Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a statewide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software package.

中文翻译：

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多层次多层次结构方程建模：一种有效的评估策略


在混合回归过于不灵活的情况下，结构方程模型越来越多地用于聚类或多级数据。然而，当有很多嵌套级别时，这些模型可能会变得难以估计。我们引入了一种新颖的评估策略 Rampart，它将正交旋转应用于模型中符合通常满足要求的部分。这种轮换极大地简化了拟合评估，并且随着数据集大小的增加而变得更加有效。我们使用三级潜在回归模拟研究来验证和评估实施情况。然后，我们分析了俄克拉荷马州人类服务部管理的全州儿童行为健康措施的数据。我们使用具有潜在方差 5 级分解的潜在因子模型来证明 Rampart 与其他类似软件相比的效率。 Rampart 在 OpenMx 中实现，OpenMx 是一个免费的开源软件包。