Modeling the dynamics of intensive longitudinal data

Organizing institution
Utrecht University - Faculty of Social and Behavioural Sciences
Course code
Course fee (excl. housing)
€ 600.00
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This is a five-day course on how to study dynamics in intensive longitudinal data, such as ambulatory assessments (AA), experience sampling method (ESM) data, ecological momentary assessments (EMA), real time data capture, observational data, or electronic daily diaries. We provide a tour of diverse modeling approaches for such data and the philosophies behind them, as well as practical experience with these modeling techniques using different software packages (including R and Mplus).

Technological developments such as smart phones, 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 modeling innovations that are designed to handle the unique challenges of such intensive longitudinal data and uncover the most valuable and 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. Moreover, when data come from multiple cases (e.g., individuals or dyads), we can also study the similarities and differences in the means, variability, and dynamics of these cases.

On day 1 of this five day course we begin with grounding ourselves in N=1 time series analysis as it has been employed for decades in econometrics. We will cover basic topics such as the ARIMA model, the autocorrelation function, stationarity, unit roots and trends. Building on this basis, we will discuss N>1 extensions and dynamics multilevel modeling during day 2, and emphasize the importance of separating within-person dynamics from between-person differences. On day 3 we will discuss measurement issues, including some modeling solutions such as models that account for measurement error. Additionally, we discuss dynamic network analysis. On day 4, we discuss continuous time modeling and changes in dynamics. On the final day we have guest speakers highlighting their research in this field, and we will have an open group discussion where all participants are invited to join. 

For this course some knowledge of multilevel analysis and Bayesian statistics is preferable, but not required. Also, experience with R and/or Mplus will be useful, but is not mandatory.


Participants are requested to bring their own laptop for lab meetings.

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

Course director

Prof. Dr. Ellen Hamaker


Prof. Dr. Ellen Hamaker

Dr. Noémi Schuurman

Dr. Laura Bringmann

Dr. Rebecca Kuiper

Oisín Ryan (MSc)

Target audience

This course is for researchers who are interested in gaining more insight in the diverse 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 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.

Course aim

Provide a broad overview of challenges and solutions associated with studying the dynamics in intensive longitudinal data.

Study load

Five days, 9:00-17:00.


Course fee
€ 600.00
Housing fee
€ 200.00

Housing through Utrecht Summer School

Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.


To spare the environment, we only provide digital course material (a few days before the start of the course).

More information



Application deadline: 02 August 2019