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

Bibliographic Data

ID23316397
AuthorsGitta H Lubke (0000-0003-2472-9771, University of Notre Dame), Gitta Lubke (University of Notre Dame), Bengt Muthén (0000-0001-9308-6525, University of California, Los Angeles), Bengt O Muthén (University of California, Los Angeles)
Year2007
Volume14
Issue1
Pages26-47
Publication date2007-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueStructural Equation Modeling: A Multidisciplinary Journal (JOURNAL)
Journal identifiersISSN: 1070-5511 • E-ISSN: 1532-8007
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10705510709336735
OpenAlexW4231731917
LanguageEN
Citations received270
References cited29

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 investigates aspects of model performance such as parameter coverage and correct class membership assignment and focuses on covariate effects, model size, and class-specific versus class-invariant parameters. 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 unsatisfactory for the small class separation without covariates, but improves dramatically with increasing separation, covariate effects, or both. Model performance is not influenced by the differences in model size investigated here. Class-specific parameters may improve some aspects of model performance but negatively affect other aspects.

Class (philosophy) · Covariate · Econometrics · Factor analysis · Function (biology) · Latent class model · Latent variable model · Mixture model · Multivariate statistics · Statistics · Artificial Intelligence · Bayesian Methods and Mixture Models · Computer Science · Genetic and phenotypic traits in livestock · Mathematics · Statistical Methods and Bayesian Inference