Factor mixture models are designed for the analysis of multivariate data obtained from a population consisting of distinct latent classes. A common factor model is assumed to hold within each of the latent classes. Factor mixture modeling involves obtaining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investi...

This paper introduces the Mixed Birnbaum model with collateral information. A simulation study is conducted to evaluate parameter estimation for this model. More specifically, the gain of incorporating collateral information into the model is investigated. The results show that the standard errors as well as latent class assignment can benefit substantially from incorporating external variables...

We propose a modified version of the three-step estimation method for the latent class model with covariates, which may be used to estimate a latent Markov (LM) model with individual covariates and possible dropout. We illustrate the proposed approach through an application finalized to the study of the health status of elderly people hosted in Italian nursing homes. This application is based o...

The aim of the study was to evaluate the latent structure of DSM-IV schizotypal personality disorder (SPD) diagnostic criteria. The sample consisted of 564 consecutively admitted inpatients and outpatients. Exploratory latent class analysis identified a four-class model as the best fitting model for DSM-IV SPD criteria. The first of the SPD latent classes was mainly characterized by odd thinkin...

A simulation study is conducted to evaluate the usefulness of incorporating collateral information in the Mixed Rasch model. The results show that the standard errors as well as latent class membership assignment can benefit substantially from incorporating external variables that associate with the latent class variable. Especially when the difference in probability structure between the laten...