Score-Guided Structural Equation Model Trees - Arnold, Manuel; Voelkle, Manuel C.; Brandmaier, Andreas M. (Frontiers in Psychology, 2021)
Structural equation model (SEM) trees are a data-driven tool for finding variables that predict group differences in SEM parameters. SEM trees build upon the decision tree paradigm by growing tree structures that divide a data set recursively into homogeneous subsets. In past research, SEM trees have been estimated predominantly with the R package semtree. The original algorithm in the semtree package selects split variables among covariates by calculating a likelihood ratio for each possible split of each covariate. Obtaining these likelihood ratios is computationally demanding. As a remedy, we propose to guide the construction of SEM trees by a family of score-based tests that have recently been popularized in psychometrics (Merkle, Fan, & Zeileis, 2014; Merkle & Zeileis, 2013). These score-based tests monitor fluctuations in the case-wise derivatives of the likelihood function to detect parameter differences between groups. Compared to the likelihood-ratio approach, score-based tests are computationally efficient because they do not require refitting the model for every possible split. In this paper, we introduce score-guided SEM trees, implement them in semtree, and evaluate their performance by means of a Monte Carlo simulation.