a Methodology and Statistics, Faculty of Social and Behavioural Sciences , Utrecht University.
b KU Leuven.
Multivariate Behav Res. 2018 Nov-Dec;53(6):820-841. doi: 10.1080/00273171.2018.1446819. Epub 2018 Apr 6.
With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.
随着密集纵向研究的日益普及，这种数据的建模技术和软件选择也在迅速扩展。在这里，我们使用动态多层建模，因为它包含在 Mplus 中的新动态结构方程建模 (DSEM) 工具箱中，来分析 COGITO 研究中的情感数据。这些数据由两个样本组成，每个样本超过 100 人，测量时间约为 100 天。我们使用积极和消极情感的综合分数，并应用多层次向量自回归模型，以允许在均值、自回归和交叉滞后效应方面存在个体差异。然后，我们将模型扩展到包括随机残差方差和协方差，最后我们通过每日日记测量的随机效应来研究先前的抑郁是否会影响以后的抑郁分数。最后，我们讨论了动态多层建模领域的几个紧迫但大多未解决的问题。
