title: Structural equation model trees.
abstract: 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.
authors.affiliation: Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany. brandmaier@mpib-berlin.mpg.de
authors.forename: Andreas M
authors.initials: AM
authors.lastname: Brandmaier
authors.forename: Timo
authors.initials: T
authors.lastname: von Oertzen
authors.forename: John J
authors.initials: JJ
authors.lastname: McArdle
authors.forename: Ulman
authors.initials: U
authors.lastname: Lindenberger
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D014888:Wechsler Scales / Q000706:statistics & numerical data