THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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

Postgraduate Course: Social Shaping of Digital Research (PGSP11389)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis is a course aimed at students looking to combine traditional qualitative or quantitative research methods with analysis of novel digital data sources in empirical research, with a view to preparing them for a dissertation in Data Science Technology and Innovation. The course will be team-taught through a series of case studies, each of which explores a cutting-edge research project in depth. Through these, it will cover not only the core skills of critical research design, but will develop a deep understanding of methodological debates within sociological research around the use of novel data sources and the different epistemological frameworks which can drive research design and practice.
Course description This course will function as a dissertation preparation course for Data Science Technology and Innovation (DSTI) students looking to do research projects integrating traditional qualitative and quantitative research methods with novel digital data sources. The course will cover a range of different approaches to this kind of research, exploring both theory-methods frameworks and practical examples. Students will learn how to plan and carry out research incorporating multiple methods and novel data sources, and by the end of the course will be able to critically assess and discuss different approaches to research projects, including sampling, collection and analysis of data, ethical aspects and the distinct kinds of knowledge produced by different methods.

Each week will cover in detail a case study of a mixed-methods research project or framework for research in social data science. Through this set of case studies, the course will discuss and critically analyse a range of different approaches, issues, and considerations for planning and carrying out research involving multiple methods and using social data. At the end of this course, students will have developed a research plan for a dissertation in Data Science Technology and Innovation.

Outline content

The first week will act as an overview, setting out key principles. It will cover key aspects of social data science research and using novel digital data sources. It will discuss how to develop a research question and the important steps and skills involved in a mixed-methods research project using novel online data sources: how to use the academic literature to find an initial topic, how to complete a literature review and use this to narrow your research question, how to select and justify the different methods you use to answer this question (and their robustness), and how to structure a plan for a research project.

We will then have three case study weeks. These will include a week on combining text mining, sequence analysis and ethnographic approaches; a week looking at analysis of Wikipedia data; and a week covering combining forum and interview data. In these sessions, we will set out the case study in-depth, then work through the theoretical, methodological, and ethical aspects of the research design. We will discuss practical difficulties attending these different methods, and how to integrate these different sources of data and forms of analysis.

The final week will focus on the students┬┐ own projects and how to write a research design. The lecture component will summarise the key issues covered in the previous weeks and ground them in the practical work of writing a research plan. The tutorial will be based around small-group workshopping of the students research designs in conversation with one another, and will finish with short presentations of their proposed research and the key methodological and ethical issues it raises.

Through this overall structure, we will cover the following content:

- Key debates in using novel online data sources for academic research, including social data science and computational sociology
- Challenges with using social data and secondary data for social science
- Why use multiple methods - key opportunities and issues
- Exploring social science literature and identifying evidence gaps
- Developing a research question for social data science research
- Structuring a multiple-methods research project
- Main components of a research plan
- Choosing a site and scoping research
- Collection strategies ┬┐ alternatives and considerations
- Planning instrument development
- Building exploratory methods into social data research
- Combining ethnography with statistical and computational approaches
- Interviews and social data
- Qualitative coding and research in social data
- Quantifying robustness in incomplete and messy datasets
- The role of domain knowledge in planning, collection and analysis
- Understanding theory-informed methods
- Moving from micro-traces to meso and macro arguments
- What is a digital trace and how does it relate to social reality?
- Key ethical issues with social data
- Platform power and how design and policies shape the data we collect

Student Learning Experience

The course will be taught online-only. Each of the five weeks will include a lecture component and a tutorial component, with the tutorial comprising either a guest speaker discussing their research with the students, a discussion session or a group research workshop session depending on the week. Assessment will be coursework-based, through a single essay.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  0
Course Start Block 3 (Sem 2)
Course Start Date 16/01/2023
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Essay - 100%
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate a knowledge of the core principles of research design for projects using multiple methods and including novel digital data sources
  2. Apply core research design principles in carrying out empirical research, bringing multiple types of data and methods together in practice, and reflecting critically on the knowledge claims associated with each
  3. Design an empirical research project which is informed by relevant epistemology and methodological theory
  4. Develop a social data science research question of appropriate scope and depth
  5. Reflect critically on the empirical and ethical dimensions of using novel digital data sources and mixed-methods research, and how to effectively communicate findings
Reading List
Campagnolo, G.M. (2020), Social Data Science Xennials: Between Analogue and Digital Social Research. Palgrave Macmillan.
Collier, B., Clayton, R., Hutchings, A., Thomas, D. (2020), Cybercrime is (often) boring: maintaining the infrastructure of cybercrime economies, Workshop on the Economics of Information Security
Currie, M. (2012). The Feminist Critique: Mapping Controversy in Wikipedia. In D. M. Berry (Ed.), Understanding Digital Humanities (pp. 224┬┐248). London: Palgrave Macmillan UK. https://doi.org/10.1057/9780230371934_13
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserMr Ben Collier
Tel:
Email: Ben.Collier@ed.ac.uk
Course secretaryMs Maria Brichs
Tel: (0131 6)51 3205
Email: mbrichs@ed.ac.uk
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