Dr Matteo M Galizzi CON 4.06

Dr Alina Velias

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

The course aims to introduce students in an intuitive and accessible way to the main methodological concepts and tools in behavioural science. To achieve this objective, the self-contained 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 experiments, lab-field experiments; internal validity, external validity, generalizability, and reproducibility of 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, principles of experimental design; Tests of hypothesis: principles and practices, parametric and non-parametric tests in practice, intuitions and rules of thumbs; Introduction to econometrics: simple and multiple linear regression models, econometric analysis of experimental data; 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, principles of survey design. The seminars involve hands-on practical applications using Stata, R, and online resources.

The course is delivered through a combination of lectures and seminars totalling a minimum of 20 hours in the second teaching session (January).

Students will be expected to produce 1 piece of coursework after the teaching session.

• 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%).

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