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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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A preliminary data analysis workflow for meta-analysis of dependent effect sizes.

James E Pustejovsky1, Jingru Zhang1, Elizabeth Tipton2

  • 1Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI, USA.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a workflow for preliminary analysis of meta-analytic data with dependent effect sizes. It helps validate data integrity and guides statistical modeling choices.

Keywords:
initial data analysismeta-analysisworkflow

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Meta-analyses frequently utilize dependent effect size estimates and hierarchical data.
  • Existing statistical methods for dependent effect sizes are advanced, but preliminary data analysis stages are less explored.
  • Understanding data structure and variation is crucial before formal statistical modeling.

Purpose of the Study:

  • To propose a generic workflow for preliminary, exploratory analyses of meta-analytic databases with dependent effect sizes.
  • To focus on validating input data integrity and informing subsequent statistical modeling decisions.
  • To enhance the understanding of data structure and variation in meta-analytic research.

Main Methods:

  • Developing a workflow for exploratory analysis of meta-analytic databases.
  • Creating summaries and visualizations of primary study features.
  • Examining between- and within-study variation in the data.
  • Illustrating the workflow with previously published meta-analytic data.

Main Results:

  • The proposed workflow aids in validating the integrity of meta-analytic input data.
  • Visualizations and summaries help understand data structure and distribution, including variation.
  • The workflow effectively informs decisions for subsequent statistical modeling strategies.
  • Demonstrated applicability using real-world meta-analytic datasets.

Conclusions:

  • A structured workflow for preliminary analysis is essential for meta-analyses with dependent effect sizes.
  • This approach improves data integrity checks and guides the selection of appropriate statistical models.
  • The workflow enhances the interpretability of meta-analytic data, particularly regarding variation.