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Effects of estimation bias on multiple-category classification with an IRT-Based adaptive classification procedure

机译：基于IRT的自适应分类程序对估计偏倚的影响

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摘要

The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following results were found. (a) The Bayesian estimators were more likely to misclassify examinees into an inward category because of their inward biases, when a fixed start value of zero was assigned to every examinee. (b) When moderately accurate start values were available, however, Bayesian estimators produced classifications that were slightly more accurate than was the maximum likelihood estimator or weighted likelihood estimator. Expected a posteriori was the procedure that produced the most accurate results among the three Bayesian methods. (c) All five estimators produced equivalent efficiencies in terms of number of items required, which was 50 or more items except for abilities that were less than -2.00 or greater than 2.00.
机译：研究了五个能力估计器，即最大似然估计器，加权似然估计器，最大后验，预期后验和欧文顺序估计器，对基于项目响应理论的自适应分类程序在多个类别上的性能的影响。模拟。发现以下结果。 （a）当将固定的起始值分配给零个考生时，贝叶斯估计量由于其向内偏见而更有可能将考生分类为内向类别。 （b）但是，当有中等准确的起始值可用时，贝叶斯估计器所产生的分类比最大似然估计器或加权似然估计器更为准确。预期后验是在三种贝叶斯方法中产生最准确结果的程序。 （c）所有五个估算器在所需项目数方面均产生了等效的效率，除小于-2.00或大于2.00的能力外，均等于或大于50。

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