University of Amsterdam, Amsterdam, The Netherlands.
Res Synth Methods. 2024 Nov;15(6):872-895. doi: 10.1002/jrsm.1735. Epub 2024 Aug 13.
Researchers may have at their disposal the raw data of the studies they wish to meta-analyze. The goal of this study is to identify, illustrate, and compare a range of possible analysis options for researchers to whom raw data are available, wanting to fit a structural equation model (SEM) to these data. This study illustrates techniques that directly analyze the raw data, such as multilevel and multigroup SEM, and techniques based on summary statistics, such as correlation-based meta-analytical structural equation modeling (MASEM), discussing differences in procedures, capabilities, and outcomes. This is done by analyzing a previously published collection of datasets using open source software. A path model reflecting the theory of planned behavior is fitted to these datasets using different techniques involving SEM. Apart from differences in handling of missing data, the ability to include study-level moderators, and conceptualization of heterogeneity, results show differences in parameter estimates and standard errors across methods. Further research is needed to properly formulate guidelines for applied researchers looking to conduct individual participant data MASEM.
研究人员可能掌握了他们希望进行荟萃分析的研究的原始数据。本研究的目的是为那些可以获得原始数据、希望将结构方程模型（SEM）应用于这些数据的研究人员确定、说明和比较一系列可能的分析选择。本研究说明了直接分析原始数据的技术，如多层次和多群组 SEM，以及基于汇总统计数据的技术，如基于相关的荟萃分析结构方程建模（MASEM），讨论了程序、能力和结果的差异。这是通过使用开源软件分析以前发表的数据集来完成的。使用不同的 SEM 技术，对反映计划行为理论的路径模型进行了拟合。除了在处理缺失数据、包含研究水平调节变量以及异质性概念化方面的差异外，结果还显示了不同方法在参数估计和标准误差方面的差异。需要进一步研究，为希望进行个体参与者数据 MASEM 的应用研究人员制定适当的指南。
