Groot Lennert J, Kan Kees Jan, Jak Suzanne
University of Amsterdam.
Struct Equ Modeling. 2025 Jun 18:1-13. doi: 10.1080/10705511.2025.2505995.
Researchers conducting meta-analytical structural equation modeling (MASEM) with individual participant data can choose from several methods, including cluster-robust estimation, two-level SEM, multivariate meta-analysis of path coefficients, and One-Stage MASEM (OSMASEM). While two-level SEM and OSMASEM model within- and between-study effects separately, cluster-robust estimation combines them, estimating an overall path coefficient. Despite its popularity, cluster-robust estimation often yields results that differ from other methods. Simulations using factor models and real-world comparisons using path models show that it may not accurately reflect within-study estimates and can produce biased standard errors. This study compares IPD MASEM methods using simulated data, varying intraclass correlations, parameter equality across levels, number of studies, and missing data. Results reveal that cluster-robust estimation frequently misrepresents within-study estimates, produces biased standard errors, and tends to incorrectly reject model fit, highlighting the need for careful method selection in IPD MASEM applications.
使用个体参与者数据进行元分析结构方程建模（MASEM）的研究人员可以从多种方法中进行选择，包括聚类稳健估计、二级结构方程模型、路径系数的多变量元分析和单阶段MASEM（OSMASEM）。虽然二级结构方程模型和OSMASEM分别对研究内和研究间效应进行建模，但聚类稳健估计将它们结合起来，估计一个总体路径系数。尽管聚类稳健估计很受欢迎，但它得出的结果往往与其他方法不同。使用因子模型的模拟和使用路径模型的实际比较表明，它可能无法准确反映研究内估计值，并且会产生有偏差的标准误差。本研究使用模拟数据、不同的组内相关系数、各水平间的参数相等性、研究数量和缺失数据来比较IPD MASEM方法。结果表明，聚类稳健估计经常错误地表示研究内估计值，产生有偏差的标准误差，并且倾向于错误地拒绝模型拟合，这突出了在IPD MASEM应用中仔细选择方法的必要性。
