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Subgroup discovery in structural equation models.

Christoph Kiefer1, Florian Lemmerich2, Benedikt Langenberg1

  • 1Department of Psychological Methods and Evaluation, Bielefeld University.

Psychological Methods
|October 6, 2022
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Summary
This summary is machine-generated.

This study introduces SubgroupSEM, a novel method combining subgroup discovery with structural equation modeling (SEM) to identify distinct groups in data. This approach enhances the analysis of heterogeneous populations in social and behavioral sciences.

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Area of Science:

  • Social and Behavioral Sciences
  • Computer Science
  • Statistical Modeling

Background:

  • Structural Equation Modeling (SEM) is widely used in social and behavioral sciences.
  • Detecting distinct parameter groups within SEM is crucial for applied research.
  • Existing methods for heterogeneous group detection in SEM have limitations.

Purpose of the Study:

  • To introduce SubgroupSEM, a new approach integrating subgroup discovery with SEM.
  • To compare SubgroupSEM with finite mixture models and SEM trees for detecting heterogeneous groups.
  • To provide a practical guide and R package for implementing SubgroupSEM.

Main Methods:

  • Subgroup discovery algorithms from computer science applied to SEM.
  • Comparison of three methods: finite mixture models, SEM trees, and SubgroupSEM.
  • Step-by-step guide, pruning strategies, and four subgroup discovery algorithms.

Main Results:

  • SubgroupSEM offers a new framework for modeling and detecting heterogeneous groups in SEM.
  • The approach is illustrated with two real data examples: measurement invariance and educational trajectories.
  • The R package `subgroupsem` provides a practical implementation.

Conclusions:

  • SubgroupSEM is a viable and effective approach for applied researchers analyzing SEM data.
  • The method facilitates the discovery of meaningful subgroups with distinct characteristics.
  • Enhanced subgroup analysis in SEM contributes to a deeper understanding of complex phenomena.