MRes Criminology (Social Statistics) / Course details

If students do not have prior training in social network analysis, they are encouraged to take SOCY60361 (Social Network Analysis) in semester 1

This short course is taught early in semester 2. Computer labs are an integrated part of the course. The course will comprise four taught days, including interactive hands-on sessions, and two days entirely based on computer workshops.

A1. Critically engage with the theoretical foundations of SNA and use them to formulate robust and coherent SNA empirical questions.

A2. Design and develop network studies and intervention that can be used in private and public sectors.

A3. Understand the variety of network data, ie. egonets, whole networks, multilevel networks, longitudinal networks, multimode networks.

A4. Assess the feasibility and applicability of a wide range of analytical techniques to social network data

A5. Understand the motivation behind the statistical modelling of networks, and define and describe the most popular models in SNA

A6. Critically understand and evaluate SNA research, and reflect upon methodology in a theoretically informed way.

A7. Understand research questions in multidisciplinary contexts, and efficiently operationalise them.

B3. Critically discuss the most recent network literature applying complex statistical models, and identify the most appropriate statistical model for a given research problem.

B4. Analyse network structures using descriptive measures, and statistically model the mechanisms for social network formation and evolution, including ERGM models, longitudinal analysis, multivariate techniques, multimode and multilevel analysis, advanced statistical tools.

B6. Master advanced methods for data visualizations.

B7. Write social network analysis scientific articles and reports.

C2. Design and develop tailored research projects and interventions to a variety of real-world problems.

C4. Collect, manage, and analyse online and offline datasets, and efficiently approach large data analysis and management.

C5. Produce cutting edge data visualization with high impact and informativity.

C6. Be proficient in software that handle quantitative and network data (Ucinet, R)

D1. Develop new or enhanced skills to identify and use diverse social network data, and use such data to inform cutting edge research projects and interventions in a variety of contexts.

D2. Understand and mediate multidisciplinary environments and liaise across different intellectual and practical contexts

D3. Work collaboratively, both face-to-face and through the use of various online tools and spaces.

D4. Accurately and effectively work with numbers, and use advanced computational software.

Feedback available via Turnitin

Reading list

• Borgatti S., Everett M, Johnson J., 2018, Analysing Social Networks 2nd Ed, Sage, London
• Robert A. Hanneman and Mark Riddle, Introduction to Social Network Analysis. Available at https://faculty.ucr.edu/~hanneman/nettext/
• Scott, J. (2000) Social Network Analysis: A Handbook, London, Sage.
• Wasserman, S. and Faust, K. (1994) Social Network Analysis, Cambridge University Press.
• Lusher,D., Koskinen, J., and Robins, G. (2013). Exponential random graph models for social networks: Theory, methods and applications. Cambridge University Press.

For Information and advice on Link2Lists reading list software, see:

http://www.library.manchester.ac.uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/