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Instructors: Dr. Robert Hellpap
Event type:
Seminar / exercise
Org-unit: Graduate School | ZUGS
Displayed in timetable as:
Network Analysis
Credits:
4,0
Location:
Campus der Zeppelin Universität
Language of instruction:
Englisch
Min. | Max. participants:
5 | 15
Course content:
Social network analyses relate the analyses of social actors and their relationships with each other. The goal is to investigate research questions which combine attributes of actors and their position in the social dependency structures. This increases the complexity of theoretical approaches as well as statistical analysis. However, the networkparadigm allows for an extensive opportunity of gaining knowledge about social realities.
Research on social networks investigates questions such as:
· What network structures provide better problem solving in groups?
· How do innovations/sicknesses/resources spread?
· How does ethnic/religious/political segregation emerge?
· How do structures of cooperation or conflict change over time?
· How strong is individuals drug consumption influenced by the peers?
Participants should have basic skills in R. For example, these can be acquired in the E-Portfolio on Ilias:
https://learning.zu.de/goto.php?target=crs_670&client_id=ilias
Educational objective:
Goal of the course is to provide a general introduction to different types of social networks. This includes their numerical description and statistical analysis. The key focus is on translating research questions to testable hypotheses, and respectively their statistical measures and analysis. The statistical software R will be used to do calculations and visualisations of social networks. Based on these results, course participants will learn how to derive meaningful conclusions about relevant questions in the field of social science.
The topics covered in the course include:
1) Introduction to the paradigm of social networks: What is relational data, and what does it have to do with social reality?
2) Basic numerical description of social networks: What measures tell what story?
3) Preparation and analysis of social network data in R.
4) Interpretation and visualisation of these results.
Further information about the exams:
Participants will have to write a short research proposal (1000-2000 words) including preliminary analyses. These can be based on current research projects or interests (MA, PhD theses).
Mandatory literature:
Day 1)
Wasserman, S., Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Chapters 2.2: P.30f and 2.3: P.35ff, Ch. 4: P.94-119, P.164-165, Ch. 5: P.169-172, Ch. 6: P.220-222, Ch 7: 249,
Jansen, D. (2006). Einführung in die Netzwerkanalyse: Grundlagen, Methoden, Forschungsbeispiele. S.37-49 sowie S.69-90.
Block, P., Hoffman, M., Raabe, I.J. et al. (2020). Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nat Human Behavior 4, 588–596.
Day 2)
Anderson, B.S., Butts, C.T., and Carley, K.M. (1999). The Interaction of Size and Density with Graph-Level Indices.Social Networks, 21(3), 239-267.
Krackhardt, D. (1988). Predicting With Networks: Nonparametric Multiple Regression Analyses of Dyadic Data.'Social Networks, 10, 359-382.
Steglich C., Knecht A. (2010) Die statistische Analyse dynamischer Netzwerke. In: Stegbauer C., Häußling R. (eds) Handbuch Netzwerkforschung. S.433-446
Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social networks,18(1),
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