Interpreting multiple linear regression: A guidebook of variable importance

Laura Lynn Nathans, Frederick L. Oswald, Kim Nimon

Research output: Contribution to journalArticle

164 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
JournalPractical Assessment, Research and Evaluation
Volume17
Issue number9
StatePublished - 2012

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regression
interpretation
role play
social science

All Science Journal Classification (ASJC) codes

  • Education

Cite this

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Interpreting multiple linear regression : A guidebook of variable importance. / Nathans, Laura Lynn; Oswald, Frederick L.; Nimon, Kim.

In: Practical Assessment, Research and Evaluation, Vol. 17, No. 9, 2012, p. 1-19.

Research output: Contribution to journalArticle

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