Finding the "genuine" schizotype: a model and method for resolving heterogeneity in performance on laboratory measures in experimental psychopathology research

Heterogeneity in the performance of persons affected with schizophrenia or schizotypic psychopathology on various laboratory tasks has long been recognized, both for its consistency across tasks and studies and for the massive methodological and substantive challenges it poses for experimental psychopathology, genetic, and other investigations. Traditional multivariate techniques, such as factor analysis, discriminant function analysis, and cluster analysis, have all been deemed inadequate for resolving heterogeneity, because of one or another statistical limitation. Here, an objective statistical approach based on a formal statistical model that uses the ubiquitous and well-developed expectation-maximization (EM) algorithm (A. P. Dempster, N. M. Laird, & D. B. Rubin, 1977) is presented, which enables one effectively to partition a group of experimental subjects, in this case identified initially using the well-known Perceptual Aberration Scale (L. J. Chapman, J. P. Chapman, & M. L. Raulin, 1978), in a manner that reduces heterogeneity and allows for the separation of what are termed genuine and false-positive schizotypes. The validity of the parsing strategy was supported by reference to other laboratory indexes of relevance to schizophrenia and schizotypy that were not included in the initial EM-based analyses. The potential utility of this approach is discussed with reference to future schizophrenia and schizotypy research.