Collinearity, Power, and Interpretation of Multiple Regression Analysis
Collinearity, Power, and Interpretation of Multiple Regression Analysis
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DOI：
10.1177/002224379102800302
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发表时间：
1991-08
影响因子：
6.1
通讯作者：
Charlotte H. Mason;W. D. Perreault
中科院分区：
文献类型：
--
作者：
Charlotte H. Mason;W. D. Perreault
多元回归分析是学术和应用营销研究中最广泛使用的统计程序之一。然而，相关的预测变量和潜在的共线性效应是解释回归估计的共同关注点。虽然文献上的方法处理共线性是广泛的，相对较少的努力已作出澄清的条件下，共线性影响的估计开发与多元回归分析或如何显着这些影响。作者报告了旨在解决这些问题的研究。研究结果表明，在许多典型的横截面营销研究的情况下，对共线预测因素的有害影响的担忧往往被夸大了。作者证明，不能孤立地看待共线性。相反，给定水平的共线性的潜在有害影响应与已知影响估计准确性的其他因素结合起来考虑。
Multiple regression analysis is one of the most widely used statistical procedures for both scholarly and applied marketing research. Yet, correlated predictor variables—and potential collinearity effects—are a common concern in interpretation of regression estimates. Though the literature on ways of coping with collinearity is extensive, relatively little effort has been made to clarify the conditions under which collinearity affects estimates developed with multiple regression analysis—or how pronounced those effects are. The authors report research designed to address these issues. The results show, in many situations typical of published cross-sectional marketing research, that fears about the harmful effects of collinear predictors often are exaggerated. The authors demonstrate that collinearity cannot be viewed in isolation. Rather, the potential deleterious effect of a given level of collinearity should be viewed in conjunction with other factors known to affect estimation accuracy.