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

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.

Keywords:

• heterogeneity in contextual effects
• multilevel regression mixtures
• regression mixture modeling

Additional information

Funding

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.

Notes on contributors

M. Lee Van Horn

M. Lee Van Horn is now in the Department of Individual, Family and Community Education at the University of New Mexico. Minjung Kim is now in the Department of Psychology at the University of Alabama.