A Monte Carlo Comparison of Relative Importance Methodologies
通过蒙特卡洛模拟比较了四种相对重要性指标与一种新方法（优势分析），发现优势分析优于传统指标，且预测变量数量和平均共线性是影响方法间排序差异的关键因素。
This article reports the results of a Monte Carlo simulation comparing four different indices of relative importance (squared correlation, squared beta, product measure, epsilon) to a relatively new method called dominance analysis. Conceptually and empirically, dominance analysis represents an improvement over traditional indices of relative importance. Eight experimental factors were manipulated in the simulations: mean and standard deviation of validity for each predictor, mean and standard deviation of collinearity for two sets of predictors, number of predictors, and presence of simple structure. Of these factors, the number of predictors and the mean collinearity were most strongly related to discrepancies among the rank orders computed using the different importance methods. Across all experimental conditions, the epsilon statistic demonstrated the greatest convergence and beta weights and correlation coefficients the greatest divergence with dominance.