Research Design for Social Data Science
Compulsory course for MSc Social Data Science, Hilary Term
Research design plan; essay
- Reading list: Research Design for Social Data Science reading list
This course introduces students to conceptual and methodological aspects of social science research methods, including both quantitative and qualitative methods. Research methods covered include interviewing, sampling, causality, experimental design, and measurement. The course emphasizes how different strategies (or designs) for collecting and analyzing data strengthen social science arguments and enhance their credibility.
- Understand the structure of scientific work, including the role of concepts, hypotheses, and theories
- Understanding how to integrate theory and methods
- Understand how qualitative data can sharpen understanding of causal mechanisms and arguments
- Understand the limits and strengths of experimental, quasi-experimental, and non-experimental designs.
- Understand why design is key to strengthen science arguments.
- Understand the issues involved in making causal arguments
- Introduction, foundations. How does science work?
- Doing social science
- Asking questions & interviewing
- Samples and surveys
- Causality, causal inferences & selectivity, I
- Causality, II
- Measurement, operationalization & big data