GESIS Training Courses
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Wiss. Koordination

Dr.
Marlene Mauk
Tel: +49 221 47694-579

Administrative Koordination

Loretta Langendörfer M.A.
Tel: +49 221 47694-143

Social Network Analysis with R

About
Veranstaltungsort:
Online via Zoom
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 400 €
Academics: 600 €
Commercial: 1200 €
Keywords
Additional links
Dozent(en): Dr. Silvia Fierascu, Ianis Rusitoru

Referenteninformationen - Dr. Silvia Fierascu

Referenteninformationen - Ianis Rusitoru

Seminarinhalt

Social Network Analysis has become an integral part of a Computational Social Scientist training. The methodological toolkit is powerful, because it is strongly rooted in interdisciplinary theory, techniques, measurements, and interpretations. It is thus suitable for either independent explorations of data and research questions (from small to large-N cases), as well as in mixed-methods research designs, complementing both qualitative and quantitative methodologies. In this class, participants learn the fundamentals of social network theory, methods and techniques, and their application to diverse sets of research problems. We cover topics such as: (1) what it means to work with network data and how to do data collection (from various sources), (2) analysis of networks at the macro-level (network structures), meso-level (network groups and communities), and micro-level (network positions of individual nodes), and (3) hypothesis testing and causal analysis. By the end of the class, participants will be autonomous in conducting Social Network Analysis to better understand the emergence, change and dissolution of social networks, mechanisms at work in complex networks, and principles of network design in organizations, social movements, and communication flows. We will work in the software R, and participants are encouraged to bring their own dataset for a comprehensive analysis during the course.
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.


Zielgruppe

Participants will find the course useful if:
  • They want to apply Social Network Analysis as a comprehensive research methodology in their projects or if they would like to combine SNA with complementary methodologies, such as process tracing, interviews, statistical analysis, etc.
  • They need to find an integrative perspective for different levels of analysis
  • They are interested in theorizing, analyzing and visualizing complex interaction ecosystems in any social scientific field (people, organizations, countries, regions, tasks, beliefs, actions, events, etc.).


  • Lernziel

    By the end of the course participants will:
  • Understand the theoretical, conceptual and methodological foundations of Social Network Analysis and its application across social scientific fields
  • Conduct independent exploratory analyses and visualizations of complex network data
  • Identify opportunities and limitations in research design and empirical analyses of networked phenomena  
  •  
    Organisational Structure of the Course:
  • around 1.5h of pre-recorded lectures and software tutorials prior to each day of the course
  • 2h of live sessions daily
  • 1.5h allocated time from instructors for on demand one-on-one consultations


  • Voraussetzungen

    No prerequisites are necessary for taking this course. Social Network Analysis is compatible with both qualitative and quantitative methodologies, and it can add value to both less as well as more advanced research projects, in academia or practice.
    Participants are encouraged to bring and use their own research data into the class. If one does not yet have such a dataset, we will provide exercise datasets, as close as possible to the field of interest of participants.
     
     
    Software requirements:
    During this course, we focus primarily on using RStudio for statistical programming of network analysis. Participants are thus kindly asked to download and install the latest versions of RStudio and R on their local machines prior to the start of the class. We encourage participants to also install the latest version of Gephi, a point-and-click social network analysis software, for large graph visualizations.  
    If using a university laptop/computer, please consult with your local IT team for possible permissions to install new software.