杏吧论坛

Dr Matteo Galizzi CON 4.06

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 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 online resouces.

The course is delivered through a combination of lectures and seminars totalling a minimum of 20 hours in the LT.

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

• 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.