Understanding the longitudinal dynamics of behavior, their stability and change over time, are of great interest in the social and behavioral sciences. Researchers investigate the degree to which an observed measure reflects stable components of the construct, situational fluctuations, method effects, or just random measurement error. An important question in such models is whether autoregressive effects occur between the residuals, as in the trait–state occasion model (TSO model), or between the state variables, as in the latent state–trait model with autoregression (LST-AR model). In this article, we compare the two approaches by applying revised latent state–trait theory (LST-R theory). Similarly to Eid et al. (2017) regarding the TSO model, we show how to formulate the LST-AR model using definitions from LST-R theory, and we discuss the practical implications. We demonstrate that the two models are equivalent when the trait loadings are allowed to vary over time. This is also true for bivariate model versions. The different but same approaches to modeling latent states and traits with autoregressive effects are illustrated with a longitudinal study of cancer-related fatigue in Hodgkin lymphoma patients.

中文翻译：

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比较修订的潜在状态特征模型，包括自回归效应。

了解行为的纵向动态、它们的稳定性和随时间的变化，对社会和行为科学非常感兴趣。研究人员调查观察到的测量值在多大程度上反映了结构的稳定成分、情境波动、方法效应或只是随机测量误差。此类模型中的一个重要问题是自回归效应是否发生在残差之间，如特征-状态事件模型（TSO 模型），或状态变量之间，如具有自回归的潜在状态-特征模型（LST-AR 模型） . 在本文中，我们通过应用修正的潜在状态特征理论（LST-R 理论）来比较这两种方法。与 Eid 等人类似。（2017）关于 TSO 模型，我们展示了如何使用 LST-R 理论的定义来制定 LST-AR 模型，我们讨论实际影响。我们证明当特征负载随时间变化时，这两个模型是等效的。对于双变量模型版本也是如此。通过对霍奇金淋巴瘤患者癌症相关疲劳的纵向研究，说明了对具有自回归效应的潜伏状态和特征进行建模的不同但相同的方法。