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Updated: Sep 9, 2025

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用嵌套数据进行混合建模的类列表:简要报告
Rashelle J Musci1, Joseph Kush2, Elise T Pas3
1Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Baltimore, MD 21205.
Journal of experimental education
|September 2, 2025
概括
教育研究人员应仔细考虑嵌套数据的潜在类分析模型规格. 这项研究比较了四种方法,为教育研究中的多层混合模型提供了建议.
科学领域:
- 教育研究
- 量化心理学
- 统计模型
背景情况:
- 教育研究越来越关注学生的异质性.
- 混合模型用于识别学生子组.
- 嵌套数据结构 (教室/学校内的学生) 在教育中很常见.
研究的目的:
- 评估嵌套数据的不同隐性类型模型规范.
- 展示各种分析方法对结果的影响.
- 引导研究人员选择合适的多层混合模型方法.
主要方法:
- 使用州收集的纵向学生数据.
- 对比了四种潜在类型模型规范:忽略嵌套,后期调整,参数和非参数方法.
- 分析了每个规范在嵌套数据中识别隐藏类的含义.
主要成果:
- 不同的模型规范在分析嵌套数据时产生不同的结果.
- 选择的规范对学生子组的识别有很大影响.
- 突出了影响选择多层混合模型方法的因素.
结论:
- 提供了使用嵌套教育数据的混合模型的建议.
- 强调适当的统计方法对于准确的子组识别的重要性.
- 帮助研究人员为多层混合物建模做出明智的决定.
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