GESIS Training Courses

Scientific Coordination

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

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

Course 9: Collecting and Analyzing Longitudinal Social Network Data

Cologne / Unter Sachsenhausen 6-8
Course duration:
Mo: 10:00-17:00 CEST
Tu-Fr: 9:00-17:00 CEST
General Topics:
Course Level:
Software used:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Lars Leszczensky, Sebastian Pink

About the lecturer - Lars Leszczensky

About the lecturer - Sebastian Pink

Course description

Please note: There is a trade fair in Cologne during this week. We recommend that you book your hotel accommodation early.
Many social scientists 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 course guides participants who intend to collect and/or analyze longitudinal social network data. For this purpose, we rely on a mix of interactive lectures, individual and group work, guided examples, and practical exercises. We use R for all guided examples and exercises, and we use and provide exemplary school-based friendship network data.
On Day 1, we introduce basic concepts, typical research questions, and longitudinal social network data. Participants can bring forward their own research aims. Participants further learn how to handle and manage network data in R by guided examples and exercises, including the visualization of longitudinal networks. On Day 2, we cover the design of longitudinal social network studies and the collection of longitudinal social network data, discussing both general challenges and, if applicable, participants' own data collection projects. Further, we will introduce stochastic actor-oriented models (SAOM) for the co-evolution of networks and behavior.  
On Day 3 and 4, we address how to analyze selection (Day 3) and influence (Day 4) with SAOM. On both days, we first introduce the respective model and show and practice how to specify and estimate it using R.  Then we practice how to interpret the model results and graphically communicate findings.
On Day 5, we address several advanced topics that participants likely will encounter when working with SAOM, such as convergence in parameter estimates, goodness of fit, and different means of analyzing multiple networks. We close by giving participants group-based and individual feedback on their own projects.
A detailed syllabus will soon be available for download here.

Target group

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 answer substantive research questions.
  • they already are analyzing social network data and want to discuss their work.

  • Learning objectives

    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 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 class time. Participants can expect a mix of interactive lectures, individual and group work, hands-on exercises, quizzes, and opportunities for group discussions with the instructors and participants with similar interests. Guided exercises in R deepen the understanding of the course material and may be used as a syntax template for own research. The lecturers will be available for individual consultations on participants' planned or current projects.


  • Basic knowledge in quantitative data analysis is required.
  • Prior knowledge of R is not necessarily required, but we strongly recommend participants without such knowledge to familiarize themselves with R before the course (we provide suggestions in the preparatory reading section).
  • Prior knowledge of social network analysis is helpful but not necessarily required.
    Software and hardware requirements
    The practical examples and exercises will be done in R. Participants should bring their own laptop computers to be able to work with R. They should have a recent R version installed. For working with R in general, we recommend using RStudio. Both R ( and RStudio ( are free and open source.
    Before the course, participants should install the following R-packages from CRAN, with dependencies: tidyverse, tidygraph, haven, ggraph, cowplot, reshape2, gridExtra, sna, igraph
    Participants also should install the newest version of the R-package “RSiena” from github, with dependencies. (The version on CRAN tends to be outdated.) The command is:  remotes::install_github("snlab-nl/rsiena", ref = "main")