RSS04.13 Complexity methods for Behavioral Science: A toolbox for studying change

This course will discuss analytic techniques that allow for the study of human behaviour from the perspective of the Complexity Sciences. Techniques include fluctuation analyses, nonlinear time series analyses, multiplex recurrence networks.

Complexity research transcends the boundaries between the classical scientific disciplines and is a hot topic in physics, mathematics, biology, economy as well as psychology and the life sciences. This course will discuss techniques that allow for the study of human behaviour from the perspective of the Complexity Sciences, specifically, Complex Adaptive Systems.

Contrary to what the term “complex” might suggest, complexity research is often about finding simple models/explanations that are able to describe a wide range of qualitatively different behavioural phenomena. “Complex” generally refers to the object of study: Complex systems are composed of many constituent parts that interact with one another across many different temporal and spatial scales to generate behaviour at the level of the system as a whole, in complex systems “everything is interacting with everything”.

The idea behind many methods for studying the dynamics of complex systems is to exploit this fact and quantify the degree of interdependence, periodicity, nonlinearity, context sensitivity or resistance to perturbation (resilience) of system behaviour. Applications in the behavioural sciences are very diverse and concern analyses of continuous time or trial series data such as response times, heart rate variability or EEG to assess proficiency of skills, or health and well-being.

Complexity methods can also be used for the analysis of categorical data, such as behaviour observation of dyadic interactions (client-therapist, child-caregiver), daily experience sampling, social and symptom networks. The complex systems approach to behavioural science often overlaps with the idiographic approach of “the science of the individual”, that is, the main goal is not to generalise properties or regularities to universal or statistical laws that hold at the level of infinitely large populations, but to apply general principles and universal laws that govern the adaptive behaviour of all complex systems to a specific case, in a specific context, at a specific moment in time.

The main focus of the course will be hands-on data-analysis. Practical sessions will follow after lecture sessions in which specific techniques will be introduced.


10 July 2023 - 14 July 2023
Course Fee

Regular: €600

Students & PhDs: €400

Early Bird Regular: €540 (application deadline* April 1st)

Early Bird Students & PhDs: €360 (application deadline* April 1st)

Scholarships and discounts Find more information here
Application deadline

May 1st

*Your application is only completed when the course fee has been paid

Course leader Fred Hasselman
Level of participant
  • Master
  • PhD
  • Postdoc
  • Professional
Admission requirements During the course we will mostly be using the R statistical software environment. Basic experience with R is highly recommended (e.g. installing packages, calling functions that run analyses, handling and plotting data).We also offer a module for the Jamovi software with which the most basic analyses can be conducted. Using Jamovi does not require any prior knowledge of R, but you will not be able to use more advanced features of certain analyses.Please install R or Jamovi on your computer beforehand. The specifications for your computer are simply this: You need to be able to connect to a wireless network (wifi) and you should be able to install and run the latest version of R. In addition, you might want to be able to use the latest versions of RStudio and Jamovi.
Admission documents A short bio which also includes your research interests
Mode of Study On Campus
Location Radboud University