Han Yuting, Jiang Zhehan, Ouyang Jinying, Xu Lingling, Cai Ting
Institute of Medical Education, Peking University, Beijing, China.
National Center for Health Professions Education Development, Peking University, Beijing, China.
Front Med (Lausanne). 2022 Dec 14;9:1037897. doi: 10.3389/fmed.2022.1037897. eCollection 2022.
As a high-stake national-level examination administrated before students' clerkship in China, the Standardized Competence Test for Clinical Medicine Undergraduates (SCTCMU) has received much attention from the relevant educational departments and society at large. Investigating SCTCMU's validity and reliability is critical to the national healthcare profession education.
Raw responses from SCTCMU, answered by 44,332 examines of 4th-year undergraduate medical students on 300 multiple-choice items, were used to evaluate the quality of the exam psychometric methods based on item response theory (IRT). The core assumptions and model-data fit of IRT models were evaluated, as well as the item properties and information functions.
The IRT models were fitted to the observed assessment data, where all the required assumptions were met. The IRT analysis showed that most items had acceptable psychometric properties, and the passing score was located close to the lowest measurement error computed from the model outcomes.
The proposed modern psychometric method provides a practical and informative approach to calibrating and analyzing medical education assessments. This work showcases a realistic depiction of the IRT analysis process and therefore facilitates the work of applied researchers wanting to conduct, interpret, and report IRT analyses on medical assessments.
作为中国医学生实习前进行的一项高风险国家级考试,临床医学本科标准化能力测试(SCTCMU)受到了相关教育部门和社会各界的广泛关注。调查SCTCMU的效度和信度对国家医疗行业教育至关重要。
采用基于项目反应理论(IRT)的心理测量方法,利用44332名本科四年级医学生对SCTCMU的300道多项选择题的原始作答来评估考试质量。评估了IRT模型的核心假设和模型与数据的拟合度,以及项目属性和信息函数。
IRT模型与观察到的评估数据拟合良好,满足所有必要假设。IRT分析表明,大多数项目具有可接受的心理测量属性,及格分数接近根据模型结果计算出的最低测量误差。
所提出的现代心理测量方法为校准和分析医学教育评估提供了一种实用且信息丰富的方法。这项工作展示了IRT分析过程的实际描述,因此有助于希望对医学评估进行IRT分析、解释和报告的应用研究人员开展工作。