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Testing moderator hypotheses in meta-analytic structural equation modeling using subgroup analysis.

Suzanne Jak1, Mike W-L Cheung2

  • 1Methods and Statistics, Child Development and Education, University of Amsterdam, Nieuwe Achtergracht 127, 1018 WS, Amsterdam, The Netherlands. S.Jak@uva.nl.

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Summary
This summary is machine-generated.

This study introduces improved methods for meta-analytic structural equation modeling (MASEM) to test subgroup differences in model parameters. It provides guidance and examples for more accurate moderator analyses in MASEM research.

Keywords:
Meta-analysisMeta-analytic structural equation modelingRandom-effects modelSubgroup analysisTwo-stage structural equation modeling

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Quantitative Psychology

Background:

  • Meta-analytic structural equation modeling (MASEM) combines correlation matrices from multiple studies.
  • Existing methods for testing parameter differences across subgroups in MASEM are often suboptimal.
  • Accurate subgroup analysis is crucial for understanding moderators in meta-analyses.

Purpose of the Study:

  • To provide guidance and practical examples for testing hypotheses about group differences in MASEM parameters.
  • To introduce improved fixed- and random-effects subgroup analysis techniques for MASEM.
  • To evaluate the impact of subgroup size on convergence in MASEM.

Main Methods:

  • Meta-analytic structural equation modeling (MASEM) with fixed- and random-effects models.
  • Subgroup analysis techniques applied to pooled correlation matrices.
  • A simulation study to assess convergence issues based on the number of studies per subgroup.

Main Results:

  • Demonstrated a clear procedure for conducting subgroup analyses in MASEM.
  • Illustrated the application of fixed- and random-effects subgroup analyses with real data.
  • Identified potential convergence problems related to subgroup size.

Conclusions:

  • The proposed methods offer a more rigorous approach to moderator analysis in MASEM.
  • Researchers can effectively test hypotheses about parameter variations across study subgroups.
  • Availability of data and R-scripts facilitates the adoption of these techniques.