004 - Course 05. Qualitative Comparative Analysis and Fuzzy Sets

Session Submission Type: Workshop

Description

The analytic challenge of case-oriented research is not simply that the number of cases is small, but that researchers gain useful in-depth knowledge of cases that is difficult to represent using conventional forms (e.g., representations that emphasize the “net effects” of “independent variables”). The researcher is left wondering how to represent knowledge of cases in a way that is meaningful and compact, but which also does not deny case complexity. Set-theoretic methods such as Qualitative Comparative Analysis (QCA), the central focus of this workshop, offer a solution. QCA is fundamentally a case-oriented method that can be applied to small-to-moderate size Ns. It is most useful when researchers have knowledge of each case included in an investigation, there is a relatively small number of such cases (e.g., 10-50), and the investigator seeks to compare cases as configurations. With these methods it is possible to construct representations of cross-case patterns that allow for substantial heterogeneity and diversity. This workshop offers an introduction to the approach and to the use of the software package fsQCA (a free download from www.fsqca.com). Both the crisp (i.e., Boolean) and fuzzy-set versions of the method will be presented.

Fuzzy set analysis is gaining popularity in the social sciences today because of the close connections it enables among verbal theory, substantive knowledge (especially in the assessment of degree of set membership), and the analysis of empirical evidence. Fuzzy sets are especially useful in case-oriented research, where the investigator has a degree of familiarity with the cases included in the investigation and seeks to understand cases configurationally—as specific combinations of aspects or elements. Using fuzzy-set methods, case outcomes can be examined in ways that allow for causal complexity, where different combinations of causally relevant conditions combine to generate the outcome in question. Also, with set-theoretic methods it is a possible to evaluate arguments that causal conditions are necessary or sufficient. Analyses of this type are outside the scope of conventional analytic methods.

Specific topics addressed in the course include: the differences between set-theoretic and correlational methods; conventional crisp sets versus fuzzy sets; the calibration of fuzzy sets; how calibration differs from conventional forms of measurement; analyzing fuzzy set relations; the correspondence between concepts and fuzzy set membership scores; the correspondence between theoretical statements and the analysis of fuzzy set relations; using fuzzy sets to study cases as configurations; and using fuzzy sets to unravel causal complexity, with a special focus on equifinality.

Sub Unit

• Course

Session Organizer

• Charles C. Ragin, University of California, Irvine

Leader

• Charles C. Ragin, University of California, Irvine