Statistical Power Computations for Detecting Dichotomous Moderator Variables with Moderated Multiple Regression

Abstract

A revised and improved version of Aguinis, Pierce, and Stone-Romero's (1994) program for estimating the statistical power of moderated multiple regression to detect dichotomous moderator variables is described. The Quick BASIC program runs on IBM and IBM-compatible personal computers and estimates power based on user-provided values for (a) total sample size, (b) sample sizes across the two moderator-based subgroups, (c) correlation coefficients between the predictor and criterion for each of the two moderator-based subgroups, (d) correlation coefficient between the predictor and hypothesized moderator, and (e) sample and population standard deviations for the predictor. Program-generated power estimates for typical research situations in education, psychology, and management indicate that hypothesis tests of moderating effects are typically conducted at insufficient levels of statistical power.