qualitative testing: audience costs So what conclusion is to be drawn from the discussion in this whole section of the great power crises won by democratic states [(1,1) cases]? The basic finding is quite simple. There is little evidence that the audience costs mechanism played a “crucial” role in any of them. Indeed, it is hard to identify any case in which that mechanism played much of a role at all. There are all kinds of ways in which new information is generated in the course of a crisis, and that new information, for the reasons Fearon outlined, plays a fundamental role in determining how that crisis runs its course. Audience costs, however, were not a major factor in any of the crises examined here. (Trachtenberg 2012, 32)
The Y-centric strategy: exploring equifinality We assess these claims through causal process observation, using an original qualitative dataset on democratic transitions and reversions during the “third wave” from 1980 to 2000. Haggard and Kaufman 2012, abstract)
The Y-centric strategy: exploring equifinality We assess these claims through causal process observation, using an original qualitative dataset on democratic transitions and reversions during the “third wave” from 1980 to 2000. Haggard and Kaufman 2012, abstract) Against theoretical expectations, a substantial number of these transitions occur in countries with high levels of inequality. Less than a third of all reversions are driven by distributive conflicts between elites and masses. We suggest a variety of alternative causal pathways to both transitions and reversions. (Haggard and Kaufman 2012, abstract).
The convergence of X-centric and Y-centric strategies: the (0,0) cell is not used.
Comparative case studies mimics statistics The comparative [case study] method can now be defined as the method of testing hypothesized empirical relationships among variables on the basis of the same logic that guides the statistical method, but in which the cases are selected in such a way as to maximize the variance of the independent variables and to minimize the variance of the control variables . (Lijphart 1975, 164, emphasis in original) [A] good case (or set of cases) for purposes of causal analysis is generally one that exemplifies quasi-experimental properties, that is, it replicates the virtues of a true experiment even while lacking a manipulated treatment. (Gerring and Cojocaru 2017, 397)
Case study: definition A case study, for present purposes, is an intensive study of a single case or a small number of cases that promises to shed light on a larger population of cases. (Gerring and Cojocaru 2016, 394)
Typologies of case studies Lijphart (1971:691) proposes six case study types: a-theoretical, interpretative, hypothesis-generating, theory-confirming, theory-infirming, and deviant. Eckstein (1975) identifies five species: configurative-idiographic, disciplined-configurative, heuristic, plausibility probes, and crucial-case. Skocpol and Somers (1980) identify three logics of comparative history: macrocausal analysis, parallel demonstration of theory, and contrast of contexts. Gerring (2007a) and Seawright and Gerring (2008) identify nine techniques: typical, diverse, extreme, deviant, influential, crucial, pathway, most-similar, and most-different. Levy (2008) identifies five case study research designs: comparable, most-likely, least-likely, deviant, and process tracing . . . .
Typologies, Gerring Table 1. Case Selection Strategies. Goals/Strategies n Factors Criteria for Cases I. Descriptive (to describe) � Typical 1 þ D Mean, mode, or median of D � Diverse 2 þ D Typical subtypes II. Causal (to explain Y ) 1. Exploratory (to identify H x ) � Outcome 1 þ Y Maximize variation in Y � Index 1 þ Y First instance of D Y � Deviant 1 þ Z Y Poorly explained by Z � Most-similar 2 þ Z Y Similar on Z , different on Y � Most-different 2 þ Z Y Different on Z , similar on Y � Diverse 2 þ Z Y Allpossibleconfigurationsof Z (assumption: X 2 Z ) 2. Estimating (to estimate H x ) � Longitudinal 1 þ X Z X changes, Z constant or biased against H x � Most-similar 2 þ X Z Similar on Z , different on X 3. Diagnostic (to assess H x ) � Influential 1 þ X Z Y Greatest impact on P ( H x ) � Pathway 1 þ X Z Y X ! Y strong, Z constant or biased against H x � Most-similar 2 þ X Z Y Similar on Z , different on X and Y Note : D ¼ descriptive features (other than those to be described in a case study); H x ¼ causal hypothesis of interest; P ( H x ) ¼ the probability of H x ; X ¼ causal factor(s) of theoretical interest; X ! Y ¼ apparent or estimated causal effect, which may be strong (high in magnitude) or weak; Y ¼ outcome of interest; Z ¼ vector of background factors that may affect X and/or Y .
Exploratory case studies Outcome. An outcome case maximizes variation on the outcome of interest. This may be achieved by a case that exhibits extreme values on Y (or ∆ Y). (Gerring and Cojocaru 2016, 398)
Deviant Case A deviant case deviates from an expected causal pattern, as suggested by theories or common sense, registering a surprising result. (Gerring and Cojocaru 2016, 399)
Most-different type, method of agreement? Most-different cases (aka the method of agreement) vary widely in background factors regarded as potential causes (Z), while sharing a common outcome (Y). The assumption is that background factors that differ across the cases are unlikely to be causes of Y since that outcome is constant across the cases. The hope is that if a factor (X) can be identified that is constant across the cases it may be the cause of Y. (Gerring and Cojocaru 2016, 399–400)
Influential type An influential case is one whose status has a profound effect on the probability of a hypothesis being true, P ( H x ) . . . . In social science settings, the most influential cases are usually those that falsify, or threaten to falsify, a hypothesis. Decisively corroborating cases are rare. (Gerring and Cojocaru 2016, 403)
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