Aims

" To be able to constitute a complete qualitative research concerning management issues, starting from various documentary sources and interviews, in a coherent and well-argued way
at theoretical and methodological levels;
" To be able to make decisions on the basis of quantitative information, and to assess accurately the performances of the mobilized models.

Main themes

" A Presentation of qualitative data analysis methods, in particular categorical, inductive, structural methods, …
" A Presentation of quantitative data analysis methods, in particular scoring methodology and classification;
" Reading texts containing data analysis methods;
" Exercises in appropriation by a group work, in analysing methods of qualitative and quantitative materials collected personally or placed at the disposal;
" Initiation to professional data analysis software such as Atlas-TI, SAS/JMP and R.

Content and teaching methods

Content
" The critical analysis of data sources and types in management sciences; clear identification of the context in which the data were collected; assessment of the validity of the data (external as well as internal):
o Secondary data: reports, statistics, web, …
o Primary data: interviews, surveys, …

" The study of data analysis methods, with a focus on the interpretation of the results; in particular content analysis, classification, scoring methodology :
o Qualitative data: content analysis, case study, projection and facilitation methods, structural analysis, …
o Quantitative data: clustering, factorial and projection methods, decision trees, logistic regression, …

" A discussion on which method to use in function of the problem at hand and the available data.

Methods

A combination of lectures, practical exercises and a project dealing with real data.
The course is divided in two modules: Qualitative data analysis (Alain Vas) and quantitative data analysis (Marie-Paule Kestemont et Marco Saerens).

Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)

Prerequisite : A course in multivariate statistical analysis
Evaluation : Projects reports by group
Support : Book chapters provided to the students
References :
" Duda, Hart & Stork (2001), "Pattern classification, 2nd ed". John Wiley & Sons.
" Bardos (2001), "Analyse discriminante. Application au risque et scoring financier. Dunod.
" Miles, M. B., & Huberman, A. M. (1994), "Qualitative Data Analysis: An Expanded Sourcebook". Thousand Oaks: Sage Publications.

Pedagogic team: Marie-Paule Kestemont, Marco Saerens, Alain Vas

Other credits in programs