Extending Egger's Regression: Detecting Outcome Reporting Bias in Meta-Analysis on Dependent Multiple Outcomes.

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Bibliographic Details
Title: Extending Egger's Regression: Detecting Outcome Reporting Bias in Meta-Analysis on Dependent Multiple Outcomes.
Authors: Park, Sunyoung (AUTHOR), Beretvas, S. Natasha (AUTHOR), Smith, Tyler E. (AUTHOR)
Source: Journal of Experimental Education. 2026, Vol. 94 Issue 4, p1066-1104. 39p.
Subjects: Research bias, Multivariate analysis, Simulation methods & models, Evidence synthesis, Statistical hypothesis testing
Abstract: Outcome reporting bias (ORB) is a form of publication bias resulting from a primary study authors' reporting results for significant outcomes in a meta-analysis (Rothstein et al., 2006). The current study extended Egger's regression test while handling the dependence from multiple effect sizes per study. We compared methods for handling within-study dependence simultaneously (multivariate) versus separately (univariate) and compared the performance of estimation models including RVE, MLMA, and MLMA-RVE. In addition to real data analysis examples, the simulation study explored five characteristics of meta-analytic data in their impact on results. According to the findings, the multivariate model enhances power and the use of RVE or MLMA+RVE with multivariate models worked best. More results, limitations, and directions for future research are discussed. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
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