个人层面的概率和集群层面的比例:对二元结果的未结合的多层模型中的可解释的2级估计
1Psychology Department, Florida International University.
Psychological methods
|February 8, 2024
概括
多级模型对于二进制结果可能是复杂的. 这项研究引入了一种新方法来准确解释集群级效应,改善了研究人员使用后勤和探头模型的分析.
科学领域:
- 统计 统计 统计 统计
- 量化心理学 量化心理学
- 计量经济学 计量经济学
背景情况:
- 多级模型 (MLM) 对于分析嵌套数据至关重要,使得假设可以在多个层面上进行测试.
- 标准的MLM规范通过分离特定级别的影响来提高连续结果的清晰度.
- 在具有二进制或顺序结果的MLM中解释集群级效应在概率尺度上具有挑战性.
研究的目的:
- 提出一种新的缩放方法,用于在双层二进制和顺序模型中准确计算集群平均成功比例,赔率和赔率比率 (OR).
- 解决MLM标准规范对非连续结果的解释性限制.
- 为研究人员分析嵌套的二进制或顺序数据提供实用工具.
主要方法:
- 开发了用于二级二进制和顺序逻辑和试验模型的重新缩放技术.
- 将拟议的方法应用于现实世界的数据集进行演示.
- 创建了额外的R功能,以方便用户实现.
主要成果:
- 拟议的重新缩放方法能够直观地解释对二进制和顺序结果的概率,赔率和OR指标的集群级影响.
- 通过实用数据示例证明了方法的实用性.
- 为更广泛的应用提供了可访问的R功能.
结论:
- 这种新型的重新缩放方法显著提高了在具有非连续结果的多层模型中集群级效应的可解释性.
- 这种方法有助于更细致地了解群体层面的因素如何影响个体的二进制或顺序反应.
- 提供的R函数旨在简化这种方法在统计研究中的应用.
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