Nijmegen, Netherlands

Complexity Methods for Behavioural Science: A Toolbox for Studying Change

when 11 July 2022 - 15 July 2022
language English
duration 1 week
credits 2 EC
fee EUR 600

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.

Course leader

Dr. Fred Hasselman
Assistant Professor
School of Pedagogical and Educational Science
Radboud University

Target group

• Master
• PhD
• Post-doc
• Professional

All researchers who are interested in acquiring hands-on experience with applying research methods and analytic techniques to study human behaviour from the perspective of Complexity Science. Prior knowledge is not required, but some basic experience using R is highly recommended. Note: this is not a course in dynamic modelling (simulation), the focus is on data analysis.

Course aim

After this course you are able to:

1. Simulate linear, nonlinear and coupled dynamics using simple models.
2. Conduct (multi-fractal) Detrended Fluctuation Analysis and related techniques to quantify global and local scaling relations.
3. Conduct Recurrence Quantification Analysis and related techniques to quantify temporal patterns, synchronisation and coupling direction.
4. Use idiographic analysis methods and (multiplex) Recurrence Networks to quantify structure and dynamics of (multivariate) time series.

Fee info

EUR 600: This fee applies to post-docs and professionals.
The fee includes the registration fees, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities.
EUR 400: This fee applies to students and PhDs.
The fee includes the registration fees, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities.

Scholarships

We offer several reduced fees:
€ 540 / 360 early bird discount- deadline 1 April 2022 (10%)
€ 340 students/PhD from partner + RU discount (15%)
€ 300 early bird + students/PhD partner + RU discount (25%)

Register for this course
on course website