Using prior information to plan appropriately powered regression studies: A tutorial using BUCSS.

Research output: Contribution to journalArticlepeer-review

Abstract

Statistical power is the probability that a scientific study can detect a purported effect using significance testing methods, assuming that the effect truly exists. Because larger sample sizes are associated with higher statistical power, sample size planning is recommended. There are many types of effect sizes, which measure effect magnitude, and researchers need to determine an appropriate effect size value to input when conducting sample size planning. However, this process can be especially difficult for researchers using linear regression analyses because of the many factors that influence the value and interpretation of the effect size, among other reasons. A potential solution is to use sample effect size information reported in prior research. Unfortunately, the sample effect size should not be used directly in sample size planning. Sample effect sizes from published studies are too high, owing to publication bias, and irrespective of publication, contain uncertainty, because the effect sizes are only estimates of the true, unknown effect size. In this article, an approach, Bias Uncertainty Corrected Sample Size (BUCSS), is demonstrated as a particularly valid method to calculate sample size for regression studies. This tutorial contains a demonstration of BUCSS software in addition to three step-by-step examples of using BUCSS in common regression-based scenarios. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Original languageEnglish (US)
JournalPsychological Methods
DOIs
StateAccepted/In press - 2020
Externally publishedYes

Keywords

  • multiple regression
  • sample size
  • statistical power
  • study design

ASJC Scopus subject areas

  • Psychology (miscellaneous)

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