Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration. Issue 6 (1st November 2016)

• Record Type: Journal Article
• Authors: Nylund-Gibson, Karen; Masyn, Katherine E.
• Is Part Of: Structural equation modeling. Volume 23:Issue 6(2016)
• Page Start: 782
• Page End: 797
• Publication Date: 2016-11-01
• Subjects: class enumeration -- covariates -- finite mixture -- latent class analysis (LCA) -- latent class predictors -- latent class regression (LCR); Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2016.1221313
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : For some time, there have been differing recommendations about how and when to include covariates in the mixture model building process. Some have advocated the inclusion of covariates after enumeration, whereas others recommend including them early on in the modeling process. These conflicting recommendations have led to inconsistent practices and unease in trusting modeling results. In an attempt to resolve this discord, we conducted a Monte Carlo simulation to examine the impact of covariate exclusion and misspecification of covariate effects on the enumeration process. We considered population and analysis models with both direct and indirect paths from the covariates to the latent class indicators. As expected, misspecified covariate effects most commonly led to the overextraction of classes. Findings suggest that the number of classes could be reliably determined using the unconditional latent class model, thus our recommendation is that class enumeration be done prior to the inclusion of covariates.