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Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression.
Abstract
Whenever multiple regression is used to test and compare theoretically motivated models, it is of interest to determine the relative importance of the predictors. Specifically, researchers seek to rank order and scale variables in terms of their importance and to express global statistics of the model as a function of these measures. This article reviews the many meanings of importance of predictors in multiple regression, highlights their weaknesses, and proposes a new method for comparing variables: dominance analysis. Dominance is a qualitative relation defined in a pairwise fashion: One variable is said to dominate another if it is more useful than its competitor in all subset regressions. Properties of the newly proposed method are described and illustrated
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“This issue of relative importance can be overcome through the method of dominance analysis (Budescu, 1993; Mizumoto, 2022 ), which 'approaches the problem of relative importance by examining the change in R 2 resulting from adding a predictor to all possible subset regression models' (Tonidandel & LeBreton, 2011, p. 3).”
From A Three-Body Problem: The effects of foreign language anxiety, enjoyment, and boredom on academic achievement (2023) · cites this paper as Budescu, 1993
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Metadata
- Authors
- Budescu, D. V.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Budescu, David V.
- ORCID
- Budescu, David V.
- Venue
- Psychological Bulletin
- Published
- 1993-11
- Volume
- 114
- Issue
- 3
- Pages
- 542-551
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W2085175893
- Cited by
- 1,193
- References
- 30
- Citations by year
- 2026: 81 · 2025: 82 · 2024: 105 · 2023: 84 · 2022: 97 · 2021: 89 · 2020: 78 · 2019: 63 · 2018: 56 · 2017: 50 · 2016: 45 · 2015: 36 · 2014: 47 · 2013: 34 · 2012: 50
- FWCI
- 2.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Bulletin (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- dominance analysis, multiple linear regression, feature ranking, variable scaling
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