中文摘要：

项目增补对认知诊断计算机化自适应测验（CD—CAT）中的题库维护至关重要。在传统CAT中，在线标定方法经常用于估计新题的项目参数。然而直到现在，在CD-CAT领域还没有任何关于在线标定的论文公开发表。为将传统CAT中3种有代表性的在线标定方法（MethodA、OEM和MEM）推广至CD—CAT（CD—MethodA、CD-OEM和CD—MEM）建立分析基础，并采用模拟方法对这3种方法进行比较。研究表明：CD—MethodA方法在项目参数的返真性方面优于其它两种方法；自适应标定设计较随机标定设计可以提高项目参数的返真质量。

英文摘要：

Like all computerized adaptive testing （CAT） applications, some items in the item bank maybe flawed or obsolete or overexposed and they should be replaced by new items （Wainer ＆ Mislevy, 1990）, item replenishing is essential for item bank maintenance and development in cognitive diagnostic CAT （CD-CAT）. In regular CAT, on-line calibration method is commonly used to calibrate the item parameters of new items. However, until now no reference is publicly available about on-line calibration for CD-CAT. Thus, this study investigated the possibility to extend some current methods used in CAT to CD-CAT situation. Three representative on-line calibration methods in regular CAT were under investigation： Method A （Stocking, 1988）, marginal maximum likelihood estimate with one EM cycle （OEM） method （Wainer ＆ Mislevy, 1990） and marginal maximum likelihood estimate with multiple EM cycles （MEM） method （Ban, Hanson, Wang, Yi, ＆ Harris, 2001）. Under certain theoretical justifications based on the Deterministic Inputs, Noisy ＂and＂ Gate （DINA） model, these methods were generalized to CD-CAT situation, denoted as CD-Method A, CD-OEM and CD-MEM, respectively. Two simulation studies were conducted to compare the performance of the three CD-CAT on-line calibration methods in terms of item-parameter recovery. In the first study, the new items were randomly assigned to the examinees and then were calibrated accordingly. 2000 examinees were generated assuming that each examinee has 50% probability of mastering each attribute, 360 operational items were simulated and their guessing and slipping parameters were all randomly drawn from U （0.05, 0.25）. 20 new items were simulated and the Q matrix corresponding to the new items was constructed by randomly selecting 20 rows from the Q matrix corresponding to the operational items, and the item parameters of new items were also randomly drawn from U （0.05, 0.25）. The Shannon Entropy method was employed to select the next item and the Max