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Henson, Robin K.; Natesan, Prathiba; Axelson, Erika D. – Journal of Experimental Education, 2014
The authors examined the distributional properties of 3 improvement-over-chance, I, effect sizes each derived from linear and quadratic predictive discriminant analysis and from logistic regression analysis for the 2-group univariate classification. These 3 classification methods (3 levels) were studied under varying levels of data conditions,…
Descriptors: Effect Size, Probability, Comparative Analysis, Classification
Vaughn, Brandon K.; Wang, Qiu – Educational and Psychological Measurement, 2010
A nonparametric tree classification procedure is used to detect differential item functioning for items that are dichotomously scored. Classification trees are shown to be an alternative procedure to detect differential item functioning other than the use of traditional Mantel-Haenszel and logistic regression analysis. A nonparametric…
Descriptors: Test Bias, Classification, Nonparametric Statistics, Regression (Statistics)

Wilcox, Rand R. – Educational and Psychological Measurement, 1980
Using three sets of real data, a comparison of four discrete discriminate analysis procedures is made using the actual versus the optimal error rate. The kernel method gives the most accurate results in all three cases. (Author/RL)
Descriptors: Achievement Tests, Comparative Analysis, Discriminant Analysis, Error of Measurement
Lei, Pui-Wa; Koehly, Laura M. – Journal of Experimental Education, 2003
Classification studies are important for practitioners who need to identify individuals for specialized treatment or intervention. When interventions are irreversible or misclassifications are costly, information about the proficiency of different classification procedures becomes invaluable. This study furnishes information about the relative…
Descriptors: Monte Carlo Methods, Classification, Discriminant Analysis, Regression (Statistics)