CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION

EcologyVolume 84, Issue 11 p. 2809-2815 Statistical Report CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION Michael H. Graham, Michael H. Graham Moss Landing Marine Laboratories, 8272 Moss Landing Road, Moss Landing, California 95039 USA E-mail: [email protected]Search for more papers by this author Michael H. Graham, Michael H. Graham Moss Landing Marine Laboratories, 8272 Moss Landing Road, Moss Landing, California 95039 USA E-mail: [email protected]Search for more papers by this author First published: 01 November 2003 https://doi.org/10.1890/02-3114Citations: 1,619 Corresponding Editor: A. M. Ellison Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers analyses and threatens their statistical and inferential interpretation. Using numerical simulations, I quantified the impact of multicollinearity on ecological multiple regression and found that even low levels of collinearity bias analyses (r ≥ 0.28 or r2 ≥ 0.08), causing (1) inaccurate model parameterization, (2) decreased statistical power, and (3) exclusion of significant predictor variables during model creation. Then, using real ecological data, I demonstrated the utility of various statistical techniques for enhancing the reliability and interpretation of ecological multiple regression in the presence of multicollinearity. Citing Literature Supporting Information Filename Description https://dx.doi.org/10.6084/m9.figshare.c.3297932 Research data pertaining to this article is located at figshare.com: Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume84, Issue11November 2003Pages 2809-2815 RelatedInformation