The impact of ignoring random features of predictor and moderator variables on sample size for precise interval estimation of interaction effects

Abstract

The influence of the joint distribution of predictor and moderator variables on the identification of interactions has been well described, but the impact on sample size determinations has received rather limited attention within the framework of moderated multiple regression (MMR). This article investigates the deficiency in sample size determinations for precise interval estimation of interaction effects that can result from ignoring the stochastic nature of continuous predictor and moderator variables in MMR. The primary finding of our examinations is that failure to accommodate the distributional properties of regressors can lead to underestimation of the necessary sample size and distortion of the desired interval precision. In order to take account of the randomness of regressor variables, two general and effective procedures for computing sample size estimates are presented. Moreover, corresponding programs are provided to facilitate use of the suggested approaches. This exposition helps to correct drawbacks in the existing techniques and to advance the practice of reporting confidence intervals in MMR analyses.