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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Veröffentlicht in: | Psychological methods 2013-03, Vol.18 (1), p.71-86 |
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creator | Brandmaier, Andreas M von Oertzen, Timo McArdle, John J Lindenberger, Ulman |
description | 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.) |
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subjects | Biological and medical sciences Computation Data Interpretation, Statistical Data Mining Factor Analysis Fundamental and applied biological sciences. Psychology Humans Intelligence Tests Methodology Models, Statistical Multivariate Analysis Parameters Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Psychometrics. Statistics. Methodology Social sciences Statistics. Mathematics Structural Equation Modeling Structural Equation Models Trees Wechsler Adult Intelligence Scale (Revised) Wechsler Scales - statistics & numerical data |
title | Structural Equation Model Trees |
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