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Testing for funnel plot asymmetry of standardized mean differences.

James E Pustejovsky1, Melissa A Rodgers1

  • 1Educational Psychology Department, The University of Texas at Austin, Austin, Texas.

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|December 4, 2018
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Summary
This summary is machine-generated.

Publication bias threatens research synthesis validity. Conventional funnel plot asymmetry tests, like Egger's regression, are miscalibrated for standardized mean differences due to effect size and standard error correlation.

Keywords:
meta-analysisoutcome reporting biaspublication biasstandardized mean difference

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Area of Science:

  • Statistics
  • Psychometrics
  • Educational Research

Background:

  • Publication bias and outcome reporting bias compromise research synthesis validity.
  • Funnel plot asymmetry tests (Egger's regression, rank correlation, Trim-and-Fill) are common for detecting bias.
  • Egger's regression test shows miscalibration with log-odds ratio effect sizes due to artifactual correlation.

Purpose of the Study:

  • To investigate bias detection issues in meta-analyses using standardized mean difference effect sizes.
  • To evaluate Type I error rates of conventional funnel plot asymmetry tests.
  • To assess the performance of a likelihood ratio test from a three-parameter selection model.

Main Methods:

  • Simulation study using standardized mean difference effect sizes.
  • Assessment of Type I error rates for conventional funnel plot asymmetry tests.
  • Evaluation of a likelihood ratio test from a three-parameter selection model.

Main Results:

  • Conventional tests for funnel plot asymmetry exhibit inflated Type I error rates.
  • This inflation is caused by the correlation between the effect size estimate and its standard error.
  • Tests using a modified standard error formula or a variance-stabilizing transformation maintain nominal Type I error rates.

Conclusions:

  • Standardized mean difference meta-analyses are susceptible to inflated Type I errors with conventional bias detection methods.
  • Modified tests or variance-stabilizing transformations are recommended for accurate bias detection.
  • Addressing these statistical issues is crucial for reliable research syntheses in education and psychology.