• Offered by School of Sociology
  • ANU College ANU College of Arts and Social Sciences
  • Course subject Social Research
  • Areas of interest Political Sciences, Sociology, Criminology, Digital Humanities
  • Academic career PGRD
  • Course convener
    • Dr Robert Ackland
  • Mode of delivery Online or In Person
  • Co-taught Course
  • Offered in Second Semester 2023
    See Future Offerings

The Internet is increasingly a source of data for social science research and this course provides students with training in online research methods for social research, with an emphasis on quantitative methods. The course focuses on unobtrusive/non-reactive methods involving socially-generated digital trace data (networks and text) from sources such as websites, social networking sites such as Facebook, microblogs such as Twitter and discussion environments such as Reddit. In this computer-lab based course, students will gain an introduction to coding and will undertake social network analysis and quantitative text analysis using the R statistical software. There is also an overview of obtrusive/reactive social research methods, including both quantitative methods (online surveys, online experiments) and qualitative methods (online focus groups and interviews, online field research).

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. compare online research methods to methods traditionally used by social scientists;
  2. undertake basic coding using the R statistical software;
  3. collect digital trace data and conduct basic social network and text analysis;
  4. locate available tools and data for online research; and
  5. understand the advantages and disadvantages of various online research methods, and their ethical implications.

Indicative Assessment

  1. Basic social network analysis (20) [LO 1,2,3,4,5]
  2. Social media network data collection and analysis (30) [LO 1,2,3,4,5]
  3. Content analysis (20) [LO 1,2,3,4,5]
  4. Combining methods (30) [LO 1,2,3,4,5]

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.


130 hours of total student learning time made up from:

a) 35 hours of contact over 12 weeks: 24 hours of lectures, and 11 hours of tutorials; and

b) 95 hours of independent student research, reading and writing.

Inherent Requirements

Not Applicable

Requisite and Incompatibility

To enrol in this course you must have completed or be studying concurrently: -6 units of a 6000 or 8000 level Computer (COMP) or Statistics (STAT) course, OR -SOCR8001 Statistics for Social Scientists Students not satisfying the above condition may request permission to enrol from the convenor. You are not able to enrol in this course if you have previously completed SOCY2169 or SOCR4006.

Prescribed Texts

Not applicable

Preliminary Reading

Halfpenny, P. and Procter, R. (2015). Innovations in Digital Research Methods, London: SAGE Publications.

Ackland, R. (2013). Web Social Science: Concepts, Data and Tools for Social Scientists in the Digital Age, London: SAGE Publications.

Salganik, M. (2016). Bit By Bit: Social Research in the Digital Age, http://www.bitbybitbook.com.

Dicks, B. (2012): Digital Qualitative Research Methods (SAGE Benchmarks in Social Research Methods Series), London: SAGE Publications.

Gertzel, B., Graham, T. and R. Ackland (2015). vosonSML - an R package for collecting and constructing networks from social media data. https://cran.r-project.org/package=vosonSML

Assumed Knowledge

This course involves use of the R statistical software. While it is not assumed that students have prior experience in coding in R, students with no background in coding (in R or comparable languages) will need to undertake extra work in the first part of the course to acquire basic familiarity with coding.

In the event that physical computer labs are not available, students will need to install the open source (and cost-free) R and RStudio software on their own devices.


Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you 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). More information about your student contribution amount for each course at Fees

Student Contribution Band:
Unit value:
6 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

6.00 0.12500
Domestic fee paying students
Year Fee
2023 $3960
International fee paying students
Year Fee
2023 $5820
Note: Please note that fee information is for current year only.

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.

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
7322 24 Jul 2023 31 Jul 2023 31 Aug 2023 27 Oct 2023 In Person View

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