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

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)259-269
Number of pages11
JournalStructural Equation Modeling
Volume23
Issue number2
DOIs
StatePublished - 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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