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An increasing number of analyses in various subfields of political science employ boolean algebra as proposed in Ragin's (1987) qualitative comparative analysis (QCA). This type of analysis is perfectly justifiable if the goal is to test deterministic hypotheses under the assumption of error-free measures of the employed variables. My contention is, however, that only in a very few research areas are our theories sufficiently advanced to yield deterministic hypotheses. Also, given the nature of our objects of study, error-free measures are largely an illusion. Hence, it is unsurprising that many studies employ QCA inductively and gloss over possible measurement errors. In this paper I address these issues and demonstrate the consequences of these problems with simple empirical examples. In an analysis similar to Monte Carlo simulation I show that using boolean algebra in an exploratory fashion without considering possible measurement errors may lead to dramatically misleading inferences. I then suggest remedies that help researchers to circumvent some of these pitfalls

Qualitative Comparative Analysis: How Inductive Use and Measurement Error Lead to Problematic Inference