A general linear model for estimating effect size in the presence of publication bias

Jack L. Vevea*, Larry V. Hedges

*Corresponding author for this work

Research output: Contribution to journalArticle

138 Scopus citations

Abstract

When the process of publication favors studies with small p-values, and hence large effect estimates, combined estimates from many studies may be biased. This paper describes a model for estimation of effect size when there is selection based on one-tailed p-values. The model employs the method of maximum likelihood in the context of a mixed (fixed and random) effects general linear model for effect sizes. It offers a test for the presence of publication bias, and corrected estimates of the parameters of the linear model for effect magnitude. The model is illustrated using a well-known data set on the benefits of psychotherapy.

Original languageEnglish (US)
Pages (from-to)419-435
Number of pages17
JournalPsychometrika
Volume60
Issue number3
DOIs
StatePublished - Sep 1 1995

Keywords

  • effect size
  • meta-analysis
  • mixed models
  • publication bias
  • research synthesis
  • selection models

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

Fingerprint Dive into the research topics of 'A general linear model for estimating effect size in the presence of publication bias'. Together they form a unique fingerprint.

  • Cite this