Research, Measurement & Statistics, Department of Educational Psychology, University of North Texas, Denton, TX, 76205, USA.
Behav Res Methods. 2024 Sep;56(6):6101-6118. doi: 10.3758/s13428-024-02340-4. Epub 2024 Feb 2.
We present an approach to meta-analytic structural equation models that relies on hierarchical modeling of sample covariance matrices under the assumption that the matrices are Wishart. The approach handles the commonplace fixed- and random-effects meta-analytic SEMs, and solves the problem of dependent covariance matrices where more than one covariance matrix is obtained from a single study or study author. The ability of the approach to adequately recover parameters is examined via a simulation study. The approach is implemented in the bayesianmasem R package and a demonstration shows applications of the model.
我们提出了一种元分析结构方程模型的方法，该方法依赖于样本协方差矩阵的层次模型，假设矩阵是 Wishart 分布的。该方法处理常见的固定效应和随机效应元分析 SEM，并解决了依赖协方差矩阵的问题，其中一个研究或研究作者从一个单一的研究中获得了多个协方差矩阵。通过模拟研究来检验该方法充分恢复参数的能力。该方法在 bayesianmasem R 包中实现，并展示了模型的应用。
