Abstract
Configural Frequency Analysis (CFA) is a technique for discovering co-occurrence patterns in categorical data that are often interpreted as indicating the presence of some syndrome or "type." This article focuses on alternative explanations for significant types identified using CFA. Specifically, although significant patterns of co-occurrence in categorical data may signal presence of a syndrome or type, such a finding can also indicate that the observed contingency table is a mixture of 2 or more underlying populations with different baserates on at least 2 of the variables under consideration. Alternatively, the assumed base model of response for the data is incorrect, thereby producing significant residual cells that are falsely identified as a syndrome. It is argued that other base models, such as a Markov chain model, may be more appropriate for prospective research. Finally, correct identification of a CFA type is most likely to occur when the researcher analyzes only that subset of variables that underlie the proposed syndrome. Graphical Bayesian belief networks, such as those operationalized in the Tetrad program, are advanced as a useful adjunct for identification of the relevant variables implicated in a syndrome as well as specification of alternate base models.