Pennsylvania State University, University Park, PA, USA.
Behav Res Methods. 2019 Feb;51(1):295-315. doi: 10.3758/s13428-018-1101-0.
With the recent growth in intensive longitudinal designs and the corresponding demand for methods to analyze such data, there has never been a more pressing need for user-friendly analytic tools that can identify and estimate optimal time lags in intensive longitudinal data. The available standard exploratory methods to identify optimal time lags within univariate and multivariate multiple-subject time series are greatly underpowered at the group (i.e., population) level. We describe a hybrid exploratory-confirmatory tool, referred to herein as the Differential Time-Varying Effect Model (DTVEM), which features a convenient user-accessible function to identify optimal time lags and estimate these lags within a state-space framework. Data from an empirical ecological momentary assessment study are then used to demonstrate the utility of the proposed tool in identifying the optimal time lag for studying the linkages between nervousness and heart rate in a group of undergraduate students. Using a simulation study, we illustrate the effectiveness of DTVEM in identifying optimal lag structures in multiple-subject time-series data with missingness, as well as its strengths and limitations as a hybrid exploratory-confirmatory approach, relative to other existing approaches.
随着密集纵向设计的最近增长和对分析此类数据的方法的相应需求，现在比以往任何时候都更需要用户友好的分析工具，这些工具可以识别和估计密集纵向数据中的最佳时滞。现有的用于识别单变量和多变量多主体时间序列中最佳时滞的标准探索性方法在群体（即人群）水平上的功效大大降低。我们描述了一种混合探索性-验证性工具，称为差分时变效应模型（DTVEM），它具有一个方便的用户可访问功能，用于识别最佳时滞并在状态空间框架内估计这些时滞。然后，使用来自实证生态瞬间评估研究的数据来演示拟议工具在识别研究一组大学生神经质与心率之间联系的最佳时滞方面的效用。通过模拟研究，我们说明了 DTVEM 在识别具有缺失的多主体时间序列数据中的最佳滞后结构方面的有效性，以及相对于其他现有方法，它作为混合探索性-验证性方法的优势和局限性。
