This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.,The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.,From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.,Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

中文翻译：

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纵向数据的多级建模：概念和应用

本文旨在讨论纵向数据的多级建模，阐明它们可以使用的情况。,作者估计了重复测量的三级模型，为其正确解释提供了条件。,从提出的概念和技术，作者可以提出模型，其中可以识别对因变量的固定和随机效应，了解多级随机效应的方差分解，测试替代协方差结构以解释异方差，并计算和解释每个分析级别的类内相关性。 , 了解嵌套数据结构和重复测量数据的工作原理使研究人员和管理人员能够定义几种类型的结构，从中可以使用多级模型。