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首页> 外文期刊>Journal of applied measurement >An Examination of Sensitivity to Measurement Error in Rasch Residual-based Fit Statistics
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An Examination of Sensitivity to Measurement Error in Rasch Residual-based Fit Statistics

机译：RASCH残差统计中测量误差敏感性

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摘要

The purpose of this paper is to examine the sensitivity of commonly used Rasch fit measures to different distributions of error in item responses. Using Monte Carlo methods, we generated 10 different measurement error conditions within the Rasch rating scale model or partial credit model, and we recorded the estimates of INFIT MNSQ, OUTFIT MNSQ, and person separation reliability for each error distribution condition. INFIT MNSQ and OUTFIT MNSQ were not sensitive to error distributions when the distribution was the same across items. When the error distribution varies across items, INFIT MNSQ and OUTFIT MNSQ detected items with higher levels of measurement error as potentially misfitting. The Rasch person separation reliability statistic was sensitive to varying levels of measurement error, as expected. Our findings have implications for the use of fit measures in diagnosing model misfit.
机译：本文的目的是检查常用RASCH适合措施对项目响应中的不同误差分布的敏感性。使用Monte Carlo方法，我们在RASCH评级规模模型或部分信用模型中生成了10个不同的测量错误条件，我们记录了INFIT MNSQ，Outfit MNSQ的估计，以及每个错误分布条件的人分离可靠性。 infit mnsq和odfit mnsq对横跨分布相同时对错误分布不敏感。当错误分布在项目上变化时，INFIT MNSQ和OUTFIT MNSQ检测到具有更高级别测量误差级别的项目，因为可能的不足。 RASCH人分离可靠性统计数据与预期的测量误差的不同程度敏感。我们的调查结果对使用适合措施诊断模型误操作有影响。

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