Relative Importance Analysis: A Useful Supplement to Regression Analysis
Relative Importance Analysis: A Useful Supplement to Regression Analysis
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DOI：
10.1007/s10869-010-9204-3
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发表时间：
2011-03-01
影响因子：
4.8
通讯作者：
LeBreton, James M.
中科院分区：
文献类型：
--
作者：
Tonidandel, Scott;LeBreton, James M.
本文主张更广泛地使用相对重要性指数作为多元回归分析的补充。这种分析的目的是在多个预测值之间划分可解释的方差，以便更好地理解每个预测值在回归方程中所起的作用。不幸的是，当预测因素相互关联时，通常依赖于指标的是具有可变重要性的有缺陷的指标。为此，我们强调了两种相对重要性分析的主要好处，即优势分析和相对权重分析，它们超过了多元回归分析产生的估计。我们还描述了许多应该使用相对重要性权重的情况，同时警告读者关于使用这些权重的限制和误解。最后，我们为有兴趣将这些分析纳入他们自己的工作的研究人员提供了循序渐进的建议，并将他们指向可用的网络资源，以帮助他们产生这些权重。
This article advocates for the wider use of relative importance indices as a supplement to multiple regression analyses. The goal of such analyses is to partition explained variance among multiple predictors to better understand the role played by each predictor in a regression equation. Unfortunately, when predictors are correlated, typically relied upon metrics are flawed indicators of variable importance. To that end, we highlight the key benefits of two relative importance analyses, dominance analysis and relative weight analysis, over estimates produced by multiple regression analysis. We also describe numerous situations where relative importance weights should be used, while simultaneously cautioning readers about the limitations and misconceptions regarding the use of these weights. Finally, we present step-by-step recommendations for researchers interested in incorporating these analyses in their own work and point them to available web resources to assist them in producing these weights.