Test design optimization in CAT early stage with the nominal response model
Test design optimization in CAT early stage with the nominal response model
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DOI：
10.1177/0146621606291571
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发表时间：
2007-05-01
影响因子：
1.2
通讯作者：
Tan, Frans E.
中科院分区：
文献类型：
--
作者：
Passos, Valeria Lima;Berger, Martijn P. F.;Tan, Frans E.
计算机化自适应测试的早期阶段是指在只管理几个项目的情况下进行特征估计的阶段。这个阶段的特征是估计的偏差和不稳定性。在这项研究中，引入了一个项目选择准则，试图减少这种不稳定性：d -最优性准则。通过多域无约束CAT仿真，对该准则在不同测试条件下的性能进行了评价。仿真结果表明，早期不稳定的程度主要取决于项目池信息的质量及其大小，其次取决于项目选择标准。d -最优性准则的效率与其他已知项目选择准则的效率相似。然而，在CAT开始时，它通常会产生对不稳定性表现出更健壮的性能的估计。
The early stage of computerized adaptive testing (CAT) refers to the phase of the trait estimation during the administration of only a few items. This phase can be characterized by bias and instability of estimation. In this study, an item selection criterion is introduced in an attempt to lessen this instability: the D-optimality criterion. A polytomous unconstrained CAT simulation is carried out to evaluate this criterion's performance under different test premises. The simulation shows that the extent of early stage instability depends primarily on the quality of the item pool information and its size and secondarily on the item selection criteria. The efficiency of the D-optimality criterion is similar to the efficiency of other known item selection criteria. Yet, it often yields estimates that, at the beginning of CAT, display a more robust performance against instability.