Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects

Van Horn, M. Lee and Feng, Y. and Kim, Minjung and Lamont, Andrea E. and Feaster, Daniel and Jaki, Thomas (2016) Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects. Structural Equation Modeling, 23 (2). pp. 259-269. ISSN 1070-5511

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Abstract

This article proposes a novel exploratory approach for assessing how the effects of Level-2 predictors differ across Level-1 units. Multilevel regression mixture models are used to identify latent classes at Level 1 that differ in the effect of 1 or more Level-2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of constraining 1 of the random effects to 0. An application of the method to evaluate heterogeneity in the effects of classroom practices on students is used to show the types of research questions that can be answered with this method and the issues faced when estimating multilevel regression mixtures.

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 28/08/2015, available online: http://wwww.tandfonline.com 10.1080/10705511.2015.1035437
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3312
Subjects:
ID Code:
72847
Deposited By:
Deposited On:
02 Feb 2015 11:45
Refereed?:
Yes
Published?:
Published
Last Modified:
12 Jul 2020 04:56