This review focuses on the use of multilevel models in psychology and other social sciences. We target readers who are catching up on current best practices and sources of controversy in the specification of multilevel models. We first describe common use cases for clustered, longitudinal, and cross-classified designs, as well as their combinations. Using examples from both clustered and longitudinal designs, we then address issues of centering for observed predictor variables: its use in creating interpretable fixed and random effects of predictors, its relationship to endogeneity problems (correlations between predictors and model error terms), and its translation into multivariate multilevel models (using latent-centering within multilevel structural equation models). Finally, we describe novel extensions—mixed-effects location–scale models—designed for predicting differential amounts of variability.

中文翻译：

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赶上多层次建模


本综述重点关注多层次模型在心理学和其他社会科学中的使用。我们的目标读者是那些正在了解多级模型规范中当前最佳实践和争议来源的读者。我们首先描述集群、纵向和跨分类设计及其组合的常见用例。然后，使用聚类和纵向设计中的示例，我们解决了观察到的预测变量的居中问题：它在创建预测变量的可解释的固定和随机效应中的用途、它与内生性问题的关系（预测变量和模型误差项之间的相关性）及其翻译转化为多元多级模型（在多级结构方程模型中使用潜在中心化）。最后，我们描述了新颖的扩展——混合效应位置尺度模型——设计用于预测差异量的变异性。