Structural Equation Modelling
Presenters: Dr. Mick Green and Karen Baird
This course will introduce participants to latent variables (variables which are not directly measured themselves) and to the use of factor analysis in investigating relationships between latent variables and observed, or measured, variables. These techniques will then be extended into the wider area of structural equation modelling, where complex models involving several latent variables will be introduced.
The course is aimed at researchers and research students who have experience of statistical modelling (up to linear regression) and hypothesis testing, who wish to develop techniques to analyse more complex data involving latent variables. The aim of the course is to provide a background of theory with opportunities to apply the techniques in practice, and each session will consist of a lecture/ demonstration and a practical. The software packages used will be SPSS and AMOS, and while participants will be expected to be familiar with SPSS, no knowledge of the structural equation modelling package AMOS will be assumed.
Cost
- Lancaster University staff and postgraduates - £50
- External staff member from an academic institution on a NODE course - £120
- External Postgraduate research student from an academic institution on a NODE course - £60
- External participant from a non academic institution on a NODE course - £440
The course fees include all supporting documentation, refreshments and lunches.
In summary, the following topics will be covered:
- introduction to latent variables and measurement error
- exploratory and confirmatory factor analysis
- measurement models
- structural equation modelling
Learning
Students will learn through the application of concepts and techniques covered in the course to real data sets. Students will be encouraged to examine issues of substantive interest in these studies.
Successful students will be:
- familiar with latent variables and factor models
- able to investigate data using factor analysis
- able to confirm hypotheses and develop structural equation models
- able to apply theoretical concepts
- able to identify and solve problems
- able to analyse data using appropriate techniques and interpret statistical output
