Undergraduate Module Descriptor

SSIM912: 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 Skills	1. recognize and critically evaluate in writing the diversity of specialized techniques and approaches involved in analysing social network data in political science, sociology and criminology 2. demonstrate advanced proficiency in the use of statistical analysis to test a social networks hypothesis 3. show ability to present and summarize analysed data in an advanced, coherent, and effective manner 4. demonstrate advanced proficiency in acquired skills, confidence and competence in a computer package for statistical analysis (the SNA package in R)
Discipline-Specific Skills	5. understand and demonstrate advanced proficiency in the use of 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:
Introduction
Centrality
Measures of Network Structure
Social Capital
Block Models and Structural Equivalence
Basic Network Statistics
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 demonstrations
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/).

ELE – https://vle.exeter.ac.uk/

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 up to 500 words	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	1000 words on analysis in a problem set	3-10	Written
Final paper	70	3000 words including an analysis component	1-9	Written

	0
	0
	0
	0
	0

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	1000 words on analysis of a problem set	3-10	August/September reassessment period
Final paper	1 written assignment with data analysis component (3000 words)	1-10	August/September reassessment period

Re-assessment notes

Problem set is worth 30% and paper worth 70%

Indicative Reading List

This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.

Borgatti, Stephen P., Martin G. Everett, and Jeffrey C. Johnson. Analyzing social networks. SAGE Publications Limited, 2013.

Scott, John, and Peter J. Carrington. The SAGE handbook of social network analysis. SAGE publications, 2011.

Bonacich, P., and Lu, P. (2012). Introduction to Mathematical Sociology

Borgatti, S. P., Mehra, A., Brass, D. J., and Labianca, G. (2009). Network analysis in the social sciences. science, 323(5916), 892-895.

Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., and Tranmer, M. (2017). Social Network Analysis for Ego-Nets.