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Missing Data and the Rasch Model: The Effects of Missing Data Mechanisms on Item Parameter Estimation

机译：缺少数据和RASCH模型：缺少数据机制对项目参数估计的影响

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摘要

This simulation study explores the effects of missing data mechanisms, proportions of missing data, sample size, and test length on the biases and standard errors of item parameters using the Rasch measurement model. When responses were missing completely at random (MCAR) or missing at random (MAR), item parameters were unbiased. When responses were missing not at random (MNAR), item parameters were severely biased, especially when the proportion of missing responses was high. Standard errors were primarily affected by sample size, with larger samples associated with smaller standard errors. Standard errors were inflated in MCAR and MAR conditions, while MNAR standard errors were similar to what they would have been, had the data been complete. This paper supports the conclusion that the Rasch model can handle varying amounts of missing data, provided that the missing responses are not MNAR.
机译：该仿真研究探讨了使用RASCH测量模型对项目参数偏差和标准误差的缺失数据机制，缺失数据，样本大小和测试长度的影响。当随机丢失的响应（MCAR）或随机（MAR）丢失时，项目参数是无偏的。当遗忘不随意（mnar）时，项目参数严重偏见，特别是当缺失响应的比例很高时。标准误差主要受样本大小的影响，具有较大的样本与较小的标准误差相关。标准误差在MCAR和MAR条件下膨胀，而MNAR标准错误与他们所完成的误差相似。本文支持结论，RASCH模型可以处理不同数量的缺失数据，只要丢失的响应不是MNAR。

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