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|a (DE-627)JST047111372
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|a (JST)3648167
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Bolt, Daniel M.
|e verfasserin
|4 aut
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|a A Mixture Item Response Model for Multiple-Choice Data
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|c 2001
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|a Text
|b txt
|2 rdacontent
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|a Computermedien
|b c
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|a Online-Ressource
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|a A mixture item response model is proposed for investigating individual differences in the selection of response categories in multiple-choice items. The model accounts for local dependence among response categories by assuming that examinees belong to discrete latent classes that have different propensities towards those responses. Varying response category propensities are captured by allowing the category intercept parameters in a nominal response model (Bock, 1972) to assume different values across classes. A Markov Chain Monte Carlo algorithm for the estimation of model parameters and classification of examinees is described. A real-data example illustrates how the model can be used to distinguish examinees that are disproportionately attracted to different types of distractors in a test of English usage. A simulation study evaluates item parameter recovery and classification accuracy in a hypothetical multiple-choice test designed to be diagnostic. Implications for test construction and the use of multiple-choice tests to perform cognitive diagnoses of item response patterns are discussed.
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|a Copyright 2002 American Educational Research Association and the American Statistical Association
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|a Cognitive Diagnosis
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|a Differential Alternative Functioning
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|a Item Response Theory
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|a Markov Chain Monte Carlo Estimation
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|a Mixture Modeling
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|a Nominal Response Model
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Statistical models
|x Parametric models
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|a Mathematics
|x Pure mathematics
|x Probability theory
|x Random variables
|x Stochastic processes
|x Markov processes
|x Markov chains
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|a Information science
|x Information management
|x Data management
|x Data architecture
|x Data models
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|a Behavioral sciences
|x Psychology
|x Cognitive psychology
|x Cognitive models
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|a Linguistics
|x Language
|x Orthographies
|x Graphemes
|x Punctuation
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|a Information science
|x Data products
|x Datasets
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|a Applied sciences
|x Research methods
|x Modeling
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Descriptive statistics
|x Correlations
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|a Applied sciences
|x Research methods
|x Modeling
|x Simulations
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|a Mathematics
|x Mathematical values
|x Mathematical variables
|x Mathematical independent variables
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|a research-article
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|a Cohen, Allan S.
|e verfasserin
|4 aut
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|a Wollack, James A.
|e verfasserin
|4 aut
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0 |
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|i Enthalten in
|t Journal of Educational and Behavioral Statistics
|d SAGE Publishing, 1976
|g 26(2001), 4, Seite 381-409
|w (DE-627)477533302
|w (DE-600)2174169-4
|x 19351054
|7 nnns
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|g volume:26
|g year:2001
|g number:4
|g pages:381-409
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|u https://www.jstor.org/stable/3648167
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|d 26
|j 2001
|e 4
|h 381-409
|