Approaches for dealing with item omission include incorrect scoring, ignoring missing values, and approaches for nonignorable missing values and have only been evaluated for certain forms of nonignorability. In this paper we investigate the performance of these approaches for various conditions of nonignorability, that is, when the missing response depends on i) the item response, ii) a latent missing propensity, or iii) both. No approach results in unbiased parameter estimates of the Rasch model under all missing data mechanisms. Incorrect scoring only results in unbiased estimates under very specific data constellations of missing mechanisms i) and iii). The approach for nonignorable missing values only results in unbiased estimates under condition ii). Ignoring results in slightly more biased estimates than the approach for nonignorable missing values, while the latter also indicates the presence of nonignorablity under all simulated conditions. We illustrate the results in an empirical example on PISA data.

中文翻译：

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在不可忽略的丢失数据条件下丢失数据方法的性能

处理项目遗漏的方法包括错误的评分，忽略缺失值以及不可忽略的缺失值的方法，并且仅针对某些形式的不可忽略性进行了评估。在本文中，我们研究了这些方法在各种不可忽略性条件下的性能，即当缺失响应取决于i）项目响应，ii）潜在缺失倾向或iii）两者时。在所有缺失的数据机制下，没有一种方法会导致Rasch模型的参数估计无偏。不正确的评分只会导致在缺少机制i）和iii）的非常具体的数据星座下产生无偏估计。在条件ii）下，不可忽略的缺失值的方法只会导致无偏估计。忽略结果会比不可忽略的缺失值方法产生更大的估计偏差，而后者也表明在所有模拟条件下均存在不可忽略性。我们以PISA数据的经验示例说明了结果。