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Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty.

Authors
  • Anderson, Samantha F1
  • Kelley, Ken1
  • Maxwell, Scott E1
  • 1 University of Notre Dame.
Type
Published Article
Journal
Psychological Science
Publisher
SAGE Publications
Publication Date
Nov 01, 2017
Volume
28
Issue
11
Pages
1547–1562
Identifiers
DOI: 10.1177/0956797617723724
PMID: 28902575
Source
Medline
Keywords
License
Unknown

Abstract

The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typically not accurate estimates of the population effect size because of publication bias and uncertainty. We show that the use of this approach often results in underpowered studies, sometimes to an alarming degree. We present an alternative approach that adjusts sample effect sizes for bias and uncertainty, and we demonstrate its effectiveness for several experimental designs. Furthermore, we discuss an open-source R package, BUCSS, and user-friendly Web applications that we have made available to researchers so that they can easily implement our suggested methods.

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