GESIS Training Courses

Wiss. Koordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Koordination

Angelika Ruf
Tel: +49 221 47694-162

Course 10: Collecting and Analyzing Longitudinal Social Network Data

Dr. Lars Leszczensky, Dr. Sebastian Pink

Datum: 16.08 - 20.08.2021 ics-Datei

Veranstaltungsort: Online via Zoom / Time: 09:00-17:00 (CEST) - including breaks

Referenteninformationen - Dr. Lars Leszczensky

Referenteninformationen - Dr. Sebastian Pink


[This is a 30 hour class.]
Social scientists often are interested in understanding how social networks emerge and/or how they shape individual behavior. These questions of network formation (“selection”) and network effects (“influence”) concern both human individuals and organizational units. Examples for selection are the emergence of friendship between people or cooperation between firms; examples for influence are adolescents start smoking because of their friends or firms copying other firms' strategies. Selection and influence are inherently dynamic processes, but few social scientists have been trained in collecting, processing and analyzing longitudinal social network data.
This practical course guides participants who intent to collect and/or analyze longitudinal social network data. We start by conceptualizing and planning data collection, discussing both general challenges and, if applicable, participants' own projects. Thereafter, participants learn how to handle and manage network data in R by guided examples and exercises. The main part of the course focuses on specifying, estimating and interpreting stochastic actor-oriented models (SAOM) for network dynamics, again with a mix of guided examples and practical exercises using the R package RSiena. We consider selection and influence as well as how SAOM can help to empirically disentangle these competing processes.


Participants will find the course useful if:
  • they (intend or consider to) collect longitudinal social network data
  • they (intend or consider to) analyze longitudinal social network data to help them answering substantive research questions
  • they already are analyzing social network data and want to discuss their work


By the end of the course participants will:
  • know how to design and conduct a longitudinal social network study
  • be able to manage and handle longitudinal network data
  • know how to exploit the potential of stochastic actor-oriented models for their research aims
  • understand how to specify and estimate stochastic actor-oriented models for network dynamics in R
  • have learned how to interpret and communicate results of stochastic actor-oriented models
    Organizational Structure of the Course:
    This is a five-day course with a total amount of 30 hours of virtual class time. Participants can expect a mix of interactive lectures, hands-on exercises, and opportunities for group discussions and individual consultation. Guided exercises in R deepen the understanding of the course material. The lecturers will be available for individual consultations on participants' planned or current projects.


  • Basic knowledge in quantitative data analysis
  • Prior knowledge of social network analysis and/or R is helpful but not necessarily required
    Software and Hardware Requirements:
    The practical examples and exercises will be done in R, so participants should have a recent version installed on their local computer. For working with R in general, we recommend using RStudio.
    Prior to the course, participants should install the following R-packages from CRAN, with dependencies:
  • statnet
  • ggplot2
  • tidyverse
  • mvmeta
  • Participants also should install the newest version of the R-package “RSiena” from R-Forge, with dependencies. (The version on CRAN tends to be outdated.) Section 2.2 of the RSiena manual (Ripley et al. 2020) explains how to install the RSiena package.