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Publication bias
Meta-analyses typically contain an overrepresentation of published studies that are biased towards statistically significant finding. We tested potential publication bias by means of the iterative non-parametric trim and fill procedure as implemented in CMA. This procedure controls for the association between individual effect sizes and their sample sizes (sampling error) by inspecting funnel plots: publication bias is present when the effect sizes of small studies with larger sapling variation than large studies are represented asymmetrically within and around the funnel. The Duval & Tweedie procedure provides a correction of the effect size after publication bias has been taken into account by trimming away studies suggesting asymmetry. We used the random effects model. Publication bias was also evaluated with Orwin’s fail-safe N statistic, which expresses how many studies would bring the combined effect to a specified level other than zero. The criterion was set to d=0.10.
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