Durée
15h Th, 15h Pr
Nombre de crédits
| Formation doctorale en sciences économiques et de gestion (Sciences de gestion) | 5 crédits |
Enseignant
Langue(s) de l'unité d'enseignement
Langue française
Organisation et évaluation
Enseignement durant l'année complète, avec partiel en janvier
Horaire
Unités d'enseignement prérequises et corequises
Les unités prérequises ou corequises sont présentées au sein de chaque programme
Contenus de l'unité d'enseignement
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.
Acquis d'apprentissage (objectifs d'apprentissage) de l'unité d'enseignement
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
Savoirs et compétences prérequis
- 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
Activités d'apprentissage prévues et méthodes d'enseignement
Mode d'enseignement (présentiel, à distance, hybride)
Cours donné exclusivement en présentiel
Supports de cours, lectures obligatoires ou recommandées
Plate-forme(s) utilisée(s) pour les supports de cours :
- LOL@
Informations complémentaires:
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
Modalités d'évaluation et critères
Travail à rendre - rapport
Informations complémentaires:
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%)
Stage(s)
Remarques organisationnelles et modifications principales apportées au cours
Contacts
Per email to m.ulm@uliege.be