Causal complexity and party preference

Qualitative Comparative Analysis (QCA) overlaps logistic regression in explaining events, but challenges the latter's lack of accounting for causal complexity. QCA has only to a limited degree been applied to large-N studies or individuals as cases and has not incorporated the logic of probability. QCA and logistic regression are compared with respect to logic, procedure and outcome. Political orientations from five national surveys are adapted to the requirements of the two methods. The methods are demonstrated on explanations of individuals' party preferences. QCA and logistic regression converge and overlap in identifying degrees of causal complexity, in ascertaining model significance and in identifying antecedents to party preference. Results differ in degree, not in kind. A slightly more nuanced picture emerges using the QCA approach, whereas logistic regression delivers greater parsimony. Choice of method(s) is not arbitrary. QCA can easily be used on any large-N research problem. It should apply probability when appropriate.