Inferential Network Analysis

This course is offered as part of the Radboud Summer School in Social Research Methods, in collaboration with MethodsNET (a global network that offers excellent training in social research methods).

The course will introduce statistical models for network data and other data with complex dependence among observations. A range of models will be discussed theoretically, in application and using R.

Course leader

Philip Leifeld
Professor of Comparative Politics
Department of Government
University of Essex

Target group

-Master
-PhD
-Post-doc
-Professional

This PhD level course is open to all researchers aiming at bringing their research to the next level. It is particularly designed for those who want to model complex network data in a multiple regression framework and predict network ties and behavior.

Course aim

After this course you are able to:

-Estimate a range of statistical network models with a given network dataset in R.
-Choose the right network model for a given task.
-Put your own data into the right format for statistical network analysis in R.
-Understand the statistical foundations of the different inferential network models.

Credits info

2 EC
2 ECTS credits, with the possibility of an extra 1-3 ECTS credits depending on additional course work and assignments handed in during or after the summer school (for a possible total of up to 5 ECTS).

Fee info

EUR 575: 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:
€ 518 early bird discount- deadline 1 April 2022 (10%)
€ 489 partner + RU discount (15%)
€ 431 early bird + partner + RU discount (25%)