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- W3122242412 abstract "This article discusses four-item selection rules to design efficient individualized tests for the random weights linear logistic test model (RWLLTM): minimum posterior-weighted [Formula: see text]-error [Formula: see text]minimum expected posterior-weighted [Formula: see text]-error [Formula: see text]maximum expected Kullback–Leibler divergence between subsequent posteriors ( KLP), and maximum mutual information ( MUI). The RWLLTM decomposes test items into a set of subtasks or cognitive features and assumes individual-specific effects of the features on the difficulty of the items. The model extends and improves the well-known linear logistic test model in which feature effects are only estimated at the aggregate level. Simulations show that the efficiencies of the designs obtained with the different criteria appear to be equivalent. However, KLP and MUI are given preference over [Formula: see text] and [Formula: see text] due to their lesser complexity, which significantly reduces the computational burden." @default.
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- W3122242412 date "2014-05-27" @default.
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- W3122242412 title "Computerized Adaptive Testing for the Random Weights Linear Logistic Test Model" @default.
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