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Item response theory: applications of modern test theory in medical education.

机译:项目反应理论:现代测试理论在医学教育中的应用。

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CONTEXT: Item response theory (IRT) measurement models are discussed in the context of their potential usefulness in various medical education settings such as assessment of achievement and evaluation of clinical performance. PURPOSE: The purpose of this article is to compare and contrast IRT measurement with the more familiar classical measurement theory (CMT) and to explore the benefits of IRT applications in typical medical education settings. SUMMARY: CMT, the more common measurement model used in medical education, is straightforward and intuitive. Its limitation is that it is sample-dependent, in that all statistics are confounded with the particular sample of examinees who completed the assessment. Examinee scores from IRT are independent of the particular sample of test questions or assessment stimuli. Also, item characteristics, such as item difficulty, are independent of the particular sample of examinees. The IRT characteristic of invariance permits easy equating of examination scores, whichplaces scores on a constant measurement scale and permits the legitimate comparison of student ability change over time. Three common IRT models and their statistical assumptions are discussed. IRT applications in computer-adaptive testing and as a method useful for adjusting rater error in clinical performance assessments are overviewed. CONCLUSIONS: IRT measurement is a powerful tool used to solve a major problem of CMT, that is, the confounding of examinee ability with item characteristics. IRT measurement addresses important issues in medical education, such as eliminating rater error from performance assessments.
机译:背景:项目响应理论(IRT)的测量模型在各种医学教育环境中的潜在有用性的背景下进行了讨论,例如成就评估和临床表现评估。目的:本文的目的是将IRT测量与更熟悉的经典测量理论(CMT)进行比较和对比,并探讨IRT在典型医学教育环境中应用的好处。简介:CMT是医学教育中使用的更常见的测量模型,非常简单直观。它的局限性在于它与样本有关,因为所有统计数据都与完成评估的特定应试者样本相混淆。 IRT的考生分数与测试题或评估刺激的特定样本无关。同样,诸如项目难度之类的项目特征与应试者的特定样本无关。 IRT的不变性使得考试分数容易相等,这将分数置于恒定的测量范围内,并允许对学生能力随时间的变化进行合法比较。讨论了三种常见的IRT模型及其统计假设。概述了IRT在计算机自​​适应测试中的应用以及在临床性能评估中作为调整评分错误的有用方法。结论:IRT测量是一种强大的工具,用于解决CMT的主要问题,即考生能力与项目特征的混淆。 IRT度量解决医学教育中的重要问题,例如从绩效评估中消除评分者错误。

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