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Undergraduate Module Descriptor

SSI3001: Introduction to Social Network Analysis

This module descriptor refers to the 2021/2 academic year.

Module Aims

You will learn about the theories of social networks and how these ideas impact our understanding of other social science topics like political engagement, social capital, and deviance. We also discuss motivations for using social network analysis and the strengths and weaknesses of this approach in a variety of social science contexts. Using a combination of lectures, practical demonstrations and assignments, this module aims at developing your skills in the analysis and presentation of relational data. Specifically, you will learn multiple ways of formulating social network hypotheses and testing them using a combination of descriptive measures and inferential statistics. The course is taught using the programming language R. This course is only suitable for students who are either comfortable programming in R or currently learning R.

Intended Learning Outcomes (ILOs)

This module's assessment will evaluate your achievement of the ILOs listed here – you will see reference to these ILO numbers in the details of the assessment for this module.

On successfully completing the programme you will be able to:
Module-Specific Skills1. recognize and evaluate in writing the diversity of specialized techniques and approaches involved in analysing social network data in political science, sociology and criminology;
2. use statistical analysis to test a social networks hypothesis;
3. show ability to present and summarize analysed data in a coherent and effective manner;
4. demonstrate acquired skills, confidence and competence in a computer package for statistical analysis (the SNA package in R).
Discipline-Specific Skills5. understand and use the tools and techniques of social network analysis for political and social data;
6. use social network evidence to empirically evaluate the (relative) validity of political, sociological and criminological theories and hypothesis;
7. construct well thought out and rigorous data analysis, tables and reports for both written and oral presentation;
8. examine relationships between theoretical concepts with real world empirical data.
Personal and Key Skills9. demonstrate an ability to study independently;
10. use IT – and, in particular, statistical software packages - for the retrieval, analysis and presentation of information.