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Meta-Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection*

Authors


  • *

    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.

Abstract

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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