Viewing Multiple Linear Regression Models through Multiple Lenses

• In Event: Advanced Topics in Multiple Regression Analysis

Abstract

This paper describes multiple methods of assessing variable importance in multiple regression analyses. Our paper is structured by (a) defining each measure, (b) highlighting each measure’s unique advantages and limitations, (c) describing how each measure can help identify suppression effects, and (d) outlining specific research questions that each measure can address. This guidebook should serve as a practical resource for researchers to define and identify the ‘best’ measure or set of measures with which to analyze their data using multiple regression. In conclusion, we present a data-driven example and results section that researchers can use as a template for their own use in reporting and interpreting multiple regression findings.

Authors

• Laura Nathans, University of North Texas

• Fred Oswald, Rice University

• Kim Nimon, University of North Texas