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

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.

Keywords:

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

ACKNOWLEDGMENTS

We would like to thank Gitta Lubke for her helpful comments that led to a meaningful improvement in this research.

FUNDING

Ross Jacobucci was supported by funding through the National Institute on Aging Grant number T32AG0037. Kevin J. Grimm was supported by National Science Foundation Grant REAL-1252463 awarded to the University of Virginia, David Grissmer (PI) and Christopher Hulleman (Co-PI).

Additional information

Funding

Ross Jacobucci was supported by funding through the National Institute on Aging Grant number T32AG0037. Kevin J. Grimm was supported by National Science Foundation Grant REAL-1252463 awarded to the University of Virginia, David Grissmer (PI) and Christopher Hulleman (Co-PI).