Attend a workshop on QCA and fuzzy sets: March 26-28, 2020 in Irvine, CaliforniaClick WORKSHOP for more information. View the poster: HERE
What is QCA?Qualitative Comparative Analysis (QCA) is a new analytic technique that uses Boolean algebra to implement principles of comparison used by scholars engaged in the qualitative study of macro social phenomena. Typically, qualitatively oriented scholars examine only a few cases at a time, but their analyses are both intensive -- addressing many aspects of cases -- and integrative -- examining how the different parts of a case fit together, both contextually and historically. By formalizing the logic of qualitative analysis, QCA makes it possible to bring the logic and empirical intensity of qualitative approaches to studies that embrace more than a handful of cases -- research situations that normally call for the use of variable-oriented, quantitative methods. Boolean methods of logical comparison represent each case as a combination of causal and outcome conditions. These combinations can be compared with each other and then logically simplified through a bottom-up process of paired comparison. Computer algorithms developed by electrical engineers in the 1950s provide techniques for simplifying this type of data. The data matrix is reformulated as a "truth table" and reduced in a way that parallels the minimization of switching circuits (see Charles Ragin, The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies). These minimization procedures mimic case-oriented comparative methods but accomplish the most cognitively demanding task -- making multiple comparisons of configurations -- through computer algorithms. The goal of the logical minimization is to represent -- in a shorthand manner -- the information in the truth table regarding the different combinations of conditions that produce a specific outcome.
What does the "fs" in fs/QCA mean?A conventional (or "crisp") set is dichotomous: An case is either "in" or "out" of a set, for example, the set of Protestants. Thus, a conventional set is comparable to a binary variable with two values, 1 ("in," i.e., Protestant) and 0 ("out," i.e., non-Protestant). A fuzzy set, by contrast, permits membership in the interval between 0 and 1 while retaining the two qualitative states of full membership and full non-membership. Thus, the fuzzy set of Protestants could include individuals who are "fully in" the set (fuzzy membership = 1.0), some who are "almost fully in" the set (membership = .90), some who are neither "more in" nor "more out" of the set (membership = .5, also known as the "crossover point"), some who are "barely more out than in" the set (membership = .45), and so on down to those who are "fully out" of the set (membership = 0). It is up to the researcher to specify procedures for assigning fuzzy membership scores to cases, and these procedures must be both open and explicit so that they can be evaluated by other scholars.
fsQCA can be applied to crisp (i.e., binary) sets, fuzzy sets, and to mixtures of fuzzy and crisp sets.