Interpreting Multiple Linear Regression: A Guidebook of Variable Importance
DOI
https://doi.org/10.7275/5fex-b874
Abstract
Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights (cf. Courville & Thompson, 2001; Nimon, Roberts, & Gavrilova, 2010; Zientek, Capraro, & Capraro, 2008), often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what and how independent variables contribute to a regression equation. Thus, this paper presents a guidebook of variable importance measures that inform MR results, linking measures to a theoretical framework that demonstrates the complementary roles they play when interpreting regression findings. We also provide a data-driven example of how to publish MR results that demonstrates how to present a more complete picture of the contributions variables make to a regression equation. We end with several recommendations for practice regarding how to integrate multiple variable importance measures into MR analyses. Accessed 103,722 times on https://pareonline.net from April 29, 2012 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Nathans, Laura L.; Oswald, Frederick L.; and Nimon, Kim
(2019)
"Interpreting Multiple Linear Regression: A Guidebook of Variable Importance,"
Practical Assessment, Research, and Evaluation: Vol. 17, Article 9.
DOI: https://doi.org/10.7275/5fex-b874
Available at:
https://scholarworks.umass.edu/pare/vol17/iss1/9