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Meta-analysis in structural equation modeling (SEM) is growing. This study explores combining SEM parameters, addressing issues with heterogeneity in correlation matrices and proposing alternative methods for robust meta-analytic evidence.

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Area of Science:

  • Social Sciences
  • Statistics
  • Psychometrics

Background:

  • Structural equation models (SEM) are vital in social sciences.
  • Meta-analytic methods are increasingly used to synthesize SEM findings.
  • Existing meta-analytic approaches include direct and indirect methods.

Purpose of the Study:

  • To review current meta-analytic approaches for SEM.
  • To identify challenges posed by heterogeneity in correlation matrices.
  • To propose alternative methods for handling heterogeneity in SEM meta-analysis.

Main Methods:

  • The study discusses direct meta-analysis of structural coefficients.
  • It examines the indirect approach of combining correlation matrices first.
  • An alternative approach for managing heterogeneity is presented.

Main Results:

  • Direct meta-analysis is appealing when studies lack heterogeneity.
  • Heterogeneity in correlation matrices presents significant practical and conceptual issues.
  • The proposed alternative method offers a way to better manage heterogeneity.

Conclusions:

  • Handling heterogeneity is crucial for accurate SEM meta-analysis.
  • The suggested approach provides a framework for improved evidence synthesis.
  • Further research into robust meta-analytic techniques for SEM is warranted.