PB413E      Half Unit
Frontiers in Behavioural Science Methods

This information is for the 2019/20 session.

Teacher responsible

Dr Matteo Galizzi

Dr Dario Krpan

Availability

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

Course content

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: measuring preferences, attitudes, beliefs, willingness-to-pay; determining evidential value of behavioural science research; 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; understanding the mechanisms behind behavioural effects by employing experimental-causal-chain, measurement-of-mediation, and moderation-of-process designs.

Teaching

9 hours of lectures, 3 hours and 45 minutes of lectures, 3 hours of seminars and 4 hours and 30 minutes of seminars in the ST.

Day 1

Lecture 1 (1h 30’): Intro. Further Considerations in Experimental Design. Measuring Preferences and Attitudes: the State-of-the-art [MMG, DK]

Lecture 2 (1h 30’): Determining Evidential Value of Behavioural Science Research: Undisclosed Flexibility in Data Collection, Dance of P-values, P-curve analysis, Pre-Registration, Pre-Analysis Plan [MMG, DK]

Seminar 1 (1h 30’): Best Experimental Practices in Practice. Sample Size Calculations in Stata and G*Power [MMG, DK].



Day 2

Lecture 3 (1h 30’): Behavioural Priming Techniques [DK]

Lecture 4 (1h 15’): System 1 In Action: Capturing Implicit Cognition: [DK]

Seminar 2 (2h): Building a Simple Task to Measure Implicit Cognition [DK]



Day 3

Lecture 5 (1h 30’): Strategic Decision-Making: Introduction to Behavioural Game Theory [MMG]

Lecture 6 (1h 15’): Behavioural Game Theory: Applications. Introduction to Non-Linear Regression Models [MMG].

Seminar 3 (2h): Building Simple Games of Strategic Interaction Using ZTree. Non-linear regression models using Stata [MMG].



Day 4

Lectures 7 (1h 30’) & 8 (1h 15’): Mechanisms, Moderators, Mediators; Mediation and Moderation in a Regression Framework; Understanding the Mechanisms Behind Behaviour Change: Experimental-causal Chain, Moderation-of-process, and Measurement-of-Mediation Designs [DK].

Seminar 4 (2h): Moderation and Mediation Using Stata [DK].



Day 5

Lecture 9 (1h 30’): Beyond Economics and Psychology: State-of-the-art Physiological Research Techniques for the Behavioural Science. Introduction to Systematic Reviews of the Literature and Meta-analyses. Wrap up. [MMG, DK]

Seminar 5 (1h 30’): Building a Simple Behavioural Science Experiment Using Qualtrics Survey Software [MMG, DK]

Formative coursework

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

For the formative assignment, you will need to produce a brief research proposal plan (500 words) that will serve as the basis for the full research proposal (3000 words) that will constitute your summative assignment. In the research proposal plan, you will propose a design and implementation of a behavioural science experiment entailing the use of (at least) two different software packages introduced in the seminars. The structure of the proposal plan should be as follows: a) Introduce a viable research question (on a topic of your choice) that will guide your experimentation; b) Describe how you would design and implement behavioural science research to answer the question; and c) Explain which statistical approaches covered in lectures and seminars you would use to analyse the data. Feedback received for the formative assignment will help you to prepare the summative assignment by identifying both strengths and weaknesses of your approach.

 

Indicative reading

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.

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. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.

Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts, applications, and implementation. Guilford Publication.

Harrison, G.W., List, J.A. (2004). Field experiments. Journal of Economic Literature, XLII, 1009-1055.

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.

van't Veer, A. E., & Giner-Sorolla, R. (2016). Pre-registration in social psychology—A discussion and suggested template. Journal of Experimental Social Psychology, 67, 2-12.

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological science, 22(11), 1359-1366.

Assessment

Proposal (100%) in the ST.

For the summative assignment, you will be required to expand the research proposal plan submitted as part of the formative assignment into a full research proposal comprising 3000 words. Your task will be to propose a design and implementation of a behavioural science experiment entailing the use of (at least) two different software packages introduced in the seminars. The structure of the proposal should be as follows: a) Introduce a viable research question (on a topic of your choice) that will guide your experimentation; b) Describe how you would design and implement behavioural science research to answer the question; and c) Explain which statistical approaches covered in lectures and seminars you would use to analyse the data. In the proposal, we will expect you to use in-text scholarly citations and provide a reference list at the end. The summative assignment should allow you to not only demonstrate your knowledge regarding the present course, but also to gain deeper insights into experimental analysis and design that should help you in producing a high-quality dissertation.

 

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