Abstract

When latent constructs are measured by observed indicators from individuals nested within groups, multilevel structural equation modeling (MSEM) for 1-1-1 mediation designs allows researchers to simultaneously test indirect effects at each level of the data structure. However, with small samples (i.e., few clusters and/or small cluster sizes), such complex mediation models often run into estimation problems like nonconvergence, biased estimates, and insufficient power. Although Bayesian estimation with accurate informative priors can help alleviate these problems, it is unrealistic in practice to assume priors are correctly specified at the true population value. This study evaluates the performance of inaccurate (informative) priors in 1-1-1 MSEM mediation under varying sample sizes, ICCs, and effect sizes. Results indicate that while within-level indirect effect estimates are somewhat robust to inaccurate priors, between-level estimates are severely impacted, especially at small sample sizes. Implications and recommendations for conducting 1-1-1 MSEM mediation with Bayesian methods are discussed.

摘要

当通过嵌套在组中的个体的观察指标来测量潜在结构时，1-1-1 中介设计的多级结构方程模型 (MSEM) 允许研究人员同时测试数据结构每个级别的间接影响。然而，对于小样本（即很少的集群和/或小集群规模），这种复杂的中介模型经常会遇到估计问题，如不收敛、估计有偏差和功率不足。尽管具有准确信息先验的贝叶斯估计可以帮助缓解这些问题，但在实践中假设先验正确地指定为真实总体值是不现实的。本研究评估了在不同样本量、ICC 和效应量下 1-1-1 MSEM 调解中不准确（信息丰富）先验的表现。结果表明，虽然级别内间接效应估计对不准确的先验有一定的鲁棒性，但级别间估计受到严重影响，尤其是在小样本量下。讨论了使用贝叶斯方法进行 1-1-1 MSEM 调解的含义和建议。