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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Structural Equation Modeling on 28/08/2015, available online: http://wwww.tandfonline.com 10.1080/10705511.2015.1035437

    Accepted author manuscript, 666 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects

Research output: Contribution to journalJournal article

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  • M. Lee Van Horn
  • Y. Feng
  • Minjung Kim
  • Andrea E. Lamont
  • Daniel Feaster
  • Thomas Jaki
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<mark>Journal publication date</mark>2016
<mark>Journal</mark>Structural Equation Modeling
Issue number2
Volume23
Number of pages11
Pages (from-to)259-269
Publication statusPublished
Early online date28/08/15
Original languageEnglish

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.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in Structural Equation Modeling on 28/08/2015, available online: http://wwww.tandfonline.com 10.1080/10705511.2015.1035437