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Item selection criteria with practical constraints for computerized classification testing

机译：具有实际约束条件的项目选择标准，用于计算机分类测试

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摘要

This study compares four item selection criteria for a two-category computerized classification testing: (1) Fisher information (FI), (2) Kullback-Leibler information (KLI), (3) weighted log-odds ratio (WLOR), and (4) mutual information (MI), with respect to the efficiency and accuracy of classification decision using the sequential probability ratio test as well as the extent of item usage. The comparability of the four item selection criteria are examined primarily under three types of item selection conditions: (1) using only the four item selection algorithms, (2) using the four item selection algorithms and content balancing control, and (3) using the four item selection algorithms, content balancing control, and item exposure control. The comparability of the four item selection criteria is also evaluated in two types of proficiency distribution and three levels of indifference region width. The results show that the differences of the four item selection criteria are washed out as more realistic constraints are imposed. Moreover, within two-category classification testing, the use of MI does not necessarily generate greater efficiency than FI, WLOR, and KLI, although MI might seem attractive for its general form of formula in item selection.
机译：这项研究比较了两类计算机分类测试的四个项目选择标准：（1）Fisher信息（FI），（2）Kullback-Leibler信息（KLI），（3）加权对数比（WLOR）和（ 4）关于使用顺序概率比率检验进行分类决策的效率和准确性以及项目使用程度的互信息（MI）。主要在三种类型的项目选择条件下检查这四个项目选择标准的可比性：（1）仅使用四种项目选择算法;（2）使用四种项目选择算法和内容平衡控制;以及（3）使用四个项目选择算法，内容平衡控制和项目曝光控制。四种项目选择标准的可比性还通过两种类型的熟练度分布和三种水平的差异区域宽度进行了评估。结果表明，由于施加了更现实的约束，因此消除了四个项目选择标准的差异。此外，在两类分类测试中，MI的使用不一定比FI，WLOR和KLI产生更高的效率，尽管MI似乎对于其项目选择中的常规公式形式具有吸引力。

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