Misclassification Rates for Four Methods of Group Classification: Impact of Predictor Distribution, Covariance Inequality, Effect Size, Sample Size, and Group Size Ratio

Autor/inn/en	Finch, W. Holmes; Schneider, Mercedes K.
Titel	Misclassification Rates for Four Methods of Group Classification: Impact of Predictor Distribution, Covariance Inequality, Effect Size, Sample Size, and Group Size Ratio
Quelle	In: Educational and Psychological Measurement, 66 (2006) 2, S.240-257 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164405278579
Schlagwörter	Classification; Sample Size; Effect Size; Discriminant Analysis; Regression (Statistics); Comparative Analysis; Monte Carlo Methods; Predictor Variables; Models; Correlation; Measurement Techniques + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Diskriminanzanalyse; Regression; Regressionsanalyse; Monte-Carlo-Methode; Prädiktor; Analogiemodell; Korrelation; Messtechnik
Abstract	This study compares the classification accuracy of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR), and classification and regression trees (CART) under a variety of data conditions. Past research has generally found comparable performance of LDA and LR, with relatively less research on QDA and virtually none on CART. This study uses Monte Carlo simulations to assess the crossvalidated predictive accuracy of these methods, while manipulating such factors as predictor distribution, sample size, covariance matrix inequality, group separation, and group size ratio. The results indicate that QDA performs as well as or better than the other alternatives in virtually all conditions. Suggestions for practitioners are provided. (Contains 1 figure and 6 tables.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10