PB130      One Unit
Statistics and Research Methods for Psychological and Behavioural Science

This information is for the 2025/26 session.

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This course equips students with the bedrock knowledge and skills for understanding and conducting research in psychological and behavioural science. It integrates core concepts from the processes of planning and conducting research with those involved in understanding and analysing data. Its lectures and classes introduce foundational research methods and statistics concepts to provide a basis for more advanced content in Year 2. In practical workshops students will be provided with datasets that they can use to put what they learn into practice using the R programming language, as well as having the opportunity to collect and analyse data of their own.

By the end of this course, you should:

• Understand how research design and statistics help us to learn about human behaviour.
• Have practical experience both collecting and analysing data, as well as planning and reporting research, on human behaviour.
• Understand the benefits of combining qualitative and quantitative data to answer research questions.
• Be comfortable using the R programming language to explore and analyse data.
• Be prepared for later courses in statistics and research methods for psychological and behavioural science.
• Be equipped to think clearly about data and to apply this thinking in your future work and life.  

This course is delivered through a combination of lectures, workshops, lab sessions and classes totalling a minimum of 84.5 hours across Autumn Term and Winter Term. There is a reading week in Week 6 of both Autumn Term and Winter Term.

Students will complete several pieces of formative work to cement learning and prepare for summative assessments:

• Several statistics worksheets.
• Practice writing up analyses of both qualitative and quantitative data.

• Bueno de Mesquita, E., & Fowler, A. (2021). Thinking clearly with data: A guide to quantitative reasoning and analysis. Princeton University Press.
• Bailey, D. H., Jung, A. J., Beltz, A. M., Eronen, M. I., Gische, C., Hamaker, E. L., ... & Murayama, K. (2024). Causal inference on human behaviour. Nature Human Behaviour, 8, 1448-1459.
• Shmueli, G. (2010). To explain or to predict? Statistical Science, 25, 289–310.
• Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners. London: Sage.
• Creswell, J.  W. (2009). Research design: Qualitative, quantitative, and mixed method approaches (3rd ed.). Thousand Oaks, CA:  Sage.
• Poldrack R. A. (2019). Statistical Thinking for the 21st Century.
• Navarro, D. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners (Version 0.6). Sydney: University of New South Wales.
• Wickham, H., & Grolemund, G. (2017). R for data science (Vol. 2). Sebastopol, CA: O'Reilly.
• British Psychological Society (2014). Code of Human Research Ethics. BPS.

Data analysis (40%)

Poster (30%)

Research project (30%, 3000 words)