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 | |
| State | Published - Mar 3 2016 |
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Keywords
- heterogeneity in contextual effects
- multilevel regression mixtures
- regression mixture modeling
ASJC Scopus subject areas
- Modeling and Simulation
- Decision Sciences(all)
- Economics, Econometrics and Finance(all)
- Sociology and Political Science
Cite this
Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects. / Van Horn, M. Lee; Feng, Yuling; Kim, Minjung; Lamont, Andrea; Feaster, Daniel J; Jaki, Thomas.
In: Structural Equation Modeling, Vol. 23, No. 2, 03.03.2016, p. 259-269.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects
AU - Van Horn, M. Lee
AU - Feng, Yuling
AU - Kim, Minjung
AU - Lamont, Andrea
AU - Feaster, Daniel J
AU - Jaki, Thomas
PY - 2016/3/3
Y1 - 2016/3/3
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.
AB - 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.
KW - heterogeneity in contextual effects
KW - multilevel regression mixtures
KW - regression mixture modeling
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U2 - 10.1080/10705511.2015.1035437
DO - 10.1080/10705511.2015.1035437
M3 - Article
VL - 23
SP - 259
EP - 269
JO - Structural Equation Modeling
JF - Structural Equation Modeling
SN - 1070-5511
IS - 2
ER -