Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects

M. Lee Van Horn; Yuling Feng; Minjung Kim; Andrea Lamont; Daniel Feaster; Thomas Jaki

doi:10.1080/10705511.2015.1035437

Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects

M. Lee Van Horn, Yuling Feng, Minjung Kim, Andrea Lamont, Daniel Feaster, Thomas Jaki

Public Health Sciences

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English (US)
Pages (from-to)	259-269
Number of pages	11
Journal	Structural Equation Modeling
Volume	23
Issue number	2
DOIs	https://doi.org/10.1080/10705511.2015.1035437
State	Published - Mar 3 2016

Keywords

heterogeneity in contextual effects
multilevel regression mixtures
regression mixture modeling

ASJC Scopus subject areas

Decision Sciences(all)
Modeling and Simulation
Sociology and Political Science
Economics, Econometrics and Finance(all)

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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.",

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AU - Feaster, Daniel

AU - Jaki, Thomas

N1 - Funding Information: This research was supported by grant number R01HD054736, M. Lee Van Horn (Principal Investigator), funded by the National Institute of Child Health and Human Development. Publisher Copyright: Copyright © Taylor & Francis Group, LLC. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.

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N2 - 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.

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Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects

Abstract

Keywords

ASJC Scopus subject areas

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