Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters

Publisher Website
Google Scholar
Abstract
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 as- sumed to hold within each of the latent classes. Factor mixture modeling involves ob- taining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investigates aspects of model per- formance such as parameter coverage and correct class membership assignment and focuses on covariate effects, model size, and class-specific versus class-invariant pa- rameters. When fitting true models, parameter coverage is good for most parameters even for the smallest class separation investigated in this study (0.5 SD between 2 classes). The same holds for convergence rates. Correct class assignment is unsatis- factory for the small class separation without covariates, but improves dramatically with increasing separation, covariate effects, or both. Model performance is not in- fluenced by the differences in model size investigated here. Class-specific parame- ters may improve some aspects of model performance but negatively affect other aspects.

This publication has 28 references indexed in Scilit: