PB471E      Half Unit
Research Methods for Behavioural Science

This information is for the 2019/20 session.

Teacher responsible

Dr Matteo Galizzi QUE.3.16


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

Course content

The course aims to introduce students to the main methodological concepts and tools in behavioural science. To achieve this objective, the course combines rigorous conceptual discussion with hands-on practical applications. The course covers: The beauty of experiments: how randomization solves the sample selection bias; randomized controlled experiments from the lab to the field: taxonomy, principles, best practices; online and lab-field experiments, Statistical tools: distributions and their moments, the inference problem; Experimental design: between-subjects design, block/stratified randomization, matched-pair design, within-subjects design, cluster randomization, the mechanics of randomization; Introduction to econometrics: simple and multiple linear regression models, econometric analysis of experimental data; Tests of hypothesis: principles and practices, parametric and non-parametric tests in practice; Sampling: optimal sample size calculation in practice, useful rules of thumbs; Experimental best practices and challenges: ethics, recruitment, informed consent form, attrition, non-compliance, external validity, behavioural data-linking; When randomization is not possible: before and after, matching, natural experiments, difference-in-difference, regression discontinuity design; Outcomes and behavioural measures in experiments, survey design. The seminars involve hands-on practical applications using Stata, R, and Qualtrics. 


17 hours and 30 minutes of lectures and 5 hours of seminars in the LT.

Formative coursework

Students will be expected to produce 1 piece of coursework in the LT.

Indicative reading

  • Angrist, J.D., Pischke J-S. (2015). Mastering ‘Metrics: the Path from Cause to Effect. Princeton: Princeton University Press.
  • Gerber, A.S., Green, D.P. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: Norton & Company.
  • Glennerster, R., Takavarasha, K. (2013). Running Randomized Evaluations: a Practical Guide. Princeton: Princeton University Press.
  • Kohler, U., Kreuter, F. (2012). Data Analysis Using Stata. College Station, TX: Stata Press.
  • Mitchell, M.N. (2015). Stata for the Behavioural Sciences. College Station, TX: Stata Press.
  • Burtless, G. (1995). The case for randomized field trials in economic and policy research. Journal of Economic Perspectives, 9(2), 63-84.
  • Dolan, P., Galizzi, M.M. (2014). Getting policy-makers to listen to field experiments. Oxford Review of Economic Policy, 30(4), 725-752.
  • Dolan, P., Galizzi, M.M. (2015). Like ripples on a pond: behavioural spillovers and their consequences for research and policy. Journal of Economic Psychology, 47, 1-16.
  • Harrison, G.W., List, J.A. (2004). Field experiments. Journal of Economic Literature, XLII, 1009-1055.
  • List, J.A. (2006). Field experiments: a bridge between the lab and naturally occurring data. Advances in Economic Analysis and Policy, 6, 8.


Portfolio (100%) in the LT.

Students will be asked to submit a “portfolio” of hands-on practical tasks related to the main stages of a behavioural science project.

Key facts

Department: Psychological and Behavioural Science

Total students 2018/19: Unavailable

Average class size 2018/19: Unavailable

Controlled access 2018/19: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Leadership
  • Self-management
  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Commercial awareness
  • Specialist skills