Modeling predictors of latent classes in regression mixture models

M. Kim, J.K. Vermunt, Z. Bakk, T. Jaki, M.L. Van Horn

Research output: Contribution to journalArticleScientificpeer-review

49 Citations (Scopus)


The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students’ academic achievement outcome. Implications of the study are discussed.
Keywords: finite mixture model, including covariates, latent class predictor, regression mixture model
Original languageEnglish
Pages (from-to)601-614
JournalStructural Equation Modeling
Issue number4
Publication statusPublished - 2016


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