Module taken in Sem 2 AY19/20
Structural Equation Modeling
Description of module: This module will introduce the ideas of structural equation modelling and its relationship to other current statistical models. Specifically, regression analysis, path analysis, confirmatory factor analysis will be formulated within the general framework of structural equation modelling. Advanced topics, such as ordinal data analysis, missing data, multiple-group analysis and latent growth models, will also be covered. After the course, students are expected to know how to conduct the analysis and interpret the results themselves.
Final Assessment: 60%
Professor: Prof Mike Cheung
You learn a great deal about SEM and related techniques. If you’re interested in research statistics, you’ll probably want to take this module, especially with the direction that research statistics is currently headed towards. However, if you’re not interested in the research path, avoid at all costs. Actually, you have to apply for it through the department so don’t even bother with it.
Prof Cheung tends to explain things assuming that you already have some basis of understanding, so even for graduate level students it may be a bit difficult to catch exactly what he’s trying to get at. The seminars were in person for a while before corona struck and we had to move online.
Principles and Practice of Structural Equation Modeling
Difficulty in understanding(1-very easy, 5-very difficult): 5
Workload(1-very light, 5-very difficult): 5