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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 t