Duration
15h Th, 15h Pr
Number of credits
| Doctoral training in economics and business management (Management) | 5 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
All year long, with partial in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Description of the course:
Content:
- Repetition of essential statistical concepts for multivariate data analysis
- Preparing the data for multivariate analysis (assumptions, data examination, ...)
- Interdependence techniques:
- Factor Analysis
- Principal Components Analysis
- Cluster Analysis
- Structural Equation Modeling
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 outcomes of the learning unit
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
Prerequisite knowledge and skills
- Basic course in probability (concepts of probability density, distribution, mean, variance)
- Introductory knowledge of open source software R or the willingness to learn it in autonomy during the period of the course
Planned learning activities and teaching methods
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Course materials and recommended or required readings
Platform(s) used for course materials:
- LOL@
Further information:
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
Written work / report
Further information:
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%)
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Per email to m.ulm@uliege.be