A Comparison of Methods for Uncovering Sample Heterogeneity: Structural Equation Model Trees and Finite Mixture Models

Ross Jacobucci, Kevin Grimm, John J. McArdle

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Although finite mixture models have received considerable attention, particularly in the social and behavioral sciences, an alternative method for creating homogeneous groups, structural equation model trees (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013), is a recent development that has received much less application and consideration. It is our aim to compare and contrast these methods for uncovering sample heterogeneity. We illustrate the use of these methods with longitudinal reading achievement data collected as part of the Early Childhood Longitudinal Study–Kindergarten Cohort. We present the use of structural equation model trees as an alternative framework that does not assume the classes are latent and uses observed covariates to derive their structure. We consider these methods as complementary and discuss their respective strengths and limitations for creating homogeneous groups.

Original languageEnglish (US)
Pages (from-to)270-282
Number of pages13
JournalStructural Equation Modeling
Volume24
Issue number2
DOIs
StatePublished - Mar 4 2017

Keywords

  • decision trees
  • finite mixture models
  • growth mixture models
  • structural equation model trees

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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