TY - JOUR
T1 - A Comparison of Methods for Uncovering Sample Heterogeneity
T2 - Structural Equation Model Trees and Finite Mixture Models
AU - Jacobucci, Ross
AU - Grimm, Kevin
AU - McArdle, John J.
N1 - Funding Information:
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).
Publisher Copyright:
Copyright © Taylor & Francis Group, LLC.
PY - 2017/3/4
Y1 - 2017/3/4
N2 - 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.
AB - 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.
KW - decision trees
KW - finite mixture models
KW - growth mixture models
KW - structural equation model trees
UR - http://www.scopus.com/inward/record.url?scp=85002251495&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85002251495&partnerID=8YFLogxK
U2 - 10.1080/10705511.2016.1250637
DO - 10.1080/10705511.2016.1250637
M3 - Article
AN - SCOPUS:85002251495
SN - 1070-5511
VL - 24
SP - 270
EP - 282
JO - Structural Equation Modeling
JF - Structural Equation Modeling
IS - 2
ER -