The course aims to:

provide post-graduate research students with a working knowledge of the assumptions, concepts, and theories underlying the most frequently used multivariate analysis techniques in quantitative social and behavioural sciences. These techniques include, but are not limited to, multiple regression, logistic regression, exploratory and confirmatory factor analysis, path analysis, structural equation modelling (SEM), and multilevel analysis. The selection of specific topics may be tailored to students’ research needs each semester. The focus will be on practical issues such as selecting the appropriate analysis, preparing data for analysis in the popular statistical packages (e.g., SPSS and AMOS) or popular programming languages (e.g., R or Python), interpreting output, and presenting results of a complex nature. 

The course addresses both the underlying mathematics and problems of applications. As such, a reasonable level of competence in both statistics and mathematics is needed.