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CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION
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.
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Cited by
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., et al. (2013) · Ecography · doi:10.1111/j.1600-0587.2012.07348.x · Cited by 9,998
Texas A&M International University, Kock, N., Lynn, G., & Stevens Institute of Technology (2012) · Journal of the Association for Information Systems · doi:10.17705/1jais.00302 · Cited by 3,783
A brief introduction to mixed effects modelling and multi-model inference in ecology
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2018) · PeerJ · doi:10.7717/peerj.4794 · Cited by 2,094
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies
P. Vatcheva, K., & Lee, M. (2016) · Epidemiology: Open Access · doi:10.4172/2161-1165.1000227 · Cited by 1,291
Thompson, C. G., Kim, R. S., Aloe, A. M., & Becker, B. J. (2017) · Basic and Applied Social Psychology · doi:10.1080/01973533.2016.1277529 · Cited by 1,092
Metadata
- Authors
- Graham, M. H.
- Venue
- Ecology
- Publisher
- Wiley
- Published
- 2003-11
- Volume
- 84
- Issue
- 11
- Pages
- 2809-2815
- ISSN
- 0012-9658 (ISSN-L)
- OpenAlex
- W2163614163
- Cited by
- 2,536
- References
- 0
- FWCI
- 28.69
- Type
- journal article
- Language
- en
- Open access
- closed
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- Graham, M. H. (2003). CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION. Ecology. https://doi.org/10.1890/02-3114
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