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Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach.

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This study addresses dependent effect sizes in meta-analysis, demonstrating how 3-level meta-analyses effectively model these dependencies. It extends key statistical concepts and offers practical implementation using the metaSEM package in R.

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Area of Science:

  • Social Sciences
  • Educational Sciences
  • Medical Sciences
  • Management Sciences
  • Behavioral Sciences

Background:

  • Meta-analysis synthesizes research but commonly assumes independent effect sizes.
  • Dependent effect sizes arise from multiple measures within studies or shared participant characteristics (e.g., cultural groups).
  • Existing methods for handling dependent effect sizes are reviewed, highlighting limitations.

Purpose of the Study:

  • To demonstrate the application of 3-level meta-analyses for modeling dependent effect sizes.
  • To compare the structural equation modeling approach with the multilevel approach for 3-level meta-analysis.
  • To extend established meta-analytic statistics (Q, I2, R2) to the 3-level framework.

Main Methods:

  • Utilizing a 3-level meta-analysis framework.
  • Employing structural equation modeling (SEM) for analysis.
  • Implementing procedures using the open-source metaSEM package in the R statistical environment.

Main Results:

  • The study illustrates the practical implementation of 3-level meta-analyses for dependent effect sizes.
  • Advantages of the SEM approach for 3-level meta-analysis are discussed.
  • Extensions of Q statistics, I2, and R2 to 3-level models are presented.

Conclusions:

  • 3-level meta-analysis provides a robust method for handling dependent effect sizes across various scientific disciplines.
  • The metaSEM package facilitates the application of these advanced techniques.
  • Further research directions in 3-level meta-analysis are identified.