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Using multiple group modeling to test moderators in meta-analysis.

Alexander M Schoemann1

  • 1East Carolina University, USA.

Research Synthesis Methods
|December 10, 2016
PubMed
Summary
This summary is machine-generated.

Structural equation modeling (SEM) and multilevel modeling (MLM) can fit meta-analyses. This study shows how multiple group analysis within SEM and MLM effectively tests categorical moderators in meta-analysis.

Keywords:
meta-analysismixed-effects modelmultiple group modelrandom-effects modelstructural equation model

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Area of Science:

  • Statistics
  • Psychometrics
  • Biostatistics

Background:

  • Meta-analysis is a widely used statistical technique.
  • Structural Equation Modeling (SEM) and Multilevel Modeling (MLM) are increasingly popular frameworks for conducting meta-analyses.
  • These advanced modeling techniques offer powerful analytical capabilities.

Purpose of the Study:

  • To demonstrate the application of multiple group analysis within SEM and MLM for testing categorical moderators in meta-analysis.
  • To highlight the advantages of using SEM and MLM for advanced meta-analytic research.
  • To provide practical guidance and illustrations for researchers.

Main Methods:

  • The study details the use of multiple group analysis, a technique within SEM and MLM.
  • This method involves fitting a model simultaneously to each level of a categorical moderator.
  • Parameter equality constraints across groups are used to test for moderator effects.

Main Results:

  • Multiple group analysis effectively tests for categorical moderators in meta-analysis using SEM and MLM.
  • The approach is particularly relevant for random-effects meta-analyses, allowing comparison of effect size means and between-study variances across groups.
  • A simulation study and a real-data analysis validated the proposed methodology.

Conclusions:

  • Multiple group analysis in SEM and MLM provides a robust framework for moderator analysis in meta-analysis.
  • This technique enhances the ability to investigate sources of heterogeneity in effect sizes.
  • The findings support the integration of advanced modeling techniques for more sophisticated meta-analytic research.