Note: this course will not explicitly cover linear regression nor generalized linear models. These methods are covered in several other courses of the doctorate school program.

Exercise classes:

During the first exercise class an introduction to working with the open-source software program R is given. In all subsequent exercise classes, the participants are asked to work on exercises implementing the theoretical concepts seen during the lecture with the help of the instructor.

Learning objectives:

The emphasis of this course is on the application and implementation of the methods to analyze data in a multivariate context. While mathematical and statistical theory is employed where necessary, the focus of the course will be on the adequate usage of these techniques. This covers checking the methods’ assumptions towards the data, the estimation of models and assessment of the goodness of fit, as well as the interpretation and validation of the results.

At the end of the course, the participant is able to:

– identify the correct method to address a particular research question

– understand and independently implement the different steps of applying a multivariate data analysis

Assessment:

• A case study conducting a multivariate data analysis in the participant’s academic field of interest (70%)
• Oral presentation of the case study before submission (30%)

References:

• Everitt B., Hothorn T. (2011) An Introduction to Applied Multivariate Analysis with R, Springer
• Haerdle W., Simar L. (2015) Applied Multivariate Statistical Analysis, Springer
• Hair J.F., Black W., Babin B., Anderson R. (2019) Multivariate Data Analysis, Pearson