TY - JOUR

T1 - Modeling predictors of latent classes in regression mixture models

AU - Kim, Minjung

AU - Vermunt, Joeren

AU - Bakk, Zsuzsa

AU - Jaki, Thomas Friedrich

AU - Van Horn, M. Lee

N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Structural Equation Modeling on 21/04/2016, available online: http://www.tandfonline.com/10.1080/10705511.2016.1158655

PY - 2016/6

Y1 - 2016/6

N2 - 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.

AB - 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.

KW - finite mixture model

KW - including covariates

KW - latent class predictor

KW - regression mixture model

U2 - 10.1080/10705511.2016.1158655

DO - 10.1080/10705511.2016.1158655

M3 - Journal article

VL - 23

SP - 601

EP - 614

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 4

ER -