Abstract: Publication bias hampers the estimation of true effect sizes. Specifically, effect sizes are systematically overestimated when
studies report only significant results. In this paper we show how this overestimation depends on the true effect size and on the sample size.
Furthermore, we review and follow up methods originally suggested by Hedges (1984), Iyengar and Greenhouse (1988), and Rust, Lehmann,
and Farley (1990) allowing the estimation of the true effect size from published test statistics (e.g., from the t-values of reported significant
results). Moreover, we adapted these methods allowing meta-analysts to estimate the percentage of researchers who consign undesired
results in a research domain to the file drawer. We also apply the same logic to the case when significant results tend to be underreported.
We demonstrate the application of these procedures for conventional one-sample and two-sample t-tests. Finally, we provide R and MATLAB
versions of a computer program to estimate the true unbiased effect size and the prevalence of publication bias in the literature.
Dieser Eintrag ist Teil der Universitätsbibliographie.
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