Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/ecological-society-of-america/confronting-multicollinearity-in-ecological-multiple-regression-tPHn1ztLWw
References (21)
-
P. Petraitis, A. Dunham, P. Niewiarowski (1996)
Inferring multiple causality : the limitations of path analysisFunctional Ecology, 10
-
M. Kutner (1975)
Applied Linear Statistical Models -
J. Snell, D. Birkes, Y. Dodge (1996)
6. Alternative Methods of RegressionJournal of The Royal Statistical Society Series A-statistics in Society, 159
-
B. Shipley (1999)
Testing causal explanations in organismal biology : causation, correlation and structural equation modellingOikos, 86
-
B. Carnes, N. Slade (1988)
The Use of Regression for Detecting Competition with Multicollinear DataEcology, 69
-
P. Bentler (1989)
EQS : structural equations program manual -
J. Bishop, D. Schemske (1998)
Variation in flowering phenology and its consequences for lupines colonizing Mount St. HelensEcology, 79
-
S. Chatterjee, A. Hadi, Bertram Price (1979)
Regression Analysis by ExampleBioScience
-
L. Hayduk (1987)
Structural equation modeling with LISREL: essentials and advancesSocial Forces, 14
-
H. Scheffé (1960)
The Analysis of Variance -
D. Brown, A. Weis (1995)
Direct and indirect effects of prior grazing of goldenrod upon the performance of a leaf beetleEcology, 76
-
K. Bollen (1989)
Structural Equations with Latent Variables -
M. Graham (1997)
Factors determining the upper limit of giant kelp, Macrocystis pyrifera Agardh, along the Monterey Peninsula, central California, USAJournal of Experimental Marine Biology and Ecology, 218
-
F. James, C. McCulloch (1990)
MULTIVARIATE ANALYSIS IN ECOLOGY AND SYSTEMATICS: PANACEA OR PANDORA'S BOX?Annual Review of Ecology, Evolution, and Systematics, 21
-
B. Tabachnick, L. Fidell (1983)
Using Multivariate Statistics -
T. Mitchell-Olds, R. Shaw (1987)
REGRESSION ANALYSIS OF NATURAL SELECTION: STATISTICAL INFERENCE AND BIOLOGICAL INTERPRETATIONEvolution, 41
-
P. Sprent, N. Draper, Harry Smith (1967)
Applied Regression Analysis.Biometrics, 23
-
R. Hocking (2003)
Methods and Applications of Linear Models: Regression and the Analysis of Variance -
G. Furnival (1971)
All Possible Regressions with Less ComputationTechnometrics, 13
-
L. Gough, L. Gough, J. Grace (1999)
EFFECTS OF ENVIRONMENTAL CHANGE ON PLANT SPECIES DENSITY: COMPARING PREDICTIONS WITH EXPERIMENTSEcology, 80
-
(1993)
Multiple regression : herbivory
- Publisher
- Ecological Society of America
- Copyright
- Copyright © 2003 by the Ecological Society of America
- Subject
- Statistical Reports
- ISSN
- 0012-9658
- DOI
- 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
Ecology – Ecological Society of America
Published: Nov 1, 2003
Keywords: confounding factors ; multicollinearity ; multiple regression ; principal components regression ; sequential regression ; structural equation modeling