检查库存:为个人参与者数据展示不同的方法元分析结构方程建模
Lennert J Groot1, Kees-Jan Kan1, Suzanne Jak1
1University of Amsterdam, Amsterdam, The Netherlands.
Research synthesis methods
|August 13, 2024
概括
本研究比较了使用原始数据进行元分析的结构方程建模 (SEM) 方法. 结果显示各种技术的参数估计和标准误差存在差异,突出了对个人参与者数据元分析进行进一步研究的需要.
科学领域:
- 心理学方法 心理学方法
- 统计建模 统计建模
- 进行元分析分析.
背景情况:
- 研究人员通常可以访问用于元分析的原始数据.
- 结构方程建模 (SEM) 是分析复杂关系的强大工具.
研究的目的:
- 在原始数据可用时,识别,说明和比较SEM分析选项.
- 讨论各种SEM技术的程序,能力和结果的差异.
主要方法:
- 使用多层次和多组SEM直接分析原始数据.
- 使用总结统计与基于相关性的元分析性SEM (MASEM).
- 使用开源软件将基于计划行为理论的路径模型配合到多个数据集中.
主要成果:
- 在参数估计和标准误差中观察到的方法之间的差异.
- 处理缺失数据的变化,包括研究级调节者,以及对异质性的概念化.
- 直接数据分析和基于总结统计数据的方法产生了不同的结果.
结论:
- 应用研究人员需要明确的指导方针来进行个人参与者数据MASEM.
- 进一步的研究是必不可少的,以建立基于原始数据的元分析SEM的最佳实践.
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