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2021-01-28Zeitschriftenartikel DOI: 10.3389/fpsyg.2020.564403
Score-Guided Structural Equation Model Trees
Arnold, Manuel cc
Voelkle, Manuel cc
Brandmaier, Andreas cc
Lebenswissenschaftliche Fakultät
Structural equation model (SEM) trees are data-driven tools 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 and Zeileis, 2013; Merkle et al., 2014). These score-based tests monitor fluctuations in 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.
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This article was supported by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.
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(CC BY 4.0) Attribution 4.0 International(CC BY 4.0) Attribution 4.0 International
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DOI
10.3389/fpsyg.2020.564403
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https://doi.org/10.3389/fpsyg.2020.564403
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<a href="https://doi.org/10.3389/fpsyg.2020.564403">https://doi.org/10.3389/fpsyg.2020.564403</a>