Rights statement: 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
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -