在存在层次效果大小多重性的情况下对相关性矩阵进行元分析
Ronny Scherer1,2, Diego G Campos1,2
1Centre for Educational Measurement (CEMO), Faculty of Educational Sciences, https://ror.org/01xtthb56University of Oslo, Oslo, Norway.
Research synthesis methods
|February 2, 2026
概括
这项研究引入了一种新的多层次,多变量,随机效应模型,用于元分析相关性矩阵. 这种方法解决了层次效应大小的多重性,改善了研究合成中的聚合相关性矩阵估计.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 研究方法研究方法研究方法学
背景情况:
- 对关联矩阵进行元分析对于合成构造之间的关系证据至关重要.
- 现有的元分析方法往往忽略了研究中的多个样本的等级效果大小多重性.
- 这种多重性来自于每项研究提取几个相关性矩阵 (例如,来自不同地点或国家).
研究的目的:
- 开发和描述一种新的多层次,多变量,随机效应建模方法,用于元分析相关性矩阵.
- 为了应对对元分析数据中等级效应大小多重性的挑战.
- 为选择合适的元分析基线模型提供灵活的框架.
主要方法:
- 提出了一个多层次,多变量,随机效应建模框架.
- 该方法将相关性矩阵的聚合与对等效应大小多重性的处理相结合.
- 在这个框架内,展示和说明了四个特定的工作模型.
主要成果:
- 提出的方法有效地将相关性矩阵聚合在一起,同时考虑层次依赖.
- 它可以测试关于随机效应之间的依赖关系的不同假设.
- 为实际应用提供了说明性示例和R代码.
结论:
- 开发的多层次,多变量,随机效应模型为对等级数据的相关性矩阵进行元分析提供了强大的解决方案.
- 这种方法的进步提高了研究合成中聚合相关性估计的准确性和可靠性.
- 该方法为处理复杂数据结构的元分析师提供了明智的模型选择.
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