Risk Nicole
Nicole Risk, American Medical Technologists, 10700 W. Higgins Road, Suite 150, Rosemont, IL 60018, USA,
J Appl Meas. 2016;17(1):54-78.
This study looked at numerous aspects of item parameter drift (IPD) and its impact on measurement in computer adaptive testing (CAT). A series of CAT simulations were conducted, varying the amount and magnitude of IPD, as well as the size of the item pool. The effects of IPD on measurement precision, classification, and test efficiency, were evaluated using a number of criteria. These included bias, root mean square error (RMSE), absolute average difference (AAD), total percentages of misclassifcation, the number of false positives and false negatives, the total test lengths, and item exposure rates. The results revealed negligible differences when comparing the IPD conditions to the baseline condition for all measures of precision, classification accuracy, and test efficiency. The most relevant finding indicates that magnitude of drift has a larger impact on measurement precision than the number of items with drift.
本研究考察了项目参数漂移（IPD）的诸多方面及其对计算机自适应测试（CAT）中测量的影响。进行了一系列CAT模拟，改变IPD的数量和幅度以及项目库的大小。使用多种标准评估IPD对测量精度、分类和测试效率的影响。这些标准包括偏差、均方根误差（RMSE）、绝对平均差（AAD）、错误分类的总百分比、假阳性和假阴性的数量、总测试长度以及项目曝光率。结果显示，将IPD条件与基线条件相比，在所有精度、分类准确性和测试效率的测量指标上差异可忽略不计。最相关的发现表明，漂移的幅度对测量精度的影响比有漂移的项目数量更大。
