Abstract
Heterogeneity in the performance of persons affected with schizophrenia or schizotypy psychopathology on laboratory tasks has long been recognized for the challenges it poses for experimental psychopathology, genetic, and other investigations. Traditional techniques such as factor analysis, discriminant function analysis, and cluster analysis have all been deemed inadequate for resolving heterogeneity due to one or another statistical shortcoming or limitation. A group of experimental subjects was initially identified as schizotypic using the well-known Perceptual Aberration Scale. We present a Bayesian approach, computationally implemented using a Gibbs sampling strategy, that enables one to effectively parse this experimental group in a manner that reduces heterogeneity and allows for the separation of what are termed true schizotypes and false-positives. This approach is complemented by maximum likelihood estimation, based on the expectation-maximization (EM) algorithm. The validity of our parsing strategy is supported by reference to other laboratory indexes of relevance to schizophrenia and schizotypy that were not included in the initial analyses.
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Jensen, S.T., Lenzenweger, M.F., Rubin, D.B. (2002). A Bayesian Approach to Reducing Heterogeneity in Laboratory Measures: An Illustration from Schizophrenia Research. In: Gatsonis, C., et al. Case Studies in Bayesian Statistics. Lecture Notes in Statistics, vol 167. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2078-7_12
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DOI: https://doi.org/10.1007/978-1-4612-2078-7_12
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