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Ehri Ryu1

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This study presents a new method for multilevel structural equation modeling (MSEM) to analyze multiple groups at Level 1. The Multiple Group analysis in MSEM (MG1-MSEM) approach addresses data dependencies and improves analysis accuracy.

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Area of Science:

  • Multilevel Structural Equation Modeling
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Standard multiple group analysis in single-level structural equation models is insufficient for multilevel data.
  • Level 1 group membership introduces complexities like non-independence of Level 2 data and inter-group dependencies within clusters.
  • Existing methods fail to adequately address these multilevel complexities.

Purpose of the Study:

  • Introduce and evaluate a novel procedure for multiple group analysis in multilevel structural equation models (MG1-MSEM).
  • Provide solutions for the challenges posed by Level 1 group membership in MSEM.
  • Enhance the accuracy and applicability of structural equation modeling for hierarchical data structures.

Main Methods:

  • Developed the Multiple Group analysis in MSEM (MG1-MSEM) approach.
  • Incorporated a Level 1 mean structure to account for differences between Level 1 groups within clusters.
  • Utilized Muthén's maximum likelihood (MUML) estimation for performance evaluation.
  • Applied the MG1-MSEM approach to both multilevel path and factor models.

Main Results:

  • The MG1-MSEM approach effectively addresses the non-independence of Level 2 data.
  • It accurately accounts for dependencies between members of different Level 1 groups within the same cluster.
  • Simulation studies demonstrated the performance of MUML estimation within the MG1-MSEM framework.
  • Empirical data analyses confirmed the utility of the MG1-MSEM approach.

Conclusions:

  • MG1-MSEM offers a robust solution for conducting multiple group analyses in multilevel structural equation models.
  • The method correctly models Level 1 group differences and Level 2 invariance.
  • This approach enhances the analysis of complex hierarchical data, applicable to path and factor models.