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

Description

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.

Topics covered

• Introduction to intensive longitudinal data and N=1 time series analysis (week 1-4)
  We cover basic topics such as the autocorrelation function, (vector) autoregressive models, the ARIMA model, psychological dynamic networks, and stationarity. This section of the course also contains an intro to Bayesian modeling. Note: We focus mainly on techniques for continuous outcome variables throughout the course.

• Extensions for N=1 time series analysis (week 5-7)
  We discuss extensions of the models in the previous weeks such that they account for measurement error, and non-stationary models that account for changing dynamics over time.
• Continuous time modeling (week 8-10)
  We discuss the basics of continuous time models for modeling single cases.
• Modeling multiple subjects (week 11-14)
  We discuss how to model time series data for multiple subjects (participants), with an emphasis on multilevel extensions of the single-subject time series models discussed in week 1-4. 

We wrap up the course with a consultation-style panel discussion with our lecturers, where we will look at specific challenges that participants have encountered, and discuss if and how these could be solved. By doing this in a plenary setting, participants will be able to learn from each other’s experiences.

Pre-requisite knowledge

• A solid understanding of multiple linear regression and null hypothesis testing. 
• Basic knowledge of latent variable modelling (such as exploratory or confirmatory factor analysis, or structural equation modelling). 
• Knowledge of multilevel analysis is useful, but not required.
• Knowledge of Bayesian analysis is useful, but not required*.

*Note: Multiple of the discussed techniques, particularly some of the more advanced methods like the multiple subject models, will require Bayesian estimation methods in practice (in this course we use either DSEM in Mplus or with JAGS via R). We provide a brief introduction to Bayesian estimation along with this course, to get participants familiar with this approach. Prior experience with Bayesian statistics will be very helpful to get the most out of this course, but it is not required. 

Certificates

If desired, we can provide you with a certificate upon completion of this course. 

There are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. 

For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here

Lecturers

Dr. Noémi Schuurman
Prof. dr. Ellen Hamaker
Dr. Rebecca Kuiper
Dr. Laura Bringmann
Dr. Jeroen Mulder
Dr. Oisín Ryan

Target audience

This course is designed for researchers who are interested in gaining more insight in modeling 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 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 60 participants will be allowed in this course. Please note that the selection for this course will be done on a first-come-first-served basis.

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.

Learning goals:

• 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.

Study load

The course combines self-study using videos and exercises (in R and/or Mplus), with optional online Q&A and practical sessions (lab meetings). Aside from these Q&A and lab meetings, participants can also ask written questions via our online platform as they self-study.

Studying all content takes about 60 hours of study time in total. The course runs from the start of October 2025 until the end of January, 2026. The online materials remain available until the end of March, 2026. See the schedule for the online meetings below.

Note: Participants can also choose to not adhere to our Q&A/lab session schedule, and work at their own pace from October 2025 – March 2026.

Schedule for online meetings:

25 September 2025, 16:00-17:00 CET – Kick off meeting 

Intro to ILD and single case time series analysis
09 October 2025, 16:00-17:00 CET – Q&A 1
23 October 2025, 15:00-17:00 CET – Lab meeting 1

Extensions for single case time series analysis
06 November 2025, 16:00-17:00 CET – Q&A 2
13 November 2025, 15:00-17:00 CET – Lab meeting 2

Continuous time modeling 
27 November 2025, 16:00-17:00 CET – Q&A 3
04 December 2025, 15:00-17:00 CET – Lab meeting 3

Modeling multiple subjects
08 January 2026, 16:00-17:00 CET – Q&A 4
22 January 2026, 15:00-17:00 CET – Lab meeting 4
29 January 2026, 15:00-18:00 CET – Wrap up meeting

Course materials available until March 31, 2026.

Tags

• M&S Statistics Data science
• Dynamic multilevel modelling
• dynamic networks
• time series
• experience sampling
• Ambulatory assessments
• autocorrelation
• Dynamic SEM
• M&S