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

Scott Tonidandel, James Marshall Lebreton

Research output: Contribution to journalArticle

39 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)767-781
Number of pages15
JournalOrganizational Research Methods
Volume13
Issue number4
DOIs
StatePublished - Sep 16 2010

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Logistics
Predictors
Relative weight
Relative importance
Logistic regression
Ordinary least squares
Dominance analysis

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 journalArticle

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