Dr Matteo Galizzi and Dr Dario Krpan

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

Behavioural science is the scientific study of human behaviour, and it combines research techniques from psychology and economics. The course offers an integrated training in advanced behavioural science methods by introducing students to state-of-the-art techniques that stretch across the spectrum of both disciplines.

The course covers the following topics: randomised controlled experiments in behavioural science, causality, selection bias; online, lab, and field experiments in behavioural science; principles of experimental design; transparency and reproducibility of behavioural science research, pre-registration, pre-analysis plan; best practices in modern behavioural science experiments; tests of hypotheses and sample size calculations for experiments in theory and practice; determining evidential value of behavioural science research, p-curve analysis; measuring attitudes and preferences; behavioural game theory and experimental games of strategic interaction; designing behavioural priming experiments and measures that tap into implicit cognition; state-of-the-art physiological research techniques; regression analysis of experimental data in theory and practice; understanding the mechanisms behind behavioural effects by employing experimental-causal-chain, measurement-of-mediation, and moderation-of-process designs.

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

Students will be expected to produce 1 presentation in the WT.

For the formative assignment, students will work in small groups (3-4 students) to produce a presentation in which they will need to propose a design and implementation of a behavioural science experiment entailing the use of (at least) two different software packages introduced in the course.

• Angrist, J.D., Pischke J-S. (2015). Mastering ‘Metrics: the Path from Cause to Effect. Princeton: Princeton University Press.
• Camerer, C.F. (2003). Behavioral Game Theory: Experiments in Strategic Interaction. Princeton: Princeton University Press.
• Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. New York, NY: The Guilford Press.
• Dijksterhuis, A., Chartrand, T. L., & Aarts, H. (2007). Effects of Priming and Perception on Social Behavior and Goal Pursuit. In J. A. Bargh, J. A. Bargh (Eds.), Social psychology and the unconscious: The automaticity of higher mental processes (pp. 51-131). New York, NY, US: Psychology Press.)
• Förster, J., & Liberman, N. (2007). Knowledge activation. Social psychology: Handbook of basic principles, 2, 201-231.
• Gawronski, B., & De Houwer, J. (2014). Implicit measures in social and personality psychology. Handbook of research methods in social and personality psychology, 2, 283-310.
• Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: The Guilford Press.
• Harrison, G.W., List, J.A. (2004). Field experiments. Journal of Economic Literature, XLII, 1009-1055.
• Munafo’ M. R., Nosek, B.A., Bishop, D.V.M., Button, K.S., Chambers, C.D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E-J., Ware, J.J. & Ioannidis, J.P.A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1, 0021.
• Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). P-curve: a key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534-547.
• Spencer, S. J., Zanna, M. P., & Fong, G. T. (2005). Establishing a causal chain: why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89, 845-851.

Report (100%) in the WT.

Students will be expected to write a 3,000 word report. The reports will need to be submitted individually and will require students to elaborate on the group-work undertaken as part of the formative assignment.