In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross‐sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time‐specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open‐source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands‐on activities in R, curated resources, and a glossary.

中文翻译：

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数字模块16：纵向数据分析

在此数字ITEMS模块中，Jeffrey Harring博士和Tessa Johnson女士介绍了线性混合效应（LME）模型，它是一种灵活的通用框架，可同时对连续重复测量的数据进行建模，并具有科学上可辩护的功能，该功能可充分总结单个变化以及平均反应。该模块从纵向数据分析的非技术性概述开始，从而根据要解决的研究问题得出与横断面分析的区别。然后讨论了纵向设计的细微差别，测量的时间安排以及丢失数据的实际可能性。LME模型的三个相互关联的部分-（1）用于个体和均值响应曲线的模型，（2）用于描述时间特定残差之间的协方差的模型，（3）在总结包括分析在内的一系列活动的背景下，讨论了总结各个系数变化程度的一组模型。最后，他们演示了如何在开源环境（R）中估计线性混合效应模型。数字模块包含示例R代码，诊断测验问题，R中的动手活动，精选资源和词汇表。