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that similar outcomes in several cases must have a common cause that remains to be discovered. This assumption misdirects the attention of the investigator by leading him or her to believe that the task of empirical inquiry is to discover a single causal pattern for cases that have similar values on the dependent variable. Instead, a major redefinition of the task of developing theory is required when a phenomenon is governed by equifinality. The task becomes that of discovering different causal patterns that lead to similar outcomes. When a phenomenon is governed by equifinality, the investigator’s task is to produce a differentiated empirically based theory that identifies different causal patterns that produce similar outcomes. If this research task is taken seriously, investigators cannot content themselves with a claim that they have at least discovered a common causal factor for all or many cases that have similar outcomes on the dependent variable. Such an explanation is incomplete, and even if justified, it leaves unanswered the question of the causal weight of the common factor in the total explanation.

Some investigators may attempt to deal with the challenge posed by equifinality by claiming only than that the relationship embodied in the single causal proposition is probabilistic. However, a quantitative description of that probability is usually left unspecified, since it would require considerable additional empirical research either on the total universe of relevant cases or a sample.

The phenomenon of equifinality also complicates efforts to assess a deductive theory’s ability to make successful predictions. Sensitivity to the possibility that the phenomenon in question is subject to equifinality requires the investigator to consider the likelihood that some undetermined number of outcomes that the deductive theory predicts or fails to predict can be predicted by another deductive theory. In addition, equifinality also calls attention to the possibility that successful predic-tions may not be necessarily valid explanations, since another theory may be able to claim to explain as well as to predict those outcomes.

Gary King, Robert Keohane, and Sidney Verba do not consider the implications of equifinality for research design and theory building in Designing Social Inquiry. In their sparse discussion of equifinality, they seem concerned primarily to claim that equifinality is not inconsistent with their definition of causal inference and their prescriptions on how to achieve such inferences.315 In contrast, we give considerable emphasis to the widespread prevalence of equifinality and to several ways of taking it into account in the design and interpretation of research—for example, by developing typological theories. Charles Ragin also provides a detailed discussion of equifinality and its implications for research and theory in Fuzzy-Set Social Science.316

Similarly, there is little attention in Designing Social Inquiry (DSI) to comparative politics and to ways of doing good comparative work.317 Not surprisingly, specialists in comparative politics have expressed important reservations about this book—for example, in the June 1995 issue of the American Political Science Review. King, Keohane, and Verba display little interest in important developments and controversies in the comparative politics field in the past two decades, summarized so well by David Collier in his article “The Comparative Method: Two Decades of Change.”318

Extensions and Adaptations to Mill’s Methods

QUALITATIVE COMPARATIVE ANALYSIS

The method of Qualitative Comparative Analysis (QCA) is a sophisticated extension of Mill’s methods developed by Charles Ragin that relies on Boolean algebra and relaxes some of the assumptions necessary for the direct use of Mill’s methods. QCA still requires rather restrictive conditions to arrive at valid causal inferences. Consequently, Ragin faces the same problem that Mill confronted: the challenge of reconciling his nondeterministic view of causality with the determinism necessary