Theory-guided exploration with structural equation model forests.

Area of Science:

• Psychometrics
• Statistical Modeling
• Data Mining

Background:

• Structural Equation Models (SEMs) and decision trees are established analytical tools.
• SEM trees integrate these methods for theory-guided empirical data exploration.
• SEM trees identify subgroups with similar data patterns within hypothesized multivariate outcome models.

Purpose of the Study:

• To introduce SEM forests as a stable alternative to SEM trees.
• To present aggregate measures for model improvement within SEM forests.
• To illustrate the utility of SEM forests in analyzing intelligence and episodic memory data.

Main Methods:

• SEM trees recursively partition data to find subgroups with similar patterns.
• SEM forests aggregate multiple SEM trees built on resampled data for stability.
• Variable importance (via OOB permutations) and case proximity (for clustering/outlier detection) are key aggregate measures.

Main Results:

• SEM forests demonstrate increased stability compared to individual SEM trees.
• Variable importance measures quantify the impact of predictors on model-predicted distributions.
• Case proximity facilitates clustering and outlier detection within the data.

Conclusions:

• SEM forests enhance the stability and interpretability of SEM tree analyses.
• Aggregate measures in SEM forests offer valuable insights for model refinement and data exploration.
• SEM forests are a promising tool for theory-guided research in psychology and related fields.