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Fitting meta-analytic structural equation models with complex datasets.

Sandra Jo Wilson1, Joshua R Polanin2, Mark W Lipsey1

  • 1Peabody Research Institute, Vanderbilt University, USA.

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|June 12, 2016
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Summary
This summary is machine-generated.

This study introduces a modified two-stage meta-analytic structural equation modeling approach for complex datasets. It addresses issues with multiple measures and heterogeneous correlations, improving the synthesis of pooled correlation matrices.

Keywords:
meta-analysismeta-analytic structural equation modelingmultilevel meta-analysissystematic review

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Quantitative Psychology

Background:

  • Standard two-stage meta-analytic structural equation modeling (MASEM) faces challenges with large, complex datasets.
  • Common issues include multiple measures for the same construct within primary studies and substantial heterogeneity in correlation coefficients.
  • This heterogeneity can obscure construct relationships and reduce comparability across study correlations.

Purpose of the Study:

  • To present a modification of the first stage of two-stage MASEM tailored for large, complex datasets.
  • To address the challenges of multiple measures per construct and heterogeneous correlations in meta-analyses.
  • To provide a robust method for synthesizing correlation matrices in meta-analytic structural equation modeling.

Main Methods:

  • A three-level random effects model is employed to synthesize a pooled correlation matrix, accommodating dependent correlation coefficients.
  • Meta-regression is utilized to generate covariate-adjusted correlation coefficients, mitigating the impact of unevenly distributed moderator variables.
  • The proposed techniques are presented non-technically with an illustrative meta-analytic dataset.

Main Results:

  • The modified approach effectively synthesizes pooled correlation matrices from studies with dependent correlations.
  • Meta-regression successfully generates covariate-adjusted correlations, reducing the influence of moderators.
  • The presented methods enhance the handling of complex data structures in MASEM.

Conclusions:

  • The modified first-stage MASEM procedure offers a viable solution for large, complex datasets.
  • The use of three-level random effects models and meta-regression improves the accuracy and comparability of meta-analytic findings.
  • This approach enhances the utility of MASEM in synthesizing research findings, particularly in the presence of methodological complexities.