预测测试与增益分数建模的条件化:在多层设置中重新审视争议
Bruno Arpino1, Silvia Bacci2, Leonardo Grilli2
1Department of Statistical Science and Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Padova, Italy.
在多层次研究中估计治疗效果需要仔细考虑统计方法. 多级建模显示,集群级处理对集群大小敏感,影响估计器可靠性.
科学领域:
- 统计 统计 统计 统计
- 教育研究教育研究
- 社会科学 社会科学 社会科学
背景情况:
- 观察性研究通常使用前测试和后测试得分来评估治疗效果.
- 两种常见的统计方法涉及对测试前分数有条件地建模测试后分数或分析增益分数.
- 在教育等领域常见的多层结构引入了诸如上下文效应和区分个人与集群治疗等复杂性.
研究的目的:
- 分析两个统计方法的优点和缺点,用于估计多层设置中的治疗效果.
- 在多层框架内调查个人层面与集群层面治疗的影响.
- 在不同的多层次场景下比较条件和增益得分模型的性能.
主要方法:
- 进行了一项模拟研究,以比较统计建模方法.
- 该研究的重点是观察数据,受试者嵌入集群 (例如,学校内的学生).
- 分析分析了多层模型中的个人层面和集群层面的治疗效应.
主要成果:
- 对于个人级别的治疗方法,研究结果与现有文献一致.
- 对于集群级别的治疗,估计器的可靠性取决于预测得分的集群平均值.
- 小集群大小可能会导致对集群级治疗效应的不可靠估计.
结论:
- 在多层次研究中,选择统计方法来估计治疗效果至关重要.
- 集群级别的处理在多层设置中提出了独特的挑战,特别是关于集群大小对估计器可靠性的影响.
- 研究人员在多级观测研究中分析集群水平治疗效应时,应仔细考虑集群大小.
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