Administrative problems such as computer malfunction and power outage occasionally lead to missing item scores and hence to incomplete data on mastery tests such as the AP and U.S. Medical Licensing examinations. Investigators are often interested in estimating the probabilities of passing of the examinees with incomplete data on mastery tests. However, there is a lack of research on this estimation problem. The goal of this article is to suggest two new approaches—one each based on classical test theory and item response theory—for estimating the probabilities of passing of the examinees with incomplete data on mastery tests. The two approaches are demonstrated to have high accuracy and negligible misclassification rates.

计算机故障和停电等管理问题偶尔会导致项目分数丢失，从而导致 AP 和美国医疗执照考试等掌握测试的数据不完整。调查人员通常对估计掌握测试数据不完整的考生通过的概率感兴趣。然而，缺乏对这个估计问题的研究。本文的目的是提出两种新方法——每一种都基于经典测试理论和项目反应理论——来估计掌握测试数据不完整的考生通过的概率。这两种方法被证明具有很高的准确性和可忽略的错误分类率。