We discuss the differences between several intensive longitudinal data models. The dynamic structural equation model (DSEM), the residual dynamic structural equation model (RDSEM) and the repeated measures longitudinal model are compared in several simulation studies. We show that the DIC can be used to select the correct modeling framework. We discuss the consequences of incomplete or incorrect modeling for the predictors in multilevel time series models. We also illustrate the advantages of the Bayesian estimation over the REML estimation for models with categorical data, subject-specific autocorrelations, and subject-specific residual variances. Dynamic factor analysis models are discussed where autoregressive relations occur not only for the factors but also for the residuals of the measurement variables. The models are also illustrated with an empirical example.

中文翻译：

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密集纵向数据分析模型的比较

我们讨论了几个密集的纵向数据模型之间的差异。动态结构方程模型 (DSEM)、剩余动态结构方程模型 (RSEM) 和重复测量纵向模型在几个模拟研究中进行了比较。我们展示了 DIC 可用于选择正确的建模框架。我们讨论了多级时间序列模型中预测变量的不完整或不正确建模的后果。对于具有分类数据、特定主题的自相关和特定主题的残差方差的模型，我们还说明了贝叶斯估计相对于 REML 估计的优势。讨论了动态因子分析模型，其中不仅因子而且测量变量的残差都存在自回归关系。