Bayesian Statistics (975C8)

15 credits, Level 7 (Masters)

Spring teaching

Structural equation modelling (SEM) is a general method of data analysis that brings together path analysis and factor analysis. In path analysis, the aim is to specify and test models of causal relationships among variables, and to estimate direct and indirect effects. SEM extends traditional path analysis by estimating models simultaneously and by providing overall measures of model fit. In factor analysis, the goal is to identify unobserved, latent variables that account for the relationships between observed variables. Traditionally, this has been data driven - that is, the factors emerge from the analysis - and known as exploratory factor analysis. In SEM, the emphasis is on confirmatory factor analysis where you propose a factor model and test to see whether it fits the data. Finally, SEM allows you to combine path analysis and confirmatory factor analysis by testing models of causal relationships among hypothesised factors.

The module will provide a thorough introduction to SEM, and will also deal with some important, related issues. These include mediation analysis, and moderation, and methods for handling missing data. The emphasis will be on analyzing continuous variables with approximately normal distributions, but we will also cover how to handle nonnormal data. Most of the analyses will be carried out with a SEM software package and a further aim of the module is to enable you to use this program.

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 22 hours of contact time and about 128 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2023/24. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum. We’ll make sure to let you know of any material changes to modules at the earliest opportunity.