Suppression situations in multiple linear regression

Gwowen Shieh*

*Corresponding author for this work

研究成果: Review article同行評審

24 引文 斯高帕斯(Scopus)


This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are incomplete and oversimplified. The proposed approach also allows a natural extension for multiple regression with more than two predictor variables. It is shown that the conditions under which both types of suppression can occur are not fully congruent with the significance of the partial F test. This implies that all the standard variable selection techniques - backward elimination, forward selection, and stepwise regression procedures - can fail to detect suppression situations. This also explains the controversial findings in the redundancy or importance of correlated variables in applied settings. Furthermore, informative visual representations of various aspects of these phenomena are provided.

頁(從 - 到)435-447
期刊Educational and Psychological Measurement
出版狀態Published - 六月 2006

指紋 深入研究「Suppression situations in multiple linear regression」主題。共同形成了獨特的指紋。