在元分析结构方程模型中处理依赖样本:基于Wishart的方法
1Research, Measurement & Statistics, Department of Educational Psychology, University of North Texas, Denton, TX, 76205, USA. james.uanhoro@unt.edu.
本研究引入了一种新的方法,用于元分析结构方程建模 (MASEM),使用样本共变矩阵的层次建模. 该方法在元分析中有效处理依赖矩阵和固定/随机效应.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 超分析结构方程建模 (MASEM) 对于合成研究结果至关重要.
- 现有的方法与依赖共变矩阵扎,这在涉及单个研究的多个结果或来源的元分析中很常见.
- 处理固定和随机效应模型对于强大的元分析合成至关重要.
研究的目的:
- 为元分析结构方程模型 (MASEM) 提出一种新的等级建模方法.
- 在元分析研究中解决依赖共变矩阵的挑战.
- 提供一个灵活的框架,适应固定和随机效应的元分析SEM.
主要方法:
- 使用样本协变矩阵的等级建模,假设是Wishart分布.
- 开发一种方法来管理单个研究或作者产生的依赖共变矩阵.
- 采用模拟研究来评估拟议方法的参数恢复.
主要成果:
- 模拟研究表明,拟议方法能够充分恢复参数.
- 该方法在元分析性SEM中成功处理依赖共变矩阵.
- 该方法在固定和随机效应的元分析SEM中得到了验证.
结论:
- 提出的等级建模方法为元分析结构方程建模提供了一个强大的解决方案.
- 这种方法有效地解决了依赖共变矩阵的问题,增强了元分析研究.
- 这种方法在"贝叶斯语质量"R包中实施,以促进实际应用.
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