Meta-Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection
This study investigates the small‐sample performance of meta‐regression methods for detecting and estimating genuine empirical effects in research literatures tainted by publication selection. Publication selection exists when editors, reviewers or researchers have a preference for statistically sig...
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Published in | Oxford bulletin of economics and statistics Vol. 70; no. 1; pp. 103 - 127 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Oxford, UK
Blackwell Publishing Ltd
01.02.2008
Department of Economics, University of Oxford |
Series | Oxford Bulletin of Economics and Statistics |
Subjects | |
Online Access | Get full text |
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Summary: | This study investigates the small‐sample performance of meta‐regression methods for detecting and estimating genuine empirical effects in research literatures tainted by publication selection. Publication selection exists when editors, reviewers or researchers have a preference for statistically significant results. Meta‐regression methods are found to be robust against publication selection. Even if a literature is dominated by large and unknown misspecification biases, precision‐effect testing and joint precision‐effect and meta‐significance testing can provide viable strategies for detecting genuine empirical effects. Publication biases are greatly reduced by combining two biased estimates, the estimated meta‐regression coefficient on precision (1/Se) and the unadjusted‐average effect. |
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Bibliography: | I wish to thank Chris Doucouliagos, Stephen Jarrell, Randall Rosenberger, Alex Sutton, and an anonymous referee for their helpful comments. I also gratefully acknowledge the support of a US Environmental Protection Agency STAR (Science To Achieve Results) grant #RD-832-421-01. Although the research has been funded in part by the US-EPA, it has not been subjected to the Agency's peer and policy review and therefore does not necessarily reflect the views of the Agency. Any remaining error or omission is solely my responsibility. ArticleID:OBES487 istex:B893B2EB225661F3498EB7FAE09E39A2B6266E9D ark:/67375/WNG-NFQ731MD-D I wish to thank Chris Doucouliagos, Stephen Jarrell, Randall Rosenberger, Alex Sutton, and an anonymous referee for their helpful comments. I also gratefully acknowledge the support of a US Environmental Protection Agency STAR (Science To Achieve Results) grant #RD‐832‐421‐01. Although the research has been funded in part by the US‐EPA, it has not been subjected to the Agency's peer and policy review and therefore does not necessarily reflect the views of the Agency. Any remaining error or omission is solely my responsibility. ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0305-9049 1468-0084 |
DOI: | 10.1111/j.1468-0084.2007.00487.x |