The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
How meta-analysis increases statistical power.
Abstract
One of the most frequently cited reasons for conducting a meta-analysis is the increase in statistical power that it affords a reviewer. This article demonstrates that fixed-effects meta-analysis increases statistical power by reducing the standard error of the weighted average effect size (T.) and, in so doing, shrinks the confidence interval around T.. Small confidence intervals make it more likely for reviewers to detect nonzero population effects, thereby increasing statistical power. Smaller confidence intervals also represent increased precision of the estimated population effect size. Computational examples are provided for 3 effect-size indices: d (standardized mean difference), Pearson's r, and odds ratios. Random-effects meta-analyses also may show increased statistical power and a smaller standard error of the weighted average effect size. However, the authors demonstrate that increasing the number of studies in a random-effects meta-analysis does not always increase statistical power.
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Citations
Cites
Distribution Theory for Glass's Estimator of Effect Size and Related Estimators
Hedges, L. V. (1981) · Journal of Educational Statistics · doi:10.2307/1164588 · Cited by 5,695
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Schmidt, F. L., & Hunter, J. E. (2015) · doi:10.4135/9781483398105 · Cited by 5,542
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The interpretation of interaction in contingency tables
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“Indeed the latter strategy is not recommended (Bravata & Olkin, 2002) and can result in outcomes characterized by Simpson’s paradox (Simpson, 1951).”
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Cumulative Meta-Analysis of Therapeutic Trials for Myocardial Infarction
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“The cumulative meta-analyses reported above illustrate the relationships between sample size, metaanalysis summaries, and statistical power; the nature of power in meta-analysis can also be discerned in other cumulative meta-analyses (e.g., Lau et al., 1992).”
BackgroundMethodHunter, J. E., & Schmidt, F. L. (2000) · International Journal of Selection and Assessment · doi:10.1111/1468-2389.00156 · Cited by 833 · cited 3×
“In contrast, a random-effects analysis assumes that the effect sizes included in a review are heterogeneous and sampled from a distribution of population effect sizes (Hedges & Vevea, 1998; Hunter & Schmidt, 2000).”
BackgroundMethodDo studies of statistical power have an effect on the power of studies?
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The power of statistical tests in meta-analysis
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“…size is positive yet some of the individual population effect sizes may be zero or negative, which " corresponds to the substantive idea that some realizations of the treatment may actually be harmful even if the average effect of the treatment is beneficial " (Hedges & Pigott, 2001, p. 211).”
BackgroundInfluential
Cited by
Unemployment impairs mental health: Meta-analyses
Paul, K. I., & Moser, K. (2009) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2009.01.001 · Cited by 2,706
Siddaway, A. P., Wood, A. M., & Hedges, L. V. (2019) · Annual Review of Psychology · doi:10.1146/annurev-psych-010418-102803 · Cited by 2,550
Miller, G. E., Chen, E., & Zhou, E. S. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.1.25 · Cited by 2,467
Ellis, P. D. (2010) · doi:10.1017/cbo9780511761676 · Cited by 2,461
How Many Studies Do You Need?: A Primer on Statistical Power for Meta-Analysis
Valentine, J. C., Pigott, T. D., & Rothstein, H. R. (2010) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998609346961 · Cited by 1,294
Metadata
- Authors
- Cohn, L. D., & Becker, B. J.
- Venue
- Psychological Methods
- Published
- 2003
- Volume
- 8
- Issue
- 3
- Pages
- 243-253
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2147234629
- Cited by
- 498
- References
- 10
- FWCI
- 9.04
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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