CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION

CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION 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 r 2 ≥≥ 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecology Ecological Society of America

CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION

Ecology, Volume 84 (11) – Nov 1, 2003

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Publisher
Ecological Society of America
Copyright
Copyright © 2003 by the Ecological Society of America
Subject
Statistical Reports
ISSN
0012-9658
D.O.I.
10.1890/02-3114
Publisher site
See Article on Publisher Site

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 r 2 ≥≥ 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.

Journal

EcologyEcological Society of America

Published: Nov 1, 2003

Keywords: confounding factors ; multicollinearity ; multiple regression ; principal components regression ; sequential regression ; structural equation modeling

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