Using Prior Information to Plan Appropriately Powered Regression Studies: A Tutorial Using BUCSS

Despite increased attention to the role of statistical power in psychological studies, navigating the process of sample size planning for linear regression designs can be challenging. In particular, it can be difficult to decide upon an appropriate value for the effect size, owing to a variety of factors, including the influence of the correlations among the predictors and between the other predictors and the outcome, in addition to the correlation between the particular predictor(s) in question and the outcome, on statistical power. One approach that addresses these concerns is to use available prior sample information but adjust the sample effect size appropriately for publication bias and/or uncertainty. This article motivates a procedure that accomplishes this, Bias Uncertainty Corrected Sample Size (BUCSS), as a valid approach for linear regression, carefully illustrating how BUCSS may be used in practice. To demonstrate the relevant factors influencing BUCSS performance and ensure it performs well in plausible regression contexts, a Monte Carlo simulation is reported. Importantly, the present difficulties in sample size planning for regression are explained, followed by clear illustrations using BUCSS software for a variety of common practical scenarios in regression studies.