2007
DOI: 10.1007/s11135-006-9018-6
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A Caution Regarding Rules of Thumb for Variance Inflation Factors

Abstract: multi-collinearity, tolerance, variance inflation factors, variance of regression coefficients,

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Cited by 7,014 publications
(3,703 citation statements)
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References 8 publications
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“…Only two variables in one set of regressions exceed this threshold (SOM Table S5). It appears that previously suggested alternatives to multiple regression have statistical effects that may be worse than using a multiple regression with mutually correlated predictors (O'Brien, 2007).…”
Section: Analytical Frameworkmentioning
confidence: 99%
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“…Only two variables in one set of regressions exceed this threshold (SOM Table S5). It appears that previously suggested alternatives to multiple regression have statistical effects that may be worse than using a multiple regression with mutually correlated predictors (O'Brien, 2007).…”
Section: Analytical Frameworkmentioning
confidence: 99%
“…Regressions using either one or multiple independent variables were run. When multiple regressions were run, we also assessed multi-colinearity of the independent (explanatory) variables with Tolerance and Variance Inflation Factors (O'Brien, 2007). Caper was also used to run phylogenetically informed t-tests evaluating whether taxa with a consistently patent promontorial artery have a smaller ratio of stapedial canal area to promontorial canal area than taxa that frequently have an involuted promontorial artery.…”
Section: Statistical Analysesmentioning
confidence: 99%
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“…Before conducting regression models, multicollinearity was checked using the variance inflation factor (VIF) for each explanatory variable. No problem of multicollinearity was detected, as the highest VIF value (all VIFs < 1.30) was well below the thresholds (≥ 5 or ≥ 10) generally considered as evidence of multicollinearity (see O’brien, 2007). …”
Section: Methodsmentioning
confidence: 95%
“…A tolerance of less than 0.20 or 0.10 and/or a VIF of 5 or 10 and above were shown to indicate a multicollinearity problem [2]. In the statistical program we use (SPSS), VIF and tolerance values are used to detect multicollinearity in linear regression analysis; however, in the logistic regression analysis the "correlation of estimates" test automatically removes those…”
mentioning
confidence: 99%