Article

Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

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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.

Keywords: Educational Research, Research Methodology, Statistical Analysis

How to Cite:

Nathans, L. L., Oswald, F. L. & Nimon, K., (2012) “Interpreting Multiple Linear Regression: A Guidebook of Variable Importance”, Practical Assessment, Research, and Evaluation 17(1): 9. doi: https://doi.org/10.7275/5fex-b874