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Meta-analysis and publication bias: How well does the FAT-PET-PEESE procedure work?

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|March 14, 2018
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Summary
This summary is machine-generated.

The FAT-PET-PEESE (FPP) procedure is unreliable for detecting publication bias and testing effects in realistic economic meta-analyses. It is less efficient for estimating mean effects and hypothesis tests are frequently unreliable.

Keywords:
Monte CarloPrecision Effect Estimate with Standard Error (PEESE)funnel asymmetry test (FAT)meta-analysispublication biassimulations

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Area of Science:

  • Economics
  • Econometrics
  • Statistical Methods

Background:

  • Publication bias is a significant concern in meta-analysis.
  • The FAT-PET-PEESE (FPP) procedure is widely used in economics and business meta-analyses.
  • FPP is employed to detect publication bias, test for statistically significant effects, and estimate mean true effects.

Purpose of the Study:

  • To evaluate the performance of the FAT-PET-PEESE (FPP) procedure.
  • To assess FPP's reliability in realistic data environments with effect heterogeneity.
  • To compare FPP's efficiency against other estimators for mean effect estimation.

Main Methods:

  • Simulation studies were conducted to assess FPP performance.
  • The study examined FPP in fixed-effects and more realistic heterogeneous data environments.
  • The simulation framework of Stanley and Doucouliagos (2017) was recreated and utilized.

Main Results:

  • FPP performs well in a basic, fixed-effects environment.
  • FPP becomes unreliable for detecting publication bias and testing effects in heterogeneous data.
  • FPP is less efficient than alternative estimators for mean effect estimation in realistic settings.
  • Hypothesis tests regarding the mean true effect are often unreliable.

Conclusions:

  • The FPP procedure's reliability is questionable in realistic economic meta-analyses.
  • Researchers should exercise caution when using FPP, especially in the presence of effect heterogeneity.
  • Alternative estimators may be more appropriate for mean effect estimation and hypothesis testing in complex data.