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Description: For the majority of the last century, personality science has focused on the study of individual differences, or how individuals differ from each other in relative terms. Studying individuals wholly as complexes of interacting systems has had to take a back seat, not for lack of interest but due to methodological limitations. The ability to collect intensive longitudinal data suitable for modeling dynamic processes in multivariate space has recently burgeoned. With it has come a resurgence in using idiographic or personalized models of individuals’ personalities. However, idiographic methods remain challenging because they are cumbersome and difficult to scale, and with that is the challenge of accumulating generalizable knowledge. This workshop will offer an introduction to and overview of Group Iterative Multiple Model Estimation (GIMME), a technique that is designed to efficiently estimate idiographic multivariate statistical models while also searching for commonalities across individual models. GIMME uses an extended unified structural equation modeling approach to estimate networks of lagged and contemporaneous associations in multivariate time-series data. It uses automatic searching algorithms, which dramatically reduce the burden of doing large-scale idiographic analyses, while also searching for commonalities among each individual’s model. The workshop will cover the conceptual underpinnings of personalized models and the basics of GIMME at an applied level, discuss data requirements and modeling issues, illustrate its utility using several published and in preparation example s, and demonstrate the use of the dedicated R package.