Dr Ganga Shreedhar

This course is compulsory on the MSc in Behavioural Science. This course is not available as an outside option.

Quantitative data collection is an integral component of behavioural science: Testing hypotheses requires designing experiments and analysing the data or conducting statistical analyses on secondary data. Whereas another core course on this programme - Experimental Design and Methods for the Behavioural Science - covers best practices in designing and conducting experimental research, Quantitative applications for Behavioural Science introduces the main statistical background of behavioural research from psychology and economics. The course will cover best practices and state of the art statistical tools that are used by psychologists and economists. All the analyses will be demonstrated on example behavioural science research, and students will learn how to identify, interpret, and evaluate appropriate analyses for different research designs, conduct their own data analysis for each of these designs as well as report the analysis for publication in a journal, and recognise and understand contemporary issues in data science analysis in psychology and economics that need to be considered for best research practices. Emphasis will be on teaching students how the same analyses are presented in psychology and economics journals so students can understand how to integrate research from these two fields that constitute behavioural science.

10 hours of lectures and 10 hours of seminars in the MT.

The course is delivered in Michaelmas Term over 10 lectures of 1 hour (1 per week, over weeks 1-5, and 7-11) and 10 weekly seminar sessions of 1 hour (1 per week, over weeks 1-5, and 7-11). Students on this course will have a reading week in Week 6, in line with departmental policy.

The department will offer an optional 3 hour session in which the course instructor will overview fundamental quantitative concepts that will help students prepare for the course.

Students will be expected to produce 1 exercise in the MT.

Formative coursework will involve a critical appraisal of a published research paper.  Students will receive more specific information about the formative assignment at the start of the course. 

Cumming, G. (2013). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. London: Routledge.

Wagenmakers, E. J. (2007). A practical solution to the pervasive problems of p values. Psychonomic Bulletin & Review, 14(5), 779-804.

Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of econometrics, 142(2), 615-635.

Baum, C. F., Schaffer, M. E., & Stillman, S. (2003). Instrumental variables and GMM: Estimation and testing. Stata Journal, 3(1), 1-31.

Dimick, J. B., & Ryan, A. M. (2014). Methods for evaluating changes in health care policy: the difference-in-differences approach. Jama, 312(22), 2401-2402.

Wagenmakers, E. J., Wetzels, R., Borsboom, D., van der Maas, H. L., & Kievit, R. A. (2012). An agenda for purely confirmatory research. Perspectives on Psychological Science, 7(6), 632-638.

Report (100%) in the LT.

Summative assessment will involve a data analysis report.  Students will receive more specific information about the summative assignment at the start of the course.