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Showing 1 to 15 of 18 results Save | Export
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Grapin, Sally L.; Kranzler, John H.; Waldron, Nancy; Joyce-Beaulieu, Diana; Algina, James – Psychology in the Schools, 2017
This study evaluated the classification accuracy of a second grade oral reading fluency curriculum-based measure (R-CBM) in predicting third grade state test performance. It also compared the long-term classification accuracy of local and publisher-recommended R-CBM cut scores. Participants were 266 students who were divided into a calibration…
Descriptors: Oral Reading, Reading Fluency, Cutting Scores, Classification
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Nokelainen, Petri; Silander, Tomi – Frontline Learning Research, 2014
This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is…
Descriptors: Models, Classification, Accuracy, Regression (Statistics)
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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
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Heiny, Robert L.; Heiny, Erik L.; Raymond, Karen – Journal of College Student Retention: Research, Theory & Practice, 2017
Two approaches, Linear Discriminant Analysis, and Logistic Regression are used and compared to predict success or failure for first-time freshmen in the first calculus course at a medium-sized public, 4-year institution prior to Fall registration. The predictor variables are high school GPA, the number, and GPA's of college prep mathematics…
Descriptors: College Freshmen, College Mathematics, Calculus, Student Placement
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Holden, Jocelyn E.; Finch, W. Holmes; Kelley, Ken – Educational and Psychological Measurement, 2011
The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis,…
Descriptors: Classification, Statistical Analysis, Comparative Analysis, Discriminant Analysis
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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)
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Vaughn, Brandon K.; Wang, Qui – Journal of Experimental Education, 2008
The authors consider the problem of classifying an unknown observation into 1 of several populations by using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for classification procedures that can be used regardless of the group-conditional…
Descriptors: Classification, Regression (Statistics), Discriminant Analysis, Monte Carlo Methods
Vaughn, Brandon; Wang, Qiu – Online Submission, 2005
We consider the problem of classifying an unknown observation into one of several populations using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for discriminant procedures that can be utilized regardless of the group-conditional distributions that…
Descriptors: Classification, Regression (Statistics), Discriminant Analysis, Monte Carlo Methods
Ferrer, Alvaro J. Arce; Wang, Lin – 1999
This study compared the classification performance among parametric discriminant analysis, nonparametric discriminant analysis, and logistic regression in a two-group classification application. Field data from an organizational survey were analyzed and bootstrapped for additional exploration. The data were observed to depart from multivariate…
Descriptors: Classification, Comparative Analysis, Discriminant Analysis, Nonparametric Statistics
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Yarnold, Paul R.; And Others – Educational and Psychological Measurement, 1994
A methodology is proposed to optimize the training classification performance of any suboptimal model. The method, referred to as univariate optimal discriminant analysis (UniODA), is illustrated through application to a two-group logistic regression analysis with 12 empirical examples. Maximizing percentage accuracy in classification is…
Descriptors: Classification, Discriminant Analysis, Models, Performance
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LaMotte, Lynn Roy; McWhorter, Archer, Jr. – Educational and Psychological Measurement, 1981
A linear regression function is developed for use in a classification procedure. The procedure is applied to faculty merit review data, resulting in an interpretable regression function and within-sample classifications as good as a four-funtion discriminant analysis. (Author/BW)
Descriptors: Classification, Discriminant Analysis, Faculty Evaluation, Higher Education
Huberty, Carl J; Smith, Janet C. – 1982
Predictive discriminant analysis involves a technique used in multivariate classification, i.e., in predicting membership in well-defined groups for units on which multiple measures are available. The validation (assessment) of group membership predictions pertains to two problems: estimating true proportions of correct classifications (i.e., hit…
Descriptors: Classification, Cluster Grouping, Discriminant Analysis, Estimation (Mathematics)
Huberty, Carl J – 1982
The issues in the interpretation of discriminant analyses presented are restricted to the typical uses of discriminant analysis by behavioral science researchers. Because behavioral researchers use computer programs packages, the issues discussed deal with information obtainable from the package discriminant analysis programs. The following issues…
Descriptors: Behavioral Science Research, Classification, Cluster Grouping, Computer Programs
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Finch, W. Holmes; Schneider, Mercedes K. – Educational and Psychological Measurement, 2006
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…
Descriptors: Classification, Sample Size, Effect Size, Discriminant Analysis
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Wilson, Rick L.; Hardgrave, Bill C. – Educational and Psychological Measurement, 1995
A study of the ability of different models--including the classification techniques of discriminant analysis, logistic regression, and neural networks--to predict the academic success of master's degree students in business administration suggests that prediction is difficult, but that classification and nonparametric techniques may be…
Descriptors: Academic Achievement, Business Administration, Classification, Discriminant Analysis
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