Module SSI3001 for 2021/2
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
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.
| On successfully completing the programme you will be able to: | |
|---|---|
| Module-Specific Skills | 1. 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 Skills | 5. 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 Skills | 9. demonstrate an ability to study independently; 10. use IT and, in particular, statistical software packages - for the retrieval, analysis and presentation of information. |
Module Content
Syllabus Plan
Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics:
- Topic 1: Introduction
- Topic 2: Centrality
- Topic 3: Measures of Network Structure
- Topic 4: Social Capital
- Topic 5: Block Models and Structural Equivalence
- Topic 6: Basic Network Statistics
- Topic 7: Brief overview of advanced techniques
Learning and Teaching
This table provides an overview of how your hours of study for this module are allocated:
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
...and this table provides a more detailed breakdown of the hours allocated to various study activities:
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activities | 22 | 11 x 2 hour sessions of lectures and demonstration |
| Guided independent study | 128 | Time spent individually undertaking data analysis for exercises, final assignment |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/)
How this Module is Assessed
In the tables below, you will see reference to 'ILO's. An ILO is an Intended Learning Outcome - see Aims and Learning Outcomes for details of the ILOs for this module.
Formative Assessment
A formative assessment is designed to give you feedback on your understanding of the module content but it will not count towards your mark for the module.
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Plan for final essay | Students can submit an abstract or outline for final assignment | 1-9 | Written |
Summative Assessment
A summative assessment counts towards your mark for the module. The table below tells you what percentage of your mark will come from which type of assessment.
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
...and this table provides further details on the summative assessments for this module.
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Problem Sets | 30 | 1,000 words on analysis of a problem set | 3-10 | Written |
| Final paper | 70 | 3,000 words | 1-9 | Written |
Re-assessment
Re-assessment takes place when the summative assessment has not been completed by the original deadline, and the student has been allowed to refer or defer it to a later date (this only happens following certain criteria and is always subject to exam board approval). For obvious reasons, re-assessments cannot be the same as the original assessment and so these alternatives are set. In cases where the form of assessment is the same, the content will nevertheless be different.
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Problem Sets | 1,000 words on analysis of a problem set (30%) | 3-10 | August/September reassessment period |
| Final paper | 1 written assignment with data analysis component (3,000 words) (70%) | 1-10 | August/September reassessment period |
