Whenever multiple regression is used to test and compare theoretically motivated models, it is of interest to determine the relative importance of the predictors. Specifically, researchers seek to rank order and scale variables in terms of their importance and to express global statistics of the model as a function of these measures. This article reviews the many meanings of importance of predictors in multiple regression, highlights their weaknesses, and proposes a new method for comparing variables: dominance analysis. Dominance is a qualitative relation defined in a pairwise fashion: One variable is said to dominate another if it is more useful than its competitor in all subset regressions. Properties of the newly proposed method are described and illustrated.