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A tutorial on Bayesian model-averaged meta-analysis in JASP.

Sophie W Berkhout1, Julia M Haaf2, Quentin F Gronau2

  • 1University of Amsterdam, Amsterdam, Netherlands. s.w.berkhout@gmail.com.

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Summary
This summary is machine-generated.

Bayesian model-averaged meta-analysis offers advantages over standard methods for synthesizing research. This tutorial demonstrates its application for analyzing child language development using JASP software.

Keywords:
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Area of Science:

  • Statistics
  • Developmental Psychology
  • Computational Linguistics

Background:

  • Meta-analysis synthesizes findings from multiple studies.
  • Standard meta-analytic methods have limitations in quantifying evidence and model flexibility.

Purpose of the Study:

  • Introduce Bayesian model-averaged meta-analysis (BMMA).
  • Highlight practical advantages of BMMA over standard methods.
  • Illustrate BMMA application using JASP software.

Main Methods:

  • Tutorial format explaining BMMA concepts and logic.
  • Application of BMMA using the open-source JASP software.
  • Running example: meta-analysis of child language development studies.

Main Results:

  • Demonstration of conducting a BMMA.
  • Guidance on interpreting BMMA results.
  • Showcasing BMMA's ability to quantify evidence for absence of effect and handle multiple models.

Conclusions:

  • BMMA provides a flexible and informative approach to meta-analysis.
  • JASP software facilitates the practical application of BMMA.
  • BMMA enhances evidence synthesis in fields like developmental psychology.