Fuzzy Sets : Calibration Versus Measurement

Fuzzy sets are relatively new to social science. The first comprehensive introduction of fuzzy sets to the social sciences was offered by Michael Smithson (1987). However, applications were few and far between until the basic principles of fuzzy set analysis were elaborated through Qualitative Comparative Analysis (QCA; see Ragin 1987; 2000), an analytic system that is fundamentally settheoretic, as opposed to correlational, in both inspiration and design. The marriage of these two yields fuzzy-set QCA (fsQCA), a family of methods that offers social scientists an alternative to conventional quantitative methods, based almost exclusively on correlational reasoning (see Ragin, forthcoming). The basic idea behind fuzzy sets is easy enough to grasp, but this simplicity is deceptive. A fuzzy set scales degree of membership (e.g., membership in the set of Democrats) in the interval from 0.0 to 1.0, with 0.0 indicating full exclusion from a set and 1.0 indicating full inclusion. However, the key to useful fuzzy set analysis is well-constructed fuzzy sets, which in turn raises the issue of calibration. How does a researcher calibrate degree of membership in a set, for example, the set of Democrats? How should this set be defined? What constitutes full membership? What constitutes full nonmembership? What would a person with 0.75 membership in this set (more in than out, but not fully in) be like? How would this person differ from someone with 0.90 membership? The main message of this essay is that fuzzy sets, unlike conventional variables, must be calibrated. Because they must be calibrated, they are superior in many respects to conventional measures, as they are used in both quantitative and qualitative social science. In essence, I argue that fuzzy sets offer a middle path between quantitative and qualitative measurement. However, this middle path is not a compromise between these two; rather, it transcends many of the limitations of both.