Multivariate Behav Res. 2001 Apr 1;36(2):249-77. doi: 10.1207/S15327906MBR3602_06.
This article combines procedures for single-level mediational analysis with multilevel modeling techniques in order to appropriately test mediational effects in clustered data. A simulation study compared the performance of these multilevel mediational models with that of single-level mediational models in clustered data with individual- or group-level initial independent variables, individual- or group-level mediators, and individual level outcomes. The standard errors of mediated effects from the multilevel solution were generally accurate, while those from the single-level procedure were downwardly biased, often by 20% or more. The multilevel advantage was greatest in those situations involving group-level variables, larger group sizes, and higher intraclass correlations in mediator and outcome variables. Multilevel mediational modeling methods were also applied to data from a preventive intervention designed to reduce intentions to use steroids among players on high school football teams. This example illustrates differences between single-level and multilevel mediational modeling in real-world clustered data and shows how the multilevel technique may lead to more accurate results.
本文将单水平中介分析程序与多层次建模技术相结合，以便在聚类数据中适当测试中介效应。一项模拟研究比较了这些多层次中介模型与单水平中介模型在具有个体或群组水平初始自变量、个体或群组水平中介变量以及个体水平结果的聚类数据中的表现。多层次解决方案中介效应的标准误差通常是准确的，而单水平程序的标准误差则向下偏倚，通常偏倚 20%或更多。在涉及群组水平变量、更大群组大小以及中介和结果变量中更高的组内相关的情况下，多层次的优势最大。多层次中介建模方法还应用于旨在减少高中橄榄球队球员使用类固醇意图的预防干预数据中。这个例子说明了在真实世界的聚类数据中单水平和多层次中介建模之间的差异，并展示了多层次技术如何可能导致更准确的结果。
