MSc Data Science (Social Analytics) / Course details

Year of entry: 2021

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Course unit details:
Social Network Analysis

Unit code SOST71032
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Social Statistics
Available as a free choice unit? Yes

Aims

Aims

  • To introduce the concepts of social networks and the various kinds of relation that can occur between members of the network.
  • To explain how do describe social networks, including visualisation using different software.
  • To show how statistical models can be used for social network analysis. To demonstrate the use of software for modelling social networks – in particular the use of R.

 

Learning outcomes

Objectives: On completion of this unit successful students will be able to:

  • Understand the concept of a social network, and the various kinds of relations that can occur with it.
  • Know how to describe and visualise the network using appropriate software and summary measures. 
  • Be familiar with how to model a social network using appropriate software, and understand the substantive reasons for doing so.
  • Be able to relate social network dependencies, and understand the substantive reasons for modeling these, to complex design more broadly.
  • To critically assess the use of social network analysis in the social sciences.
  • Use Visone and R for social network analysis, and organise the network data for use with each of these software packages.
  • Participate in a discussion about the strengths and weaknesses of a given piece of research that involves social network analysis.
  • Understand the main arguments in methodological journal articles on social network analysis.

 

Teaching and learning methods

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.

 

Knowledge and understanding

 

 

Assessment methods

One report equivalent to a 3,000 word essay, and comprising two parts: part one (1500 words) on concepts, description and visualisation of social networks and part two (1500) on statistical models for social networks.

 

Recommended reading

 

Reading list

  • Borgatti, S.P., Everett, M.G., Johnson, J.C. (2013). Analyzing Social Networks. Sage.
  • Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social Network Analysis for Ego-Nets. SAGE.
  • Hanneman, R.A. and Riddle.  M. (2005) Introduction to social network methods.  Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/
  • Scott, J (2000) Social Network Analysis: A handbook. Sage
  • Lusher D, Koskinen J, Robins G [editors] (2013). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications (Structural Analysis in the Social Sciences). NY: Cambridge University Press.
  • Robins, G. (2015). Doing Social Networks Research: Network Research Design for Social Scientists. Sage
  • Snijders, T. A. (2011). Statistical models for social networks. Annual Review of Sociology 37, 131–153.

Note: for preliminary reading we recommend only the introductory and discussion chapters or sections of these books and papers; we do not expect you to be familiar with all the technical details prior to the course.

See also Statnet in R (for fitting ERGMS): http://statnet.csde.washington.edu

 

Study hours

Scheduled activity hours
Seminars 33
Independent study hours
Independent study 117

Teaching staff

Staff member Role
Termeh Shafie Unit coordinator
Nick Crossley Unit coordinator

Additional notes

Timetable

Semester 2, six-day course from 10.00am - 4.00pm.

 

 

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