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, processes shared by a subset of the sample, and processes unique to a given individual. As this algorithm was motivated and validated for use with neuroimaging data, its performance and utility is less understood in the context of ambulatory assessment data collected by psychologists. 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. Importantly, 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 the S-GIMME algorithm 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.
At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study
