PB4A7      Half Unit
Quantitative Applications for Behavioural Science

This information is for the 2025/26 session.

Course Convenor

Availability

The primary objective of this course is to familiarise students with the comprehensive statistical toolkit necessary to comprehend the multifaceted and individual-level causes of human behaviour and to equip them to conduct their own research. The course will cover leading methods used by psychologists and economists to test behavioural science hypotheses and examine relationships in data. Beginning with essential data cleaning and screening techniques for identifying and handling missing data, outliers, and ensuring data quality, students will master the core statistical foundations of the General Linear Model (GLM), which serves as the unifying framework for t-tests, Analysis of Variance (ANOVA), and regression analysis. Building on these foundations, the course covers sophisticated multivariate analyses, including factor analysis for identifying underlying constructs, structural equation modelling (SEM) for testing theoretical models and examining latent variables, and multilevel modelling for analysing hierarchical data structures common in psychological and economic research. This course complements 'Experimental Design and Methods for Behavioural Science' (PB413), which covers experimental design and research methods for MSc Behavioural Science students, providing a comprehensive methodological foundation across both experimental and observational research paradigms.

15 hours of seminars and 20 hours of lectures in the Autumn Term.

This course has a reading week in Week 6 of Autumn Term.

Students will complete bi-weekly statistical worksheets.

Textbooks

Field, A. (2012). Discovering statistics using R. London: Sage.

Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. London: Guilford Publications.

Keith, T. (2015). Multiple regression and beyond. New York: Routledge.

Navarro, D. (2013). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners: Version 0.5. Adelaide, Australia: University of Adelaide. Available here.

Phillips, N. D. (2017). Yarrr! The pirate’s guide to R. Available here.

Poldrack R. A. (2019). Statistical Thinking for the 21st Century. Available here.

Tabachnick, B., & Fidell, L. (2013). Using multivariate statistics. Boston: Pearson Education.

Urdan, T. C. (2011). Statistics in plain English. London: Routledge.

Indicative reading

Beaumont, R. (2018). An Introduction to Structural Equation Modelling (SEM). Chapter 65, pp. 1-7. Available here.

Flora, D. B., & Flake, J. K. (2017). The purpose and practice of exploratory and confirmatory factor analysis in psychological research: Decisions for scale development and validation. Canadian Journal of Behavioural Science, 49, 78.

Lindeløv, J. K. (2019). Common statistical tests are linear models (or: how to teach stats). Available here: https://lindeloev.github.io/tests-as-linear/

Loehlin, J. C. & Beaujean, A. A. (2017). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis (5th Ed.) Routledge: London. Chapter 1.

Nimon, K. F. (2012). Statistical assumptions of substantive analyses across the general linear model: a mini-review. Frontiers in psychology, 3, 322.

Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of school psychology, 48, 85-112.

Problem sets (30%)

Report (70%, 2500 words)