A Factor Mixture Model Approach to Elaborating on Offender Mental Health Classification With the MMPI–2–RF

Abstract

A large proportion of prison inmates suffer from mental illnesses or severe personality disorders; therefore, offender classification is a worthwhile endeavor both for efficiently allocating mental health treatment resources and security risk classification. This study sought to elaborate on offender classification by using an advanced statistical technique, factor mixture modeling, which capitalizes on the strengths of both latent trait analysis and latent class analysis. A sample consisting of 616 male and 194 female prison inmates was used for this purpose. The MMPI–2–RF Restructured Clinical (RC) scales were used to elaborate on a variety of latent trait, latent class, and factor mixture models. A 3-factor, 5-class mixture model was deemed optimal in this sample. Remaining MMPI–2–RF scales as well as scores on external criterion measures relevant to externalizing psychopathology were used to further elaborate on the utility of the resulting latent classes. These analyses indicated that 3 of the 5 classes were predominantly different expressions of externalizing personality proclivities, whereas the remaining 2 indicated inmates with substantial internalizing or thought-disordered characteristics. Implications of these findings are discussed.