The Random Forests Statistical Technique: An Examination of Its Value for the Study of Reading

Autor/inn/en	Matsuki, Kazunaga; Kuperman, Victor; Van Dyke, Julie A.
Titel	The Random Forests Statistical Technique: An Examination of Its Value for the Study of Reading
Quelle	In: Scientific Studies of Reading, 20 (2016) 1, S.20-33 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1088-8438
DOI	10.1080/10888438.2015.1107073
Schlagwörter	Reading Ability; Statistical Analysis; Research Methodology; Inferences; Eye Movements; Reading Processes; Reading Comprehension; Decision Making; Undergraduate Students; Cognitive Ability; Verbal Ability; Oral Reading; Reading Tests; Predictor Variables; Decoding (Reading); Reading Rate; Vocabulary; Time; Goodness of Fit; Models; Regression (Statistics); Intelligence Tests; Scores; Gray Oral Reading Test + Suchen Sie Ihr Suchwort? Reading competence; Lesekompetenz; Statistische Analyse; Research method; Forschungsmethode; Inference; Inferenz; Augenbewegung; Leseprozess; Leseverstehen; Decision-making; Entscheidungsfindung; Denkfähigkeit; Mündliche Leistung; Oral work; Reading; Mündliche Übung; Lesen; Lesetest; Prädiktor; Dekodierung; Reading readiness; Reading speed; Lesegeschwindigkeit; Wortschatz; Zeit; Analogiemodell; Regression; Regressionsanalyse; Intelligence test; Intelligenztest
Abstract	Studies investigating individual differences in reading ability often involve data sets containing a large number of collinear predictors and a small number of observations. In this article, we discuss the method of Random Forests and demonstrate its suitability for addressing the statistical concerns raised by such data sets. The method is contrasted with other methods of estimating relative variable importance, especially Dominance Analysis and Multimodel Inference. All methods were applied to a data set that gauged eye-movements during reading and offline comprehension in the context of multiple ability measures with high collinearity due to their shared verbal core. We demonstrate that the Random Forests method surpasses other methods in its ability to handle model overfitting and accounts for a comparable or larger amount of variance in reading measures relative to other methods. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01