Course content

The course builds upon regression analysis, analysis of variance and principal component/factor analysis. In so doing, the course treats some advanced first-generation statistical analyses such as logistic regression, repeated measures and mixed design ANOVA that are commonly used in the field of psychology. Furthermore, the course provides a thorough introduction to a second-generation multivariate statistical analysis, namely the variance-based approach to structural equation modelling (i.e. PLS-SEM). In the treatment and presentation of these statistical analyses, the emphasis is put on a strong conceptual understanding as well as applications with the help of relevant statistical software.

Learning outcome

Knowledge:

- The candidate has acquired an advanced understanding of general linear modelling.

- The candidate has gained solid insight into traditional multivariate statistical analyses.

- The candidate has in-depth knowledge about partial least squares structural equation modelling.

Skills:

- The candidate masters choosing the most suitable multivariate analyses for diverse research inquiries.

- The candidate can independently apply these analytical methods in the context of their master’s thesis.

- The candidate can apply and interpret the resulting output with the help of statistical software.

General competence:

- The candidate possesses profound comprehension of the necessity for employing a multivariate approach in data analysis.

- The candidate demonstrates sound understanding of the advantages and need for employing appropriate techniques for analysing latent variables.

- The candidate can independently apply the statistical methods acquired during the course to real-life work scenarios.

Learning methods and activities

Teaching in Blocks: lectures and practical exercises.

Specific conditions

Credit reductions