Uncovering General, Shared, and Unique Temporal Patterns in Ambulatory Assessment Data
Uncovering General, Shared, and Unique Temporal Patterns in Ambulatory Assessment Data
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DOI：
10.1037/met0000192
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发表时间：
2019-02-01
影响因子：
7
通讯作者：
Wright, Aidan G. C.
中科院分区：
文献类型：
--
作者：
Lane, Stephanie T.;Gates, Kathleen M.;Wright, Aidan G. C.
密集的纵向数据为心理学研究者提供了更好地理解个体水平时间过程的潜力。虽然这些数据的收集已经变得越来越普遍，但适合分析这些数据的方法相对较少，许多方法假设个体之间具有同质性。最近的发展植根于结构方程和向量自回归模型，分组组迭代多模型估计（S-GIMME），提供了一种方法来达到由样本，样本的子集，和一个给定的个人共享的过程组成的个人水平的模型。由于该算法的动机和验证用于神经影像学数据，其性能在动态评估数据的背景下了解较少。在这里，我们评估了S-GIMME算法在日常日记（与神经成像相比）数据经常遇到的各种条件下的性能;即，变量数量较少，时间点数量较少，自回归效应较小。我们首次证明了自回归效应在恢复数据生成连接和方向方面的重要性，以及使用S-GIMME与日常日记研究中常见的数据长度的能力。我们展示了使用S-GIMME与经验的例子评估一般，共享和独特的时间过程与边缘型人格障碍（BPD）的个人样本。最后，我们强调，鉴于在心理学研究中越来越多地使用密集的纵向数据，以及这些数据为人类行为和心理健康提供新见解的潜力，需要S-GIMME等方法向前发展。
Intensive longitudinal data provide psychological researchers with the potential to better understand individual-level temporal processes. While the collection of such data has become increasingly common, there are a comparatively small number of methods well-suited for analyzing these data, and many methods assume homogeneity across individuals. A recent development rooted in structural equation and vector autoregressive modeling, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME), provides one method for arriving at individual-level models composed of processes shared by the sample, a subset of the sample, and a given individual. As this algorithm was motivated and validated for use with neuroimaging data, its performance is less understood in the context of ambulatory assessment data. Here, we evaluate the performance of the S-GIMME algorithm across various conditions frequently encountered with daily diary (compared to neuroimaging) data; namely, a smaller number of variables, a lower number of time points, and smaller autoregressive effects. We demonstrate, for the first time, the importance of the autoregressive effects in recovering data-generating connections and directions, and the ability to use S-GIMME with lengths of data commonly seen in daily diary studies. We demonstrate the use of S-GIMME with an empirical example evaluating the general, shared, and unique temporal processes associated with a sample of individuals with borderline personality disorder (BPD). Finally, we underscore the need for methods such as S-GIMME moving forward given the increasing use of intensive longitudinal data in psychological research, and the potential for these data to provide novel insights into human behavior and mental health.