In this course, participants will be introduced to time series analysis for modeling intensive longitudinal data in the context of the social sciences. Intensive longitudinal data are data that consist of many repeated measures (e.g., at least 25) for one or multiple cases (e.g., persons) and variables. In the context of psychology, these data are often collected through experience sampling or other forms of ecological momentary assessment. With these assessment techniques, participants are measured repeatedly throughout their daily lives, for example, through randomly prompted smartphone surveys multiple times a day. Participants report on, for example, their feelings, cognitions, and activities at the moment. During the workshop, we will discuss how the intensive longitudinal data can be analyzed with time series models, as well as the challenges and considerations in applying these techniques to the data. These techniques focus on modeling the dynamics in intensive longitudinal data and are suitable for research questions such as “Are these variables associated with themselves and each other (from variable a to b, b to a, or in both directions), over time?” On the first day, we will discuss models for single case analyses (e.g., modeling a single person at a time). On the second day, we will extend these techniques such that one can analyze multiple cases (e.g., persons) with multilevel models. These multilevel models will be estimated with Bayesian techniques using R. Mplus codes will also be available for interested participants.