Abstract
This paper outlines the method of structural equation model tree (SEMTree) for longitudinal design, which enables researchers simultaneously to extract the primary patterns of trajectories of changes and to explore independent variables that can explain the group differences in trajectories. More specifically, in SEMTree, relations between longitudinally observed dependent variables are structured through a template model using SEM, and one explores the independent variables that can explain the differences in terms of parameters in template model through decision tree. We also address the issue of the misspecifications of the template model, which can lead to dramatically different estimation results of trees.
