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Neglect of publication bias compromises meta-analyses of educational research
I. Ropovik, M. Adamkovic, D. Greger
Language English Country United States
Document type Journal Article, Research Support, Non-U.S. Gov't
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- MeSH
- Biomedical Research * MeSH
- Humans MeSH
- Meta-Analysis as Topic * MeSH
- Publication Bias * MeSH
- Journalism, Medical MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Because negative findings have less chance of getting published, available studies tend to be a biased sample. This leads to an inflation of effect size estimates to an unknown degree. To see how meta-analyses in education account for publication bias, we surveyed all meta-analyses published in the last five years in the Review of Educational Research and Educational Research Review. The results show that meta-analyses usually neglect publication bias adjustment. In the minority of meta-analyses adjusting for bias, mostly non-principled adjustment methods were used, and only rarely were the conclusions based on corrected estimates, rendering the adjustment inconsequential. It is argued that appropriate state-of-the-art adjustment (e.g., selection models) should be attempted by default, yet one needs to take into account the uncertainty inherent in any meta-analytic inference under bias. We conclude by providing practical recommendations on dealing with publication bias.
Faculty of Education University of Presov Presov Slovakia
Institute of Psychology Faculty of Arts University of Presov Presov Slovakia
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- $a Because negative findings have less chance of getting published, available studies tend to be a biased sample. This leads to an inflation of effect size estimates to an unknown degree. To see how meta-analyses in education account for publication bias, we surveyed all meta-analyses published in the last five years in the Review of Educational Research and Educational Research Review. The results show that meta-analyses usually neglect publication bias adjustment. In the minority of meta-analyses adjusting for bias, mostly non-principled adjustment methods were used, and only rarely were the conclusions based on corrected estimates, rendering the adjustment inconsequential. It is argued that appropriate state-of-the-art adjustment (e.g., selection models) should be attempted by default, yet one needs to take into account the uncertainty inherent in any meta-analytic inference under bias. We conclude by providing practical recommendations on dealing with publication bias.
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