This course provides an overview of basic statistical modelling for the analysis of psychological data. Drawing conclusions from data requires the application of scientific models. The latter will teach students how theory impacts on analytical goals, which in turn influence the design and analysis of a study, and ultimately, what kind of model to apply. The course will be taught by various lecturers using examples from their own research activities.

Weekly one-hour lectures

None

Formal written exam 100%

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

To provide an understanding of basic statistical modelling approaches to the analysis of psychological data. To provide an understanding of the relation between scientific theory and analytical models, analytical goals and decision-making in scientific research and to be able to reflect on modelling criteria in relation to analytical goals and theoretical considerations.

By the end of this course students will be able to:

■ Integrate knowledge about study design and statistics to formulate and estimate the General Linear Model (GLM) appropriate to the various types of study designs encountered in psychology, especially studies with repeated observations.

■ Visualise and interpret various effects (including interactions) in multi-way designs.

■ Estimate linear mixed-effects models and describe their relation to traditional techniques such as ANOVA and multiple regression.

■ Perform logistic regression and explain and interpret the statistical output.

■ Create reproducible data analysis scripts and reports within the R statistical programming environment.

■ Understand the relationship between theoretically motivated research aims and data analysis

■ Distinguish between different analytical goals in data modelling (e.g. confirmatory vs. exploratory).

■ Understand how analytical goals affect decision-making (and related criteria) in an analysis

■ Reflect on potential trade-offs in the analytical decision-making process (e.g. internal validity vs. generalisability, parsimony vs. theoretical soundness, accuracy vs. coverage).

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.