A Bayesian approach to configural frequency analysis

Abstract

Configural Frequency Analysis (CFA) is a method for cell‐wise inspection of cross‐classifications. CFA searches for types, that is, patterns of variable categories that occur more often than expected from some chance model, and for antitypes, that is, patterns observed less often than expected. Thus far, CFA has been plagued by the difficulties involved when looking for patterns of types and antitypes. This article introduces Bayesian CFA. Using Bayesian CFA one can (1) search for types and antitypes as before with the advantage that adjustment of the experiment‐wise significance level α is not necessary; and (2) test whether groups of types and antitypes form composite types or composite antitypes. This option is crucial when patterns of types or antitypes must exist for a concept to be retained. Empirical examples use data from alcohol research and from sleep research to illustrate both new options. Characteristics of Bayesian CFA and extensions are discussed.