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  • Modeling Latent Class Predictors_Final_Submitted

    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

    Accepted author manuscript, 518 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Modeling predictors of latent classes in regression mixture models

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<mark>Journal publication date</mark>06/2016
<mark>Journal</mark>Structural Equation Modeling
Issue number4
Volume23
Number of pages14
Pages (from-to)601-614
Publication StatusPublished
Early online date21/04/16
<mark>Original language</mark>English

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.

Bibliographic note

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