Structural Equation Model Trees

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and t...

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Bibliographic Details
Published inPsychological methods Vol. 18; no. 1; pp. 71 - 86
Main Authors Brandmaier, Andreas M, von Oertzen, Timo, McArdle, John J, Lindenberger, Ulman
Format Journal Article
LanguageEnglish
Published United States American Psychological Association 01.03.2013
Subjects
Computation
Data Interpretation, Statistical
Factor Analysis
Humans
Intelligence Tests
Models, Statistical
Multivariate Analysis
Structural Equation Models
Wechsler Adult Intelligence Scale (Revised)
Wechsler Scales - statistics & numerical data
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Summary:In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree structures that separate a data set recursively into subsets with significantly different parameter estimates in a SEM. SEM Trees provide means for finding covariates and covariate interactions that predict differences in structural parameters in observed as well as in latent space and facilitate theory-guided exploration of empirical data. We describe the methodology, discuss theoretical and practical implications, and demonstrate applications to a factor model and a linear growth curve model. (Contains 6 figures.)
ISSN:1082-989X
1939-1463
DOI:10.1037/a0030001