A Comparison of Approaches for Estimating Covariate Effects in Nonparametric Multilevel Latent Class Models. Issue 5 (3rd September 2018)

• Record Type: Journal Article
• Authors: Park, Jungkyu; Yu, Hsiu-Ting
• Is Part Of: Structural equation modeling. Volume 25:Issue 5(2018)
• Page Start: 778
• Page End: 790
• Publication Date: 2018-09-03
• Subjects: Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1448711
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : The inclusion of covariates improves the prediction of class memberships in latent class analysis (LCA). Several methods for examining covariate effects have been developed over the past decade; however, researchers have limited to the comparisons of the performance among these methods in cases of the single-level LCA. The present study investigated the performance of three different methods for examining covariate effects in a multilevel setting. We conducted a simulation to compare the performance of the three methods when level-1 and level-2 covariates were simultaneously incorporated into the nonparametric multilevel latent class model to predict latent class membership at each level. The simulation results revealed that the bias-adjusted three-step maximum likelihood method performed equally well as the one-step method when the sample sizes were sufficiently large and the latent classes were distinct from each other. However, the unadjusted three-step method significantly underestimated the level-1 covariate effect in most conditions. Keywords : covariate effects, latent class models, multilevel modeling