Yi Qing, Chang Hua-Hua
ACT, Inc., Iowa City. IA 52243, USA.
Br J Math Stat Psychol. 2003 Nov;56(Pt 2):359-78. doi: 10.1348/000711003770480084.
Content balancing is often required in the development and implementation of computerized adaptive tests (CATs). In the current study, we propose a modified a-stratified method, the a-stratified method with content blocking. As a further refinement of a-stratified CAT designs, the new method incorporates content specifications into item pool stratification. Simulation studies were conducted to compare the new method with three previous item selection methods: the a-stratified method; the a-stratified with b-blocking method; and the maximum Fisher information method with Sympson-Hetter exposure control. The results indicated that the refined a-stratified design performed well in reducing item overexposure rates, balancing item usage within the pool, and maintaining measurement precision, in a situation where all four procedures were forced to balance content.
在计算机自适应测试（CAT）的开发和实施过程中，通常需要进行内容平衡。在本研究中，我们提出了一种改进的α分层方法，即带有内容分组的α分层方法。作为α分层CAT设计的进一步优化，新方法将内容规范纳入题库分层。我们进行了模拟研究，以将新方法与之前的三种选题方法进行比较：α分层方法；带有β分组的α分层方法；以及带有辛普森-赫特曝光控制的最大费舍尔信息量方法。结果表明，在所有四种方法都被迫平衡内容的情况下，改进后的α分层设计在降低题目过度曝光率、平衡题库内题目使用情况以及保持测量精度方面表现良好。
