The Accuracy of Dominance Analysis as a Metric to Assess Relative Importance: The Joint Impact of Sampling Error Variance and Measurement Unreliability

Dominance analysis (DA) has been established as a useful tool for practitioners and researchers to identify the relative importance of predictors in a linear regression. This article examines the joint impact of two common and pervasive artifacts-sampling error variance and measurement unreliability-on the accuracy of DA. We present Monte Carlo simulations that detail the decrease in the accuracy of DA in the presence of these artifacts, highlighting the practical extent of the inferential mistakes that can be made. Then, we detail and provide a user-friendly program in R (R Core Team, 2017) for estimating the effects of sampling error variance and unreliability on DA. Finally, by way of a detailed example, we provide specific recommendations for how researchers and practitioners should more appropriately interpret and report results of DA.