The course will introduce experience sampling methods (ESM) applied to psychological research. ESM include a number of techniques and designs based on the longitudinal and frequent collection of data from research participants, such as emotional experiences, perceptions of events, self-states, physiological parameters, etc. Compared to traditional designs, these methods increase ecological validity and allow investigating dynamic psychological processes. The main data collection strategies will be outlined (e.g., event- or time-contingent) with a particular focus on survey data and the dos and don’ts to consider when building an ESM study. Advantages and limitations of ESM will be reviewed. Finally, an overview of the main techniques to analyze data will be offered. The course will specifically cover the basics of multilevel models and how to conduct them in the R environment to unravel the links between relevant variables, including basic strategies to test contemporaneous, autoregressive, and cross-lagged associations between repeatedly measured constructs.