title: Group membership prediction when known groups consist of unknown subgroups: a Monte Carlo comparison of methods.
abstract: Classification using standard statistical methods such as linear discriminant analysis (LDA) or logistic regression (LR) presume knowledge of group membership prior to the development of an algorithm for prediction. However, in many real world applications members of the same nominal group, might in fact come from different subpopulations on the underlying construct. For example, individuals diagnosed with depression will not all have the same levels of this disorder, though for the purposes of LDA or LR they will be treated in the same manner. The goal of this simulation study was to examine the performance of several methods for group classification in the case where within group membership was not homogeneous. For example, suppose there are 3 known groups but within each group two unknown classes. Several approaches were compared, including LDA, LR, classification and regression trees (CART), generalized additive models (GAM), and mixture discriminant analysis (MIXDA). Results of the study indicated that CART and mixture discriminant analysis were the most effective tools for situations in which known groups were not homogeneous, whereas LDA, LR, and GAM had the highest rates of misclassification. Implications of these results for theory and practice are discussed.
authors.affiliation: Department of Educational Psychology, Ball State University Muncie, IN, USA.
authors.forename: W Holmes
authors.initials: WH
authors.lastname: Finch
authors.affiliation: Department of Educational Psychology, Ball State University Muncie, IN, USA.
authors.forename: Jocelyn H
authors.initials: JH
authors.lastname: Bolin
authors.affiliation: Department of Management, University of Notre Dame Notre Dame, IN, USA.
authors.forename: Ken
authors.initials: K
authors.lastname: Kelley
keywords: classification trees; discriminant analysis; generalized additive models; mixture models; subgroup analysis