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Quantifying and explaining heterogeneity in meta-analytic structural equation modeling: Methods and illustrations.

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Summary
This summary is machine-generated.

This study compares Bayesian meta-analytic structural equation modeling (BMASEM) and one-stage MASEM (OSMASEM) for modeling heterogeneity in psychological research. It clarifies their application in analyzing interventions and scale structures.

Keywords:
Bayesian approachBetween-study heterogeneityMeta-analysisMeta-analytic structural equation modelingModerating effect

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Meta-Analysis

Background:

  • Meta-analytic structural equation modeling (MASEM) is valuable for hypothesis testing.
  • Modeling heterogeneity in structural equation modeling (SEM) parameters presents challenges.
  • Bayesian MASEM (BMASEM) and one-stage MASEM (OSMASEM) are novel methods addressing these challenges.

Purpose of the Study:

  • To describe and compare BMASEM and OSMASEM for psychological research.
  • To clarify the application of these methods in handling heterogeneity.
  • To illustrate their use in analyzing intervention mechanisms and scale factor structures.

Main Methods:

  • Comparison of BMASEM and OSMASEM using two empirical illustrations.
  • Application of both methods to test moderating effects of covariates.
  • Utilizing methods to build prediction equations for effect sizes and evaluate scale factor loadings.

Main Results:

  • The study provides practical illustrations of BMASEM and OSMASEM applications.
  • Demonstrates how these methods can address heterogeneity in psychological research.
  • Highlights the utility in analyzing mediating mechanisms and scale psychometrics.

Conclusions:

  • BMASEM and OSMASEM offer advanced approaches for handling heterogeneity in MASEM.
  • The study clarifies their distinct applications and practical considerations.
  • Provides guidance for researchers applying these sophisticated quantitative methods.