Downing Steven M
College of Medicine, Department of Medical Education (MC 591), University of Illinois at Chicago, Chicago, IL 60612-7309, USA.
Med Educ. 2003 Aug;37(8):739-45. doi: 10.1046/j.1365-2923.2003.01587.x.
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.
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.
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, which places 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.
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.
项目反应理论(IRT)测量模型在各种医学教育环境中的潜在用途背景下进行了讨论,例如成绩评估和临床表现评价。
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的应用优势。
CMT是医学教育中更常用的测量模型,简单直观。其局限性在于它依赖于样本,因为所有统计数据都与完成评估的特定考生样本混淆在一起。IRT得出的考生分数独立于特定的试题样本或评估刺激。此外,项目特征,如项目难度,独立于特定的考生样本。IRT的不变性特征允许轻松地对考试分数进行等值化,即将分数置于恒定的测量尺度上,并允许对学生能力随时间的变化进行合理比较。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作为一种用于调整临床表现评估中评分者误差的有用方法。
IRT测量是解决CMT一个主要问题的有力工具,即考生能力与项目特征的混淆。IRT测量解决了医学教育中的重要问题,如从绩效评估中消除评分者误差。