A master level course.
Basic level of Statistics.
The course offers an overview of an increasingly popular approach for studying social relations: Social Network Analysis (SNA). SNA involves theoretical concepts and analytical techniques to uncover the social relations of individuals and organizations in specific socio-economic and environmental systems. These systems are characterized by interdependencies and complexities: SNA allows to investigate these elements by managing network data. The aim of this course is to provide a theoretical and methodological background of SNA, and illustrating the basic tools for applying SNA to agricultural and environmental topics. At the end of the course, the student will have a working knowledge of the data, methods, and software required to perform SNA.
Teaching period IV
- History of Social Network Analysis
- Network data: nodes and relationships; one-mode and two-mode networks; ego networks.
- Graphs and adjacency matrices: how to manage and visualize network data.
- Network statistics: actor level and network level.
- Statistical models: ERGM and longitudinal models.
- Applications of Social Network Analysis to agricultural and environmental topics.
Distributed in class: articles, case studies. Use of real network data from agricultural and environmental case studies in order to apply Social Network Analysis theory.
Handbook: Prell, C. (2012). Social network analysis: history, theory & methodology. SAGE.
In-class discussion, articles, case studies. Presentation of the usage of some of the most common software for Social Network Analysis (Ucinet, R).
Attendance not required. However, participants that will attend 80% of the classes will be allowed to present, as final exam, a case-study groupwork (small groups of 3-4 people) that will account for the finale grade. Those that will not be able to attend the course, will have to take a final written exam (scale 1-5).
Due to current COVID-19 situation general examinations in lecture halls are cancelled. You can contact the teacher to ask about alternative completion methods.
In-class lectures; case-study analysis; usage of Social Network Analysis software (Ucinet and R).
Dr. Stefano Ghinoi.