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Controlling Guessing Bias in the Dichotomous Rasch Model Applied to a Large-Scale, Vertically Scaled Testing Program

机译:控制二分Rasch模型中的猜测偏差,将其应用于大规模,垂直规模的测试程序

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摘要

Recent research has shown how the statistical bias in Rasch model difficulty estimates induced by guessing in multiple-choice items can be eliminated. Using vertical scaling of a high-profile national reading test, it is shown that the dominant effect of removing such bias is a nonlinear change in the unit of scale across the continuum. The consequence is that the proficiencies of the more proficient students are increased relative to those of the less proficient. Not controlling the guessing bias underestimates the progress of students across 7 years of schooling with important educational implications.
机译:最近的研究表明,如何消除因选择题的猜测而引起的Rasch模型难度估计中的统计偏差。使用备受瞩目的国家阅读测试的垂直缩放,可以看出消除这种偏差的主要作用是整个连续范围中缩放单位的非线性变化。结果是,相对于不熟练的学生,熟练程度更高的学生的熟练度得到了提高。不控制猜测偏差会低估学生在整个7年的学习过程中的学习进度,并具有重要的教育意义。

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