Multilevel Growth Mixture Models for Classifying Groups

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

Journal of Educational and Behavioral Statistics, 2010, vol. 35, issue 5, 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
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/1076998610376895 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:35:y:2010:i:5:p:532-565

DOI: 10.3102/1076998610376895

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().