Complexities in power analysis: Quantifying uncertainties with a Bayesian-classical hybrid approach

Psychological Methods
Jolynn Pek, Joonsuk Park

Abstract

Power analysis serves as the gold standard for evaluating study feasibility and justifying sample size. However, mainstream power analysis is often oversimplified, poorly reflecting complex reality during data analysis. This article highlights the complexities inherent in power analysis, especially when uncertainties present in data analysis are realistically taken into account. We introduce a Bayesian-classical hybrid approach to power analysis, which formally incorporates three sources of uncertainty into power estimates: (a) epistemic uncertainty regarding the unknown values of the effect size of interest, (b) sampling variability, and (c) uncertainty due to model approximation (i.e., models fit data imperfectly; Box, 1979; MacCallum, 2003). To illustrate the nature of estimated power from the Bayesian-classical hybrid method, we juxtapose its power estimates with those obtained from traditional (i.e., classical or frequentist) and Bayesian approaches. We employ an example in lexical processing (e.g., Yap & Seow, 2014) to illustrate underlying concepts and provide accompanying R and Rcpp code for computing power via the Bayesian-classical hybrid method. In general, power estimates become more realistic and much more varied a...Continue Reading

Citations

Jul 28, 2020·Personality and Social Psychology Review : an Official Journal of the Society for Personality and Social Psychology, Inc·Leandre R FabrigarRichard E Petty
Jul 27, 2021·Personality & Social Psychology Bulletin·Duane T WegenerKathryn Hoisington-Shaw

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