This online course covers how time series models can be used to model the dynamics of intensive longitudinal data (ILD). We focus on 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.
This course is designed for researchers who are interested in gaining more insight into modelling approaches for intensive longitudinal data, with a specific focus on the underlying dynamics (i.e. lagged relationships). While there will be computer labs to obtain some hands-on experience, the emphasis in this course is on obtaining an overview of the diverse challenges associated with these data, and the different philosophies behind the techniques that have been designed to tackle these.
A maximum of 50 participants will be admitted to this course. Please note that the selection for this course will be done on a first-come-first-served basis.
Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.
Aim of the course
The aim of the course is to provide a broad overview of challenges and solutions associated with studying the dynamics in intensive longitudinal data with (extensions of) time series models, in the context of the Social and Behavioural sciences.
- Attain basic knowledge of the basics of time series analysis, including basic descriptives, concepts like stationarity, ARIMA models, dynamic networks;
- Attain a thorough understanding of the interpretation of univariate and multivariate single subject autoregressive models applied in the Social and Behavioural sciences, including important caveats;
- Attain basic knowledge about techniques for modelling non-stationary time series data, and time series data that contains measurement errors;
- Attain basic knowledge about discrete vs continuous time modelling approaches, including considerations of equally spaced measurements, rethinking mediation;
- Awareness of the within/between problem when modelling multiple subject data, and techniques to counter the problem, and an understanding of related important concepts like ergodicity;
- Attain a thorough understanding of multilevel extensions of univariate and multivariate autoregressive models applied in the - Social and Behavioural sciences, including important caveats;
- To get practical experience with the above approaches in R or Mplus.
The course combines self-study using videos and exercises (in R and/or Mplus), with either an online Q&A session (one hour) or a practical session (two hours) every week. Participants will spend about four hours per week on the course, for a ten-week period.
Participants can choose to spend more or less time on specific topics in the course.