A practical course in Network Analysis for Psychological Research

Target group

PhD students from the Doctoral School of Social and Behavioural Sciences, Faculty of Psychology and Educational Sciences


Kristof Hoorelbeke
Department of Experimental Clinical and Health Psychology
Phone: +32 (0)9 264 94 16
Email: Kristof.Hoorelbeke@UGent.be
Prof. Dr. Ernst H. W. Koster, PhD
Department of Experimental Clinical and Health Psychology
Phone: +32 (0)9 264 64 89
Email: Ernst.Koster@UGent.be

Aim and scope

Students start by acquiring basic skills in order to be able to understand and conduct network analysis in the R software environment. This allows students to understand the network analyses and complete the hands-on exercises during the second and third day of the network analysis course.
At the end of this course the students should:

  • Be familiar with the R environment and the available packages for network analysis
  • Know the elementary commands and statements used in R and network analysis packages
  • Understand the theoretical underpinnings of network models and their potential applications
  • Be able to set-up designs to collect (cross-sectional and longitudinal) data for network analysis
  • Be able to apply network analysis to cross-sectional and longitudinal data (i.e., setting-up and interpreting cross-sectional group-level networks and dynamical networks)
  • Be able to report output of network analyses
  • Have knowledge regarding stability and precision of network models and be aware of more advanced techniques such as network comparison


  • Day 1: R environment, functions and packages for network analysis [Maarten De Schryver]

9:00 Registration
9:30 1. Introduction to R and RStudio, Arithmetics in R, Vectors in R
11:00 2. Factors and levels, Adding dimensions, Reading in Data
12:00 Break
1:00 3. Manipulating data frames, Summarizing data, Working with lists
3:30 4. Functions, Visualizing data, and Reporting
5:00 End of day 1

  • Day 2: Cross-sectional group-level networks: Estimation, inference, and stability [Eiko Fried + Marie Deserno]

9:00 1. Introduction to psychopathological networks
10:30 2. Network estimation: How to construct networks
12:30 Break
1:30 3. Network Inference: How to interpret networks
2:00 4. Robustness of networks: How stable and precise are networks
3:00 5. Practical with large dataset
4:00 6. Advanced methods such as network comparison
5:00 End of day 2

  • Day 3: Dynamical network analysis with intra-individual data [Marie Deserno + Eiko Fried]

9:00 1. Introduction to dynamical networks
10:30 2. Introduction to Vector Auto-Regression (VAR) models
11:30 3. Multilevel extension of VAR models
12:30 Break
1:30 4. Practical with data
2:30 5. Design considerations
3:00 6. Manuscript preparation considerations
3:30 7. Q & A about data and ideas
4:30 Wrap-up

Dates and Venue

17 - 19 January 2017 (Tue to Thu) from 9:00 till 17:00
Computer Class (PC-Lokaal 1), Henri Dunantlaan 1, Faculty of Psychology and Educational Science / HILO, Ghent University, 9000 Ghent


  • Maarten De Schryver, MSc

Statistical consultant at the LIP Lab, Department of Experimental Clinical & Health Psychology, Ghent University
Henri Dunantlaan 2, 9000 Ghent, Belgium
+(32)09-264-91-07 - Maarten.DeSchryver@UGent.be

  • Eiko Fried, PhD

Postdoctoral researcher, Department of Psychology, University of Amsterdam
Nieuwe Achtergracht 129, REC G, 1018 Amsterdam, The Netherlands
+(31) 61-433-82-77 - Eiko.Fried@gmail.com - www.eiko-fried.com

  • Marie Deserno, MSc

PhD student, Department of Psychology, University of Amsterdam
Nieuwe Achtergracht 129, REC G, 1018 Amsterdam, The Netherlands
+(31) 20-525-24-27 - M.K.Deserno@uva.nl

Study material

The instructors will provide the students of a syllabus and will give theoretical presentations which will be alternated with practical demonstrations and hands-on computer exercises in the R environment.

Used Software

The course will be taught using R (free statistical open-source software) packages relevant for network analysis.


The course is fully booked. You can ask to be added to the waiting list by e-mail to

Number of participants

Maximum 15 PhD students



Evaluation criteria (doctoral training programme)

100% active participation

Registration fee

Free of charge for members of the Doctoral School of Social and Behaioural Sciences