Modelling the Dynamics of Intensive Longitudinal Data (e-learning) 2025

During this e-learning course we focus on analysing intensive longitudinal data (ILD) from the behavioural sciences, typically collected using questionnaires with ambulatory assessments (AA), the experience sampling method (ESM), ecological momentary assessments (EMA), or daily diaries. We explain the basics of simple and more advanced modelling approaches, the philosophies behind them, and caveats to consider (see list below for more info on the exact topics that we cover).

Technological developments such as smartphones, activity trackers, and other wearables have made it relatively easy to obtain many repeated measurements per person in a relatively short period of time. In response to these measurement innovations, there is a surge of statistical modelling innovations that are designed to handle the unique challenges of such intensive longitudinal data and uncover meaningful properties of these data.

A particular appealing aspect of such data is that the observations are ordered in time, thereby allowing us to study the dynamic relationships between variables over time: how future observations depend on past observations. This can be done with autoregressive time series models for single cases (e.g., individuals or dyads), but also with multilevel extensions for multiple cases. In the latter case, we can additionally study the similarities and differences in the means, variability and dynamics of these different cases. During this course, we discuss these techniques and extensions of these techniques, illustrated with empirical examples from the social and behavioral sciences. We focus mainly on techniques for continuous outcome variables throughout the course.