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The Use of Meta-Analytic Statistical Significance Testing
Research Synthesis Methods
  • Terri D. Pigott, Loyola University Chicago
  • Joshua R. Polanin, Loyola University Chicago
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Meta-analysis multiplicity, the concept of conducting multiple tests of statistical significance within one study, is an underdeveloped literature (Tendal, Nüesch, Higgins, Jüni, & Gøtzsche, 2011). We address this issue by considering how Type I errors can impact meta-analytic results, suggest how statistical power may be affected through the use of multiplicity corrections, and propose how meta-analysts should analyze multiple tests of statistical significance. The context for this study is a meta-review of meta-analyses published in two leading review journals in education and psychology. Our review of 130 meta-analyses revealed a strong reliance on statistical significance testing without considering of Type I errors or the use of multiplicity corrections. In order to provide valid conclusions, meta-analysts must consider these issues prior to conducting the study.


Author Posting. © 2014 John Wiley & Sons, Ltd. This is the author's version of the work. It is posted here by permission of Wiley for personal use, not for redistribution. The definitive version was published in Research Synthesis Methods Volume 6, Issue 1, pages 63–73, March 2015.

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Creative Commons Attribution-Noncommercial-No Derivative Works 3.0
Citation Information
Terri D. Pigott and Joshua R. Polanin. "The Use of Meta-Analytic Statistical Significance Testing" Research Synthesis Methods Vol. 6 Iss. 1 (2015)
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