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辅助解读评估后心理测量报告的十二条建议。

Twelve tips to aid interpretation of post-assessment psychometric reports.

作者信息

Tavakol Mohsen, O'Brien David G, Sharpe Claire C, Stewart Claire

机构信息

Medical Education Centre, School of Medicine, The University of Nottingham, Nottingham, UK.

出版信息

Med Teach. 2024 Feb;46(2):188-195. doi: 10.1080/0142159X.2023.2241624. Epub 2023 Aug 4.

Abstract

Post-assessments psychometric reports are a vital component of the assessment cycle to ensure that assessments are reliable, valid and fair to make appropriate pass-fail decisions. Students' scores can be summarised by examination of frequency distributions, central tendency measures and dispersion measures. Item discrimination indicies to assess the quality of items, and distractors that differentiate between students achieving or not achieving the learning outcomes are key. Estimating individual item reliability and item validity indices can maximise test-score reliability and validity. Test accuracy can be evaluated by assessing test reliability, consistency and validity and standard error of measurement can be used to measure the variation. Standard setting, even by experts, may be unreliable and reality checks such as the Hofstee method, P values and correlation analysis can improve validity. The Rasch model of student ability and item difficulty assists in modifying assessment questions, pinpointing areas for additional instruction. We propose 12 tips to support test developers in interpreting structured psychometric reports, including analysis and refinement of flawed items and ensuring fair assessments with accurate and defensible marks.

摘要

评估后的心理测量报告是评估周期的重要组成部分,以确保评估可靠、有效且公平,从而做出合适的通过或不通过的决定。学生的分数可以通过检查频率分布、集中趋势度量和离散度度量来汇总。评估试题质量的试题区分指数,以及区分达到或未达到学习成果的学生的干扰项是关键。估计单个试题的信度和效度指数可以使考试分数的信度和效度最大化。可以通过评估测试信度、一致性和效度来评估测试准确性,测量标准误可用于衡量变异性。即使由专家进行标准设定也可能不可靠,诸如霍夫斯泰方法、P值和相关分析等实际检验可以提高效度。学生能力和试题难度的拉施模型有助于修改评估问题,确定需要额外指导的领域。我们提出12条建议,以支持测试开发者解读结构化的心理测量报告,包括分析和改进有缺陷的试题,并确保以准确且合理的分数进行公平评估。

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