Determining the Relative Importance of Predictors in Logistic Regression: An Extension of Relative Weight Analysis

By Scott Tonidandel, James M. LeBreton. 2010

149 citations recorded by the source corpus.

Subfields: Food Science, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/09f121e716863b7eac35ff6f6fb2c39a69d51f81

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. Scott Tonidandel
2. F. Oswald
3. Kim F. Nimon
4. Laura L. Nathans
5. James M. LeBreton
6. Jeffery S. McMullen
7. Alexander S. Kier
8. Crystal L. Park

In Management Science and Operations Research

1. F. Oswald
2. Kim F. Nimon
3. Scott Tonidandel
4. Laura L. Nathans
5. James M. LeBreton
6. Konstantin P. Cigularov
7. K. Hoffmeister
8. A. Gibbons

In Statistics and Probability

1. F. Oswald
2. Kim F. Nimon
3. Scott Tonidandel
4. Laura L. Nathans
5. James M. LeBreton
6. Peter Y. Chen
7. A. Gibbons
8. Konstantin P. Cigularov

Citations in the corpus

• Relative Importance Analysis: A Useful Supplement to Regression Analysis (2011, methodology): “Although both dominance analysis and relative weight analysis were developed for use with OLS regression, Azen and Traxel (2009) and Tonidandel and LeBreton (2010) presented modifications of these respective analyses to handle categorical criterion variables that would typically be analyzed using ”
• Interpreting Multiple Linear Regression: A Guidebook of Variable Importance (2012, methodology): “…are correlated, relative weights address this problem by using principal components analysis to transform the original independent variables into a set of uncorrelated principal components that are most highly correlated with the original independent variables (Tonidandel & LeBreton, 2010).”
• RWA Web: A Free, Comprehensive, Web-Based, and User-Friendly Tool for Relative Weight Analyses (2014, methodology): “…and Tonidandel 2008), regression models containing higher order terms such as cross-product terms, quadratic terms, or other polynomial terms (LeBreton et al. 2013), logistic regression (Tonidandel and LeBreton 2010), and multivariate analysis of variance (MANOVA; Toni-dandel and LeBreton 2013).”
• The differential effects of transformational leadership facets on employee safety (2014, methodology): “As noted earlier, to examine the relationships between leadership variables and work-related injuries and pain, we used logistic regression and logistic relative weights analysis (Tonidandel and LeBreton, 2010).”