Qualitative Comparative Analysis (QCA) is a technique, developed by Charles Ragin in 1987, for solving the problems that are caused by making causal inferences on the basis of only a small number of cases.[1] The method is used in social science and is based on the binary logic of Boolean algebra, and attempts to maximize the number of comparisons that can be made across the cases under investigation.
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The technique aims to alleviate the small N problem by allowing inferences to be drawn from the maximum number of comparisons that can be made across the cases under analysis. The small N problem arises when the number of units of analysis (e.g. countries) available is inherently limited. For example: a study where countries are the unit of analysis is limited in the fact that are only a limited number of countries in the world (less than 200), far less than necessary for (probabilistic) statistical techniques. By maximizing the number of comparisons that can be made across the cases under investigation, causal inferences are according to Ragin possible. This technique would also allow the analysis of multiple causation and interaction effects.[2]
As this is a logical (deterministic) and not a statistical (probabilistic) technique, with "Crisp-Set" QCA (csQCA), the original application of QCA, variables can only have two values, which is problematic as the researcher has to determine the values of each variable. For example: GDP per capita has to be divided by the researcher in two categories (eg. low = 0 and high = 1). But as this variable is essentially a continuous variable, the division will always be arbitrary. A second, related problem is the fact that the technique does not allow an assessment of the effect of the relative strengths of the independent variables (as they can only have two values).[2] Ragin, and other scholars such as Lasse Cronqvist, have tried to deal with these issues by developing new tools that extend QCA, such as Multi-value QCA (mvQCA) and fuzzy set QCA (fsQCA).[3]