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This study introduces a new method for meta-analytic structural equation modeling (MASEM) using hierarchical modeling of sample covariance matrices. The approach effectively handles dependent matrices and fixed/random effects in meta-analysis.

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Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Meta-analytic structural equation modeling (MASEM) is crucial for synthesizing research findings.
  • Existing methods struggle with dependent covariance matrices, common in meta-analyses involving multiple outcomes or sources from single studies.
  • Handling both fixed- and random-effects models is essential for robust meta-analytic synthesis.

Purpose of the Study:

  • To present a novel hierarchical modeling approach for meta-analytic structural equation models (MASEM).
  • To address the challenge of dependent covariance matrices in meta-analytic research.
  • To provide a flexible framework accommodating both fixed- and random-effects meta-analytic SEMs.

Main Methods:

  • Utilizes hierarchical modeling of sample covariance matrices, assuming a Wishart distribution.
  • Develops a method to manage dependent covariance matrices arising from single studies or authors.
  • Employs a simulation study to evaluate the parameter recovery of the proposed approach.

Main Results:

  • The simulation study demonstrates the proposed approach's ability to adequately recover parameters.
  • The method successfully handles dependent covariance matrices in meta-analytic SEM.
  • The approach is validated for both fixed- and random-effects meta-analytic SEM.

Conclusions:

  • The presented hierarchical modeling approach offers a robust solution for meta-analytic structural equation modeling.
  • This method effectively addresses the issue of dependent covariance matrices, enhancing meta-analytic research.
  • The approach is implemented in the `bayesianmasem` R package, facilitating practical applications.