Calculating the Relative Importance of Multiple Regression Predictor Variables Using Dominance Analysis and Random Forests

Autor/in	Mizumoto, Atsushi
Titel	Calculating the Relative Importance of Multiple Regression Predictor Variables Using Dominance Analysis and Random Forests
Quelle	In: Language Learning, 73 (2023) 1, S.161-196 (36 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Mizumoto, Atsushi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0023-8333
DOI	10.1111/lang.12518
Schlagwörter	Predictor Variables; Artificial Intelligence; Evaluation Methods; Multiple Regression Analysis; Correlation; Second Language Learning; Language Research + Suchen Sie Ihr Suchwort? Prädiktor; Künstliche Intelligenz; Korrelation; Zweitsprachenerwerb; Sprachforschung
Abstract	Researchers often make claims regarding the importance of predictor variables in multiple regression analysis by comparing standardized regression coefficients (standardized beta coefficients). This practice has been criticized as a misuse of multiple regression analysis. As a remedy, I highlight the use of dominance analysis and random forests, a machine learning technique, in this method showcase article for accurately determining predictor importance in multiple regression analysis. To demonstrate the utility of dominance analysis and random forests, I reproduced the results of an empirical study and applied these analytical procedures. The results reconfirmed that multiple regression analysis should always be accompanied by dominance analysis and random forests to identify the unique contribution of individual predictors while considering correlations among predictors. I also introduce a web application for facilitating the use of dominance analysis and random forests among second language researchers. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01