Effect size estimation from t-statistics in the presence of publication bias: A brief review of existing approaches with some extensions

Ulrich, Rolf ; Miller, Jeff ; Erdfelder, Edgar

DOI: https://doi.org/10.1027/2151-2604/a000319
URL: https://econtent.hogrefe.com/doi/abs/10.1027/2151-...
Additional URL: http://psycnet.apa.org/record/2018-04817-006
Document Type: Article
Year of publication: 2018
The title of a journal, publication series: Zeitschrift für Psychologie = Journal of Psychology
Volume: 226
Issue number: 1
Page range: 56-80
Place of publication: Göttingen
Publishing house: Hogrefe
ISSN: 2190-8370 , 2151-2604
Publication language: English
Institution: School of Social Sciences > Kognitive Psychologie u. Differentielle Psychologie (Erdfelder)
Subject: 150 Psychology
Abstract: 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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ORCID: Ulrich, Rolf ; Miller, Jeff ; Erdfelder, Edgar ORCID: 0000-0003-1032-3981

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