
Course unit details:
Doing research with social network data and visualizations
Unit code | SOCY60292 |
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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 |
Available as a free choice unit? | Yes |
Overview
- Research design
- Data collection (whole networks)
- Data collection (ego networks)
- Archival data
- Visualization in iGraph and SNA, layout algorithms.
- Visualization in other packages Eg Visone/ Gephi Missing data
- Negative ties
- Large networks
- Network measurement theory
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Data Analysis with R & RStudio | CRIM70821 | Co-Requisite | Compulsory |
CRIM70821 is a Pre-Requisite for SOCY60292
Aims
The main aim of the course is to provide a solid understanding of various types of network data. Understanding the sources of such data, their properties and limitations. In addition to be able to gather and manipulate both on line and off line data for later analysis. The course provides a set of fundamental skills including data visualization that are required for the more dedicated units where the analysis of network data is the primary goal.
Learning outcomes
- Design and develop network studies and intervention that can be used in private and public sectors.
- Understand the variety of network data, ie. egonets, whole networks, multilevel networks, longitudinal networks, multimode networks.
- Assess the feasibility and applicability of a wide range of analytical techniques to social network data
- Collect social network data in online and offline contexts, selecting the right data collection tools and assessing the validity and reliability of the data collection.
- Produce cutting edge data visualization in a meaningful way.
Teaching and learning methods
Each week contains a one hour lecture followed by a 2 hour computer laboratory session.
Assessment methods
One computer-based assignment to manage and visualize different types of social network data (100%)
Study hours
Independent study hours | |
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Independent study | 120 |
Teaching staff
Staff member | Role |
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Martin Everett | Unit coordinator |