Multilevel Growth Mixture Models for Classifying Groups

Gregory J. Palardy and Jeroen K. Vermunt
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Gregory J. Palardy: University of California
Jeroen K. Vermunt: Tilburg University

Journal of Educational and Behavioral Statistics, 2010, vol. 35, issue 5, pages 532-565

Abstract: This article introduces a multilevel growth mixture model (MGMM) for classifying both the individuals and the groups they are nested in. Nine variations of the general model are described that differ in terms of categorical and continuous latent variable specification within and between groups. An application in the context of school effectiveness research is presented. Schools are classified into three Type B effectiveness categories based on their mean student mathematics achievement growth trajectories, controlling for differences in students' backgrounds across schools. The classification outcome is regressed on a set of school practice variables to investigate the association between practices and cognitive development. Various issues related to model specification are discussed, including the use of covariates to identify substantively meaningful classes.

Keywords: multilevel growth mixture model; latent class model; school effectiveness (search for similar items in EconPapers)
Date: 2010
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Persistent link: http://EconPapers.repec.org/RePEc:sae:jedbes:v:35:y:2010:i:5:p:532-565

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