Confronting multicollinearity in ecological multiple regression

Downloaded 5963 times.

Primary tabs

Graham, M. H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology, 84(11), 2809-2815.
TitleConfronting multicollinearity in ecological multiple regression
AuthorsH. Graham
AbstractThe 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.
Start page2809
End page2815
Subjectscommunity ecology, multiple regression, statistical analysis
NoteCited By (since 1996):503, CODEN: ECOLA