Investigating the Impact of Covariate Inclusion on Sample Size Requirements of Factor Mixture Modeling: A Monte Carlo Simulation Study. Issue 5 (3rd September 2021)

• Record Type: Journal Article
• Authors: Wang, Yan; Hsu, Hsien-Yuan; Kim, Eunsook
• Is Part Of: Structural equation modeling. Volume 28:Issue 5(2021)
• Page Start: 716
• Page End: 724
• Publication Date: 2021-09-03
• Subjects: Factor mixture modeling -- sample size -- covariate -- model selection; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2021.1910036
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: Factor mixture modeling (FMM) has been increasingly used in behavioral and social sciences to capture underlying population heterogeneity. This Monte Carlo simulation study investigated a common issue in applications of FMM, sample size requirements, particularly taking into account the impact of covariate inclusion. Results indicated that it was critical to include a proper covariate into FMM for accurate class enumeration and the required sample size became smaller with larger covariate effect. Overall, the statistical power for detecting factor mean difference was adequate but positive bias was observed in some conditions. The required minimum sample sizes were suggested for applied researchers.