Modeling predictors of latent classes in regression mixture models

Kim, Minjung and Vermunt, Joeren and Bakk, Zsuzsa and Jaki, Thomas Friedrich and Van Horn, M. Lee (2016) Modeling predictors of latent classes in regression mixture models. Structural Equation Modeling, 23 (4). pp. 601-614. ISSN 1070-5511

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Structural Equation Modeling
Additional Information:
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
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3312
Subjects:
ID Code:
78353
Deposited By:
Deposited On:
24 Feb 2016 11:16
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Dec 2020 03:19