Cut-Score操作功能扩展:基于惩罚的错误和不确定性在标准设置。

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Applied Psychological Measurement Pub Date : 2021-10-01 Epub Date: 2021-10-11 DOI:10.1177/01466216211046896
Irina Grabovsky, Jesse Pace, Christopher Runyon
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引用次数: 0

摘要

我们对合格/不合格考试进行建模,旨在提供一个系统的工具,以最大限度地减少分类错误。我们使用cut-score操作函数的方法在最小化几个重要的误分类度量的基础上生成特定的cut-score。本研究的目的是考察已知的考生能力分布和标准设置的不确定性对最佳分数线选择的综合影响。此外,我们描述了一个在线应用程序,允许其他人利用cut-score操作功能为他们自己的标准设置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cut-Score Operating Function Extensions: Penalty-Based Errors and Uncertainty in Standard Settings.

Cut-Score Operating Function Extensions: Penalty-Based Errors and Uncertainty in Standard Settings.

Cut-Score Operating Function Extensions: Penalty-Based Errors and Uncertainty in Standard Settings.

We model pass/fail examinations aiming to provide a systematic tool to minimize classification errors. We use the method of cut-score operating functions to generate specific cut-scores on the basis of minimizing several important misclassification measures. The goal of this research is to examine the combined effects of a known distribution of examinee abilities and uncertainty in the standard setting on the optimal choice of the cut-score. In addition, we describe an online application that allows others to utilize the cut-score operating function for their own standard settings.

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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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