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

被引:18
作者
Jacobucci, Ross [1 ]
Grimm, Kevin J. [2 ]
McArdle, John J. [1 ]
机构
[1] Univ Southern Calif, Los Angeles, CA USA
[2] Arizona State Univ, Tempe, AZ 85287 USA
基金
美国国家科学基金会;
关键词
decision trees; finite mixture models; growth mixture models; structural equation model trees; CLASSIFICATION; SKILLS; MPLUS;
D O I
10.1080/10705511.2016.1250637
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页码:270 / 282
页数:13
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