Abstract
Techniques such as dominance analysis and relative weight analysis have been proposed recently to evaluate more accurately predictor importance in ordinary least squares (OLS) regression. Similar questions of predictor importance also arise in instances where logistic regression is the primary mode of analysis. This article presents an extension of relative weight analysis that can be applied in logistic regression and thus aids in the determination of predictor importance. We briefly review relative importance techniques and then discuss a new procedure for calculating relative importance estimates in logistic regression. Finally, we present a substantive example applying this new approach to an example data set.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 767-781 |
| Number of pages | 15 |
| Journal | Organizational Research Methods |
| Volume | 13 |
| Issue number | 4 |
| DOIs | |
| State | Published - Sep 16 2010 |
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All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Strategy and Management
- Management of Technology and Innovation
Cite this
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Determining the Relative Importance of Predictors in Logistic Regression : An Extension of Relative Weight Analysis. / Tonidandel, Scott; Lebreton, James Marshall.
In: Organizational Research Methods, Vol. 13, No. 4, 16.09.2010, p. 767-781.Research output: Contribution to journal › Article
TY - JOUR
T1 - Determining the Relative Importance of Predictors in Logistic Regression
T2 - An Extension of Relative Weight Analysis
AU - Tonidandel, Scott
AU - Lebreton, James Marshall
PY - 2010/9/16
Y1 - 2010/9/16
N2 - Techniques such as dominance analysis and relative weight analysis have been proposed recently to evaluate more accurately predictor importance in ordinary least squares (OLS) regression. Similar questions of predictor importance also arise in instances where logistic regression is the primary mode of analysis. This article presents an extension of relative weight analysis that can be applied in logistic regression and thus aids in the determination of predictor importance. We briefly review relative importance techniques and then discuss a new procedure for calculating relative importance estimates in logistic regression. Finally, we present a substantive example applying this new approach to an example data set.
AB - Techniques such as dominance analysis and relative weight analysis have been proposed recently to evaluate more accurately predictor importance in ordinary least squares (OLS) regression. Similar questions of predictor importance also arise in instances where logistic regression is the primary mode of analysis. This article presents an extension of relative weight analysis that can be applied in logistic regression and thus aids in the determination of predictor importance. We briefly review relative importance techniques and then discuss a new procedure for calculating relative importance estimates in logistic regression. Finally, we present a substantive example applying this new approach to an example data set.
UR - http://www.scopus.com/inward/record.url?scp=77956454808&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956454808&partnerID=8YFLogxK
U2 - 10.1177/1094428109341993
DO - 10.1177/1094428109341993
M3 - Article
AN - SCOPUS:77956454808
VL - 13
SP - 767
EP - 781
JO - Organizational Research Methods
JF - Organizational Research Methods
SN - 1094-4281
IS - 4
ER -