The world is inherently "networked" and social network analysis provides a rigorous framework for understanding how the structure of relations, in addition to the attributes of individual actors, determine behaviour and outcomes. Via this course, students learn about the network perspective and how to apply it to answer important questions in various fields in social science. The course teaches students a range of social network analysis techniques, provides training in social network analysis software and students work on an independent research project.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Upon Successful completion of this course, students will be able to:
- Understand a broad range of network concepts and
theories.
- Appreciate how network analysis can contribute to
increasing knowledge about diverse aspects of society.
- Use a relational approach to answer questions of interest
to them (i.e. be able to apply 'network thinking').
- Analyse social network data using various software
packages.
- Present results from social network analysis, both orally and in writing.
Indicative Assessment
Problem set 1 (using NodeXL) (10%) (750 words equiv.) - LO 1, 2, 4
Problem set 2 (using R statistical software) (20%) (1500 words equiv.) - LO 1, 2, 4
Oral presentation of research project (10%) (20 minutes) - LO 1-5
Final paper for research project (60%) (4500 words) - LO 1-5
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
3 hours of contact time (mixture of lectures, seminars and computer labs) per week for 13 weeks. Students are expected to undertake a further 7 hours per week of independent study over the semester (total 130 hours).
Requisite and Incompatibility
Prescribed Texts
Notes will be provided.Assumed Knowledge
Some prior exposure to introductory social network analysis, via courses such as SOCR8005 Social Science of the Internet, SOCR8006 Online Research Methods or equivalent courses. Prior exposure to statistical programming e.g. R or Stata.
Specialisations
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 1
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2016 | $3054 |
- International fee paying students
| Year | Fee |
|---|---|
| 2016 | $4368 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.