Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

作者：Amanda Kraha, Heather Turner, Kim Nimon, Linda Reichwein Zientek, Robin K. Henson · 发表于：Frontiers in Psychology · 年份：2012 · DOI：10.3389/fpsyg.2012.00044 · 被引用次数：368 · 研究领域：Advanced Statistical Methods and Models、Statistical Methods and Applications、Spectroscopy and Chemometric Analyses

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.