A Caution Regarding Rules of Thumb for Variance Inflation Factors
Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated w...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
O’brien, Robert M. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Quality & quantity - Springer Netherlands, 1967, 41(2007), 5 vom: 13. März, Seite 673-690 |
|---|---|
| Übergeordnetes Werk: |
volume:41 ; year:2007 ; number:5 ; day:13 ; month:03 ; pages:673-690 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11135-006-9018-6 |
|---|
| Katalog-ID: |
OLC2063487634 |
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| 520 | |a Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. | ||
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10.1007/s11135-006-9018-6 doi (DE-627)OLC2063487634 (DE-He213)s11135-006-9018-6-p DE-627 ger DE-627 rakwb eng 050 VZ 3,4 ssgn O’brien, Robert M. verfasserin aut A Caution Regarding Rules of Thumb for Variance Inflation Factors 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2007 Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. multi-collinearity tolerance variance inflation factors variance of regression coefficients Enthalten in Quality & quantity Springer Netherlands, 1967 41(2007), 5 vom: 13. März, Seite 673-690 (DE-627)129084328 (DE-600)4140-3 (DE-576)014417715 0033-5177 nnns volume:41 year:2007 number:5 day:13 month:03 pages:673-690 https://doi.org/10.1007/s11135-006-9018-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-SOW GBV_ILN_11 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_754 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4325 GBV_ILN_4700 AR 41 2007 5 13 03 673-690 |
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10.1007/s11135-006-9018-6 doi (DE-627)OLC2063487634 (DE-He213)s11135-006-9018-6-p DE-627 ger DE-627 rakwb eng 050 VZ 3,4 ssgn O’brien, Robert M. verfasserin aut A Caution Regarding Rules of Thumb for Variance Inflation Factors 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2007 Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. multi-collinearity tolerance variance inflation factors variance of regression coefficients Enthalten in Quality & quantity Springer Netherlands, 1967 41(2007), 5 vom: 13. März, Seite 673-690 (DE-627)129084328 (DE-600)4140-3 (DE-576)014417715 0033-5177 nnns volume:41 year:2007 number:5 day:13 month:03 pages:673-690 https://doi.org/10.1007/s11135-006-9018-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-SOW GBV_ILN_11 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_754 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4325 GBV_ILN_4700 AR 41 2007 5 13 03 673-690 |
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10.1007/s11135-006-9018-6 doi (DE-627)OLC2063487634 (DE-He213)s11135-006-9018-6-p DE-627 ger DE-627 rakwb eng 050 VZ 3,4 ssgn O’brien, Robert M. verfasserin aut A Caution Regarding Rules of Thumb for Variance Inflation Factors 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2007 Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. multi-collinearity tolerance variance inflation factors variance of regression coefficients Enthalten in Quality & quantity Springer Netherlands, 1967 41(2007), 5 vom: 13. März, Seite 673-690 (DE-627)129084328 (DE-600)4140-3 (DE-576)014417715 0033-5177 nnns volume:41 year:2007 number:5 day:13 month:03 pages:673-690 https://doi.org/10.1007/s11135-006-9018-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-SOW GBV_ILN_11 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_754 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4325 GBV_ILN_4700 AR 41 2007 5 13 03 673-690 |
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10.1007/s11135-006-9018-6 doi (DE-627)OLC2063487634 (DE-He213)s11135-006-9018-6-p DE-627 ger DE-627 rakwb eng 050 VZ 3,4 ssgn O’brien, Robert M. verfasserin aut A Caution Regarding Rules of Thumb for Variance Inflation Factors 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2007 Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. multi-collinearity tolerance variance inflation factors variance of regression coefficients Enthalten in Quality & quantity Springer Netherlands, 1967 41(2007), 5 vom: 13. März, Seite 673-690 (DE-627)129084328 (DE-600)4140-3 (DE-576)014417715 0033-5177 nnns volume:41 year:2007 number:5 day:13 month:03 pages:673-690 https://doi.org/10.1007/s11135-006-9018-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-SOW GBV_ILN_11 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_754 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4325 GBV_ILN_4700 AR 41 2007 5 13 03 673-690 |
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10.1007/s11135-006-9018-6 doi (DE-627)OLC2063487634 (DE-He213)s11135-006-9018-6-p DE-627 ger DE-627 rakwb eng 050 VZ 3,4 ssgn O’brien, Robert M. verfasserin aut A Caution Regarding Rules of Thumb for Variance Inflation Factors 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2007 Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. multi-collinearity tolerance variance inflation factors variance of regression coefficients Enthalten in Quality & quantity Springer Netherlands, 1967 41(2007), 5 vom: 13. März, Seite 673-690 (DE-627)129084328 (DE-600)4140-3 (DE-576)014417715 0033-5177 nnns volume:41 year:2007 number:5 day:13 month:03 pages:673-690 https://doi.org/10.1007/s11135-006-9018-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-SOW GBV_ILN_11 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_754 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4325 GBV_ILN_4700 AR 41 2007 5 13 03 673-690 |
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A Caution Regarding Rules of Thumb for Variance Inflation Factors |
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A Caution Regarding Rules of Thumb for Variance Inflation Factors |
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O’brien, Robert M. |
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a caution regarding rules of thumb for variance inflation factors |
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A Caution Regarding Rules of Thumb for Variance Inflation Factors |
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Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. © Springer 2007 |
| abstractGer |
Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. © Springer 2007 |
| abstract_unstemmed |
Abstract The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index. © Springer 2007 |
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A Caution Regarding Rules of Thumb for Variance Inflation Factors |
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