Residualized Relative Importance Analysis: A Technique for the Comprehensive Decomposition of Variance in Higher Order Regression Models

James M. LeBreton, Scott Tonidandel, Dina V. Krasikova

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

The current article notes that the standard application of relative importance analyses is not appropriate when examining the relative importance of interactive or other higher order effects (e.g., quadratic, cubic). Although there is a growing demand for strategies that could be used to decompose the predicted variance in regression models containing such effects, there has been no formal, systematic discussion of whether it is appropriate to use relative importance statistics in such decompositions, and if it is appropriate, how to go about doing so. The purpose of this article is to address this gap in the literature by describing three different yet related strategies for decomposing variance in higher-order multiple regression models-hierarchical F tests (a between-sets test), constrained relative importance analysis (a within-sets test), and residualized relative importance analysis (a between- and within-sets test). Using a previously published data set, we illustrate the different types of inferences these three strategies permit researchers to draw. We conclude with recommendations for researchers seeking to decompose the predicted variance in regression models testing higher order effects.

Original languageEnglish (US)
Pages (from-to)449-473
Number of pages25
JournalOrganizational Research Methods
Volume16
Issue number3
DOIs
StatePublished - Jul 1 2013

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation

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