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The Effects of Probability Threshold Choice on an Adjustment for Guessing using the Rasch Model

机译：概率阈值选择对使用Rasch模型进行猜测的调整影响

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This paper investigates a strategy for accounting for correct guessing with the Rasch model that we entitled the Guessing Adjustment. This strategy involves the identification of all person/item encounters where the probability of a correct response is below a specified threshold. These responses are converted to missing data and the calibration is conducted a second time. This simulation study focuses on the effects of different probability thresholds across varying conditions of sample size, amount of correct guessing, and item difficulty. Biases, standard errors, and root mean squared errors were calculated within each condition. Larger probability thresholds were generally associated with reductions in bias and increases in standard errors. Across most conditions, the reduction in bias was more impactful than the decrease in precision, as reflected by the RMSE. The Guessing Adjustment is an effective means for reducing the impact of correct guessing and the choice of probability threshold matters.
机译：本文调查了考虑正确猜测我们授予猜测调整的RASCH模型的策略。该策略涉及识别所有人/项目的遇到，其中正确响应的概率低于指定的阈值。这些响应转换为缺失数据，第二次进行校准。该仿真研究侧重于不同概率阈值对样本大小的不同条件的影响，正确猜测和物品难度。在每个条件下计算偏差，标准错误和均方方平方误差。较大的概率阈值通常与偏差的减少和标准误差的增加相关。在大多数条件下，偏差的减少比精确的降低更大，因为RMSE反映。猜测调整是减少正确猜测和概率阈值问题的影响的有效手段。

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