Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects.
This paper 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 one 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 zero. 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 which can be answered with this method and the issues faced when estimating multilevel regression mixtures.
University of South Carolina.
M Lee
ML
Van Horn
University of South Carolina.
Yuling
Y
Feng
University of South Carolina.
Minjung
M
Kim
University of South Carolina.
Andrea
A
Lamont
University of Miami.
Daniel
D
Feaster
Lancaster University.
Thomas
T
Jaki