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DRPS : Course Catalogue : School of Social and Political Science : School (School of Social and Political Studies)

Undergraduate Course: Advanced Social network Analysis using UCINet and R (SSPS10022)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryWhile 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.
Course description Not entered
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Not being delivered
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

Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
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