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Wind, Stefanie A. – Educational and Psychological Measurement, 2017
Molenaar extended Mokken's original probabilistic-nonparametric scaling models for use with polytomous data. These polytomous extensions of Mokken's original scaling procedure have facilitated the use of Mokken scale analysis as an approach to exploring fundamental measurement properties across a variety of domains in which polytomous ratings are…
Descriptors: Nonparametric Statistics, Scaling, Models, Item Response Theory
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Park, Jungkyu; Yu, Hsiu-Ting – Educational and Psychological Measurement, 2016
The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…
Descriptors: Hierarchical Linear Modeling, Nonparametric Statistics, Data Analysis, Simulation
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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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Koslowsky, Meni – Educational and Psychological Measurement, 1979
Recent trends in the analysis of categorical or nominal variables were discussed for univariate, multivariate, and psychometric problems. It was shown that several statistical procedures commonly used with these problems have analogues which can be applied to assessing categorical variables. (Author/CTM)
Descriptors: Classification, Cluster Grouping, Correlation, 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