Multiple imputation under a two-way model with error is a simple and effective method that has been used to handle missing item scores in unidimensional test and questionnaire data. Extensions of this method to multidimensional data are proposed. A simulation study is used to investigate whether these extensions produce biased estimates of important statistics in multidimensional data, and to compare them with lower benchmark listwise deletion, two-way with error and multivariate normal imputation. The new methods produce smaller bias in several psychometrically interesting statistics than the existing methods of two-way with error and multivariate normal imputation. One of these new methods clearly is preferable for handling missing item scores in multidimensional test data.

中文翻译：

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当测试数据非常复杂时，对项目得分进行多次估算。

具有误差的双向模型下的多重插补是一种简单有效的方法，已用于处理一维测试和问卷数据中缺失的项目分数。建议将该方法扩展到多维数据。使用模拟研究来调查这些扩展是否会在多维数据中对重要统计数据产生偏倚的估计，并将其与较低的基准列表删除，双向误差和多元正态插值进行比较。与现有的带有误差和多元法向插补的双向方法相比，新方法在一些心理学上有意义的统计数据中产生的偏差更小。这些新方法之一显然是处理多维测试数据中缺失项目分数的首选方法。