A New Perspective on the Effects of Covariates in Mixture Models. Issue 2 (4th March 2018)

• Record Type: Journal Article
• Authors: Stegmann, Gabriela; Grimm, Kevin J.
• Is Part Of: Structural equation modeling. Volume 25:Issue 2(2018)
• Page Start: 167
• Page End: 178
• Publication Date: 2018-03-04
• Subjects: covariates -- entropy -- finite mixture models -- growth mixture model; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1318070
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : In this simulation study, we explored the effect of introducing covariates to a growth mixture model when covariates were also generated by a mixture model. We varied the association between the latent classes underlying the growth trajectories and the covariates, the degree of separation between the latent classes underlying the covariates, the number of covariates included, and amount of missing data in the growth data. We found that adding covariates to the growth mixture model generally hurt class recovery except where the latent classes underlying the growth trajectories and the covariates were the same or very strongly associated, and there was a large degree of separation between the classes underlying the covariates. We found that when covariates were introduced, entropy might no longer be an accurate indicator of the distinctiveness of the growth trajectory classes.