9am - 5:45pm
Monday 4 May - Wednesday 6 May 2020

Social Network Analysis

This course introduces you to various concepts, methods, and applications of social network analysis drawn from the social sciences. We will start with an introduction to graph theory and the fundamentals of social network analysis, including data collection and visualisation. We will then consider descriptive network- and individual-level statistics and their applications in social science research. Finally, we will discuss methods for testing hypotheses about social network structure, and introduce models for social networks. The emphasis will be on applying social network analysis theories and methods to real-world data, and on understanding and interpreting results, rather than on the underlying mathematics. The course will also provide training to use software to investigate social networks.

from £395.00 to £595.00

University of Surrey
Guildford
Surrey
GU2 7XH

COURSE CONTENTS

Indicative content includes:

  • Historical and theorietical foundations
  • Data sources and data collection strategies
  • Graphs, matrices, and sociograms
  • Centrality and centralisation
  • Balance, reciprocity, and transitivity
  • Density and cohesive subgroups
  • Equivalence and blockmodels
  • Dyads and triads
  • Statistical models for social networks

LEARNING OUTCOMES

On successful completion of this course, you will be able to:

  • Describe social network analysis concepts, data collection strategies, and analytic techniques (C,K)
  • Have a critical understanding of the key network data collection strategies and their potential limitations (C,K)
  • Use social network analysis statistics and models to describe social networks and test hypotheses, and interpret the results (C,P)
  • Be able to implement a social network analysis on real world data and critically evaluate the results (C/P)
  • Engage with different applications of social network analysis in the social sciences (C,T)
  • Use social network analysis software to analyse network data (T,P)
  • Key: C-Cognitive/Analytical; K-Subject Knowledge; T-Transferable Skills; P- Professional/ Practical skills

COURSE LEADER

Dr Berlusconi is interested in fusing criminology scholarship with quantitative methodologies and, in particular, social network analysis. Giulia's research focuses primarily on co-offending and illicit markets. For full details of her research interests and experience, see: https://www.surrey.ac.uk/people/giulia-berlusconi.

LEVEL

No prior knowledge is required. Familiarity with basic mathematical notation and standard statistical methods is an advantage but not essential. All computing workshops will be in R (https://www.r-project.org), using the GUI RStudio (https://www.rstudio.com). Familiarity with R is therefore an advantage. For a basic introduction to R for data manipulation and analysis, the following interactive workshops are recommended: https://swirlstats.com. For additional resources, see: https://www.r-bloggers.com/getting-started-with-r/. You may also find the following resource useful https://tinyurl.com/IntroRSurreySC

PREPARATORY READING

Borgatti, S.P., Mehra, A., Brass, D.J. & Labianca, G. (2009). “Network Analysis in the Social Sciences.” Science 323: 892-895.

Participants on the course will include some students completing the MSc in Social Research Methods.

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