The semi-generalized partial credit model (Semi-GPCM) has been proposed as a unidimensional modeling method for handling not applicable scale responses and neutral scale responses, and it has been suggested that the model may be of use in handling missing data in scale items. The purpose of this study is to evaluate the ability of the unidimensional Semi-GPCM to aid in the recovery of person parameters from item response data in the presence of item-level missingness, and to compare the performance of the model with two other proposed methods for handling such missingness: a multidimensional modeling approach for missingness and full information maximum likelihood estimation. The results indicate that the Semi-GPCM performs acceptably in an absolute sense when less than 30% of the item data is missing but does not outperform the other two methods under any particular conditions. We conclude with a discussion about when practitioners may or may not want to use the Semi-GPCM to recover person parameters from item response data with missingness.

中文翻译：

---

处理项目级缺失的半广义部分信用模型的性能

半广义部分信用模型 (Semi-GPCM) 已被提出作为一种用于处理不适用尺度反应和中性尺度反应的一维建模方法，并表明该模型可用于处理尺度项目中的缺失数据. 本研究的目的是评估一维 Semi-GPCM 在存在项目级缺失的情况下帮助从项目响应数据中恢复人员参数的能力，并将该模型的性能与其他两种提出的方​​法进行比较处理这种缺失：缺失和全信息最大似然估计的多维建模方法。结果表明，当少于 30% 的项目数据丢失时，Semi-GPCM 在绝对意义上的表现是可以接受的，但在任何特定条件下都没有优于其他两种方法。我们最后讨论了从业者何时可能或可能不想使用 Semi-GPCM 从缺失的项目响应数据中恢复人员参数。