Structural equation model trees

Psychol Methods. 2013 Mar;18(1):71-86. doi: 10.1037/a0030001. Epub 2012 Sep 17.


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.

MeSH terms

  • Data Interpretation, Statistical*
  • Humans
  • Models, Statistical*
  • Wechsler Scales / statistics & numerical data