ArticleFrontiers in psychology2020
Score-Guided Structural Equation Model Trees.
Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Score-based tests for parameter instability in ordinal factor models.The British journal of mathematical and statistical psychology · 2025Article
- Tree-based latent variable model for assessing differential item functioning in patient-reported outcome measures: a simulation study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Investigating heterogeneity in IRTree models for multiple response processes with score-based partitioning.The British journal of mathematical and statistical psychology · 2025Article
- Article
- Latent Variable Forests for Latent Variable Score Estimation.Educational and psychological measurement · 2024Article
- Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning.Applied psychological measurement · 2024Article
- Science Interest, Utility, Self-Efficacy, Identity, and Science Achievement Among High School Students: An Application of SEM Tree.Frontiers in psychology · 2021Article
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3 authors.
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Abstract
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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