Technical difficulties occasionally lead to missing item scores and hence to incomplete data on computerized tests. It is not straightforward to report scores to the examinees whose data are incomplete due to technical difficulties. Such reporting essentially involves imputation of missing scores. In this paper, a simulation study based on data from three educational tests is used to compare the performances of six approaches for imputation of missing scores. One of the approaches, based on data mining, is the first application of its kind to the problem of imputation of missing data. The approach based on data mining and a multiple imputation approach based on chained equations led to the most accurate imputation of missing scores, and hence to most accurate score reporting. A simple approach based on linear regression performed the next best overall. Several recommendations are made regarding the reporting of scores to examinees with incomplete data.

中文翻译：

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大型教育评估数据不完整的考生成绩报告

技术上的困难有时会导致项目得分丢失，从而导致计算机化测试中的数据不完整。向由于技术困难而数据不完整的考生报告分数并不容易。这种报告本质上涉及遗漏分数的估算。在本文中，基于来自三个教育测试的数据进行的模拟研究被用来比较六种缺失得分估算方法的性能。其中一种基于数据挖掘的方法是此类方法首次应用于缺失数据的估算问题。基于数据挖掘的方法和基于链式方程式的多重插补方法导致了对遗漏分数的最准确插补，从而导致了最准确的分数报告。基于线性回归的简单方法在整体上排名第二。对于向不完整数据的考生报告分数，提出了一些建议。