ABSTRACT

The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though often computationally intractable in the frequentist framework, can be readily handled using Markov chain Monte Carlo techniques in a Bayesian framework. However, researchers in social science fields may be unfamiliar with ways to capitalize on recent developments in Bayesian software programs. In this paper, we provide step-by-step illustrations and comparisons of options to fit Bayesian mlVAR models using Stan, JAGS and Mplus, supplemented with a Monte Carlo simulation study. An empirical example is used to demonstrate the utility of mlVAR models in studying intra- and inter-individual variations in affective dynamics.

 抽象的

密集纵向数据的涌入迫切需要复杂的建模工具，以帮助加深我们对个人如何随时间变化的理解。多级向量自回归（mlVAR）模型允许同时评估动态过程和个体差异之间的相互联系，并且近年来得到了越来越多的认可。 mlVAR 模型的高维和其他复杂变体虽然在频率论框架中通常计算起来很困难，但可以在贝叶斯框架中使用马尔可夫链蒙特卡罗技术轻松处理。然而，社会科学领域的研究人员可能不熟悉如何利用贝叶斯软件程序的最新发展。在本文中，我们提供了使用 Stan、JAGS 和 Mplus 拟合贝叶斯 mlVAR 模型的选项的逐步说明和比较，并辅以蒙特卡罗模拟研究。使用实证示例来证明 mlVAR 模型在研究个体内和个体间情感动态变化方面的实用性。