Structural Equation Modelling

Structural equation modelling (SEM) is a statistical technique that encompasses multiple linear regression, path analysis, factor analysis and causal modelling with latent variables in a unified framework. Health and social scientists as well as market researchers can use SEM to create powerful models that explain various aspects of human behaviours, from well-being to social attitudes and consumer behaviour. This course covers the main background principles of SEM, preparing data for SEM, model-building strategies, and applications of SEM in health and social sciences. The course covers different techniques that are part of the SEM "family": path analysis, confirmatory factor analysis, structural regression models, and cross-lagged structural equation modelling. SEM analyses will be demonstrated in two popular SEM programs: Mplus (a command driven interface) and Amos (with a graphical user interface).



Friday, July 19, 2019 - 09:00 to 17:00




Basic familiarity with multiple linear regression and factor analysis