Decision support for selecting optimal logistic regression models

Abstract: This study concerns itself with providing user support for a decision problem in logistic regression analysis: given a set of metric variables and one binary dependent variable, select the optimal subset of variables that can best predict this dependent variable. The problem requires an evaluation of competing models based on heuristic selection criteria such as goodness-of-fit and prediction accuracy. This paper documents the heuristics, formalizes the algorithms, and eventually presents an interactive decision support system to facilitate the selection of such an optimal model. This study adds to the sparsely studied domain of expert systems for social science researchers, and makes three contributions to the literature. First, the study formalizes a number of heuristics to arrive at optimal logistic regression models. Second, the study presents two computational algorithms that incorporate these formalized heuristics. Third, the paper documents an implementation of these algorithms through an interactive decision support system. The study concludes with a discussion on the risks of relying too heavily on the system and with future opportunities for research. [Copyright &y& Elsevier]