Uncovering General, Shared, and Unique Temporal Patterns in Ambulatory Assessment Data

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. (PsycINFO Database Record (c) 2019 APA, all rights reserved).