Intermediate Statistics and Research Methods for Psychological and Behavioural Science
This information is for the 2020/21 session.
Dr Thomas Curran CON.3.16
This course is compulsory on the BSc in Psychological and Behavioural Science. This course is not available as an outside option nor to General Course students.
Students must have completed Statistics and Research Methods for Psychological and Behavioural Science (PB130).
This course aims to provide students with integrated core knowledge and skills in contemporary research and analysis methods in psychological and behavioural science. Specific core methodological tools for preregistering and collecting data will be presented in lectures, selected to reflect parallel theoretical issues raised in PB200 Biological Psychology, PB201 Cognitive Psychology, PB202 Developmental Psychology, PB204 Social Psychology: Individuals, Groups and Culture, and PB205 Individual Differences and Why They Matter.
This course presents conceptual and practical knowledge on the range of tools available to the psychological/behavioural scientist. In particular, this course will examine current controversies and new developments in research methods in psychology and behavioural science. The overall goal of the course is to learn to think critically about how psychological and behavioural science is conducted, how conclusions are drawn, and how data are appropriately analysed considering intermediate issues such as measurement error and clustering. We will cover both methodological and statistical issues that affect the validity of research in psychology, with an emphasis on psychological and behavioural sciences. We will also discuss the recent controversy in psychology about the replicability of scientific results and preregistration of both quantitative and qualitative research. The course also instructs students in the use of quantitative data collection methods, including surveys, experiments, assessment tools, and computerised tasks. It also covers principles and issues involved in the analysis of quantitative data, including the importance of transparency in data analysis and reporting.
Where statistics are concerned, this course presents students with knowledge of, and practical exposure to, statistical modelling. It covers linear and non-linear models, factor analysis, structural equation modelling, multilevel modelling, and intermediate issues in data cleaning and imputation. These topics build directly on from the introduction to the linear model students received in PB130. Throughout the course, an understanding of key concepts such as statistical power and effect sizes will be emphasised in line with current controversies regarding replicability and questionable research practice. Practical sessions will equip students with knowledge of how to conduct the taught statistical techniques using the R programming language.
This course is delivered through a combination of lectures, workshops, lab sessions and classes totalling a minimum of 62 hours across Michaelmas Term and Lent Term. There is a reading week in Week 6 of Michaelmas Term and Week 6 of Lent Term.
In response to the current situation, some or all of this teaching will be delivered through a combination of live online classes, Q+A sessions, online lab sessions and pre-recorded short online videos. You will receive the same amount of teaching whether you are on campus or online.
Students will complete a number of pieces of formative work to cement learning and prepare for the summative assessments:
- Practice Data Analysis Plan (MT)
- 3 statistics worksheets
- American Psychological Association (2020). Publication manual of the American Psychological Association. (7th ed.)
- Camerer, C. F., Dreber, A., Holzmeister, F., Ho, T. H., Huber, J., Johannesson, M., ... & Altmejd, A. (2018). Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nature Human Behaviour, 2(9), 637-644.
- Chambers, C. (2017). The 7 deadly sins of psychology: A manifesto for reforming the culture of scientific practice. Princeton, NJ: Princeton University Press.
- Keith, T. (2015). Multiple regression and beyond. New York: Routledge.
- L. Haven, T., & Van Grootel, D. L. (2019). Preregistering qualitative research. Accountability in Research, 26(3), 229-244.
- Munafo, M. R., Nosek, B. A., Bishop, D. V., Button, K. S., Chambers, C. D., Du Sert, N. P., ... & Ioannidis, J. P. (2017). A manifesto for reproducible science. Nature human behaviour, 1(1), 1-9.
- Nosek, B. A., & Lakens, D. (2014). Registered reports: A method to increase the credibility of published results. Social Psychology, 45(3), 137-141.
- Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251).
- Tabachnick, B., & Fidell, L. (2013). Using multivariate statistics. Boston: Pearson Education.
Report (50%) in the MT.
Other (50%) in the LT.
Report (50%) in MT – You will develop a pre-registered report assignment of around 3500 words
Other (50%) in LT – You will undertake a secondary data analysis. The write up will be 3500.
Important information in response to COVID-19
Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.
Department: Psychological and Behavioural Science
Total students 2019/20: Unavailable
Average class size 2019/20: Unavailable
Capped 2019/20: No
Value: One Unit
Personal development skills
- Problem solving
- Application of information skills
- Application of numeracy skills
- Specialist skills