对具有交叉分类结构的项目级数据的方法:用学生对教学评价进行说明
1School of Education, Indiana University.
Multivariate behavioral research
|February 14, 2024
概括
学生对教学 (SET) 数据的评估,通常是复杂的,可以使用新的交叉分类项目响应理论 (IRT) 模型进行分析. 这种模式在高等教育研究中比现有方法 (如CCREM和MLIRT) 有优势.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 高等教育研究 高等教育研究
背景情况:
- 学生对教学评估 (SET) 问卷在北美高等教育中被广泛使用.
- SET数据通常具有多变量分类结果和交叉分类结构 (学生和教师).
- 现有的方法可能无法完全捕捉SET数据的复杂性.
研究的目的:
- 审查和比较分析学生评估教学 (SET) 数据的统计方法.
- 引入和评估用于SET数据分析的交叉分类项目响应理论 (IRT) 模型.
- 引导研究人员选择合适的方法来分析复杂的SET数据.
主要方法:
- 对四种统计方法的审查:交叉分类的IRT,交叉分类的随机效应模型 (CCREM),多层次项目响应理论 (MLIRT) 和两步策略.
- 经验数据分析比较这些方法的性能.
- 预先模拟研究,以评估不同条件下的模型行为.
主要成果:
- 交叉分类的IRT模型在处理SET数据的复杂性方面表现出有效性.
- 对比显示了针对特定分析目标的四种方法的不同优缺点.
- 经验和模拟结果为每种方法的实际应用提供了洞察力.
结论:
- 交叉分类IRT模型是分析学生评估教学 (SET) 数据的一个有希望的方法.
- 研究人员在选择分析方法时,应仔细考虑其SET数据的结构和性质.
- 需要进一步的研究来探索这些模型在教育环境中的全部潜力和局限性.
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