Undergraduate Course: Advanced Social network Analysis using UCINet and R (SSPS10022)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Credits | 20 |
| Home subject area | School (School of Social and Political Studies) |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | While SNA techniques are mainly exploratory, hypothesis testing and estimation techniques with network data are possible, but require specific statistical tools. The course will have a practical focus and introduce students to a range of basic and more advanced SNA techniques. Starting with the five lab sessions of the PG course ¿Social Network Analysis: Mapping and exploring the network society¿, the course will continue with five new lab sessions to apply (descriptive and inferential) statistical tools to network data, that might be appropriate to the research questions students will consider in their dissertation work. Students will also learn how to manipulate and analyse large samples of networks. The course will be taught using UCINet software and the statnet packages for the R statistical computing environment. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | No |
Course Delivery Information
| Not being delivered |
Summary of Intended Learning Outcomes
At the end of this course, students are expected to be familiar with the following techniques:
¿ Exploratory SNA techniques: cohesion (e.g. density, reciprocity)
¿ Exploratory SNA techniques: subgroups and blockmodelling
¿ Exploratory SNA techniques: centrality
¿ 2-mode network analysis
¿ Permutation methods (e.g. QAP, bootstrap)
¿ Hypothesis testing at various levels of analysis (nodal, dyadic and network levels)
¿ Apply these techniques on large samples of networks
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Assessment Information
20% practical exercises during the lab sessions.
80% an essay including SNA analysis of a specific set of empirical data using UCINet to critically address a research question in a discipline of a student's choosing. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | |
Course secretary | |
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