This is a four-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 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 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 four 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.
For this course some knowledge of multilevel analysis and Bayesian statistics is preferable, but not required. Also, experience with R and/or Mplus is useful, but is not mandatory.
Please note that there is always the possibility that we have to change the course pending COVID19-related developments. The exact details, including a day-to-day program, will be communicated 6 weeks prior to the start of the course.
Prof. dr. Ellen Hamaker
dr. Noémi Schuurman
dr. Laura Bringmann
dr. Rebecca Kuiper
dr. Oisín Ryan
This course is designed 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.
A maximum of 50 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.
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A typical course day starts at 9.00 and ends at 17.00 with breaks for coffee, lunch and tea.
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.
Extra information about housing
You can choose between two options for participating in this course, but please note that there is always the possibility that we have to change the course pending COVID19-related developments:
- If you choose the livestream option, you will get a discount on the course fee since we will not provide lunch then. The lectures will be broadcasted in Central European Summer Time via a livestream (not recorded). Participants can ask questions via the chat which will be moderated by a second lecturer who will either directly answer your questions via the chat or ask your questions to the first lecturer during class. You will also receive online support during the group computer labs from our team. Additionally, Q&A sessions will be organised so you will benefit from our normal high level expertise while enjoying the class from the comfort of your own chair.
- If you choose the campus option, you will be able to attend the lectures and computer labs at our campus. Of course, we will follow all COVID19-guidelines that hold at the time of the start of your course. We will keep you updated about the newest developments (see also https://www.uu.nl/en/information-coronavirus). Note that, at the moment, it is unclear how many participants will be allowed in our lecture rooms. Therefore, if you register for the campus option, we will also register you for the livestream option such that you are guaranteed a spot via the livestream option (and at first, send an invoice for this option only). We will put you ‘on hold’ for the campus option until we have more information about how many participants are allowed in our lecture rooms. As soon as we hear from the university, we will contact you and send you a second invoice for the part of the fee related to catering and campus registration.
If you are interested in the campus option, let us know via a message in the application form under ‘Student Comment’.
The physical course costs €615, but if you participate via the livestream you will get a 80 euro discount. Note that if you choose the campus option, you will be asked to first pay the livestream-fee (€535) and, when we have permission from the university to actually organise classes on location, we will send a second invoice for the remainder of the fee. This way, you will be ensured to have at least a spot for the livestream.
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.
There are no scholarships available for this course.
Irma Reyersen | E: email@example.com