GESIS Training Courses

Scientific Coordination

Alisa Remizova

Administrative Coordination

Noemi Hartung
Tel: +49 621 1246-211

Collecting and Analyzing Longitudinal Social Network Data

Online via Zoom
Additional links
Lecturer(s): Lars Leszczensky, Sebastian Pink

About the lecturer - Lars Leszczensky

About the lecturer - Sebastian Pink

Course description

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 of selection are the emergence of friendship between people or cooperation between firms; examples of influence are adolescents starting 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 and cover the design of longitudinal social network studies and the collection of longitudinal social network data. We discuss both general challenges and, if applicable, participants' own data collection projects. You can bring forward your own research aims. You further learn how to handle and manage network data in R by guided examples and exercises. On Day 2, we introduce stochastic actor-oriented models (SAOM) for the co-evolution of networks and behavior, addressing both how to analyze selection (Day 2) and influence (Day 3) 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. We close on Day 3 by briefly addressing advanced topics that you likely will encounter when working with SAOM, such as convergence in parameter estimates, goodness of fit, and different means of analyzing multiple networks.
Organizational Structure of the Course
This is a three-day course with a total amount of 18 hours of class time. You can expect a mix of interactive lectures, individual and group work, hands-on exercises, quizzes, and opportunities for group discussions with both 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 your planned or current projects.

Target group

You will find the course useful if:
  • you (intend or consider to) collect longitudinal social network data
  • you (intend or consider to) analyze longitudinal social network data to help you answer substantive research questions
  • you are already analyzing social network data and want to discuss your work

Learning objectives

By the end of the course, you 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 your 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


  • Basic knowledge of quantitative data analysis (e.g., linear and logistic regression)
  • Prior knowledge of R is not 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 required
Software and Hardware Requirements
R; R packages: RSiena, tidyverse, tidygraph, haven, ggraph, cowplot, reshape2, gridExtra, sna, igraph


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