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Simple and multiple correspondence analysis

  • Johannes Hjellbrekke, professeur à l'Université de Bergen ( Hors EHESS )

    Cet enseignant est référent pour cette UE

S'il s'agit de l'enseignement principal d'un enseignant, le nom de celui-ci est indiqué en gras.

Les 30 et 31 mars et 1er avril 2020 de 8 h 30 à 18 h (Université Paris-Dauphine, Place du Maréchal de Lattre de Tassigny 75016 Paris), salle communiquée ultérieurement

In the social sciences, multiple correspondence analysis (MCA) is a statistical technique that perhaps has become best known through the work of the late Pierre Bourdieu (1930-2002), in particular “Distinction” (Bourdieu 1984), “Homo Academicus” (Bourdieu 1988) and “The State Nobility” (Bourdieu 1996). In more recent years, the technique has found a wider audience, and is now used by social scientists in several disciplines.

As a counterpart to principal component analysis (PCA), a geometric technique for the analysis of metric variables, MCA is a geometric technique for the analysis of categorical or categorized variables. Originating in the early 1960s and the French statistician Jean-Paul Benzécri’s work in mathematical linguistics, MCA represents and models data sets as clouds of points in a multidimensional Euclidean space. The interpretation of the data is based on these clouds of points. By combining MCA with inferential techniques and variance analysis, we arrive at an integrated framework of interpretation that also is known under the name of Geometric Data Analysis (GDA).

In a combination of lectures and laboratory excercises, this course will introduce students to the fundamental properties, procedures and rules of interpretation of the most commonly used forms of correspondence analysis, i.e. simple correspondence analysis (CA) and MCA, and also to the most commonly used software. A main emphasis will be put on how to use MCA in one’s own work, and on practical examples and applications. Particular attention will therefore be paid to how MCA can be used in the construction of social spaces.

The course starts with an historical introduction to Benzécri’s work on contingency tables, and to the key ideas and basic properties in geometric data analysis. A first explanation of the procedures, the key concepts and the fundamental rules of interpretation will be given through a simple correspondence analysis (CA) of a standard contingency table.

Thereafter, we will go through the generalisation from CA to MCA by analysing an Individuals x Variables table. Particular emphasis will be put on the definition of distances between individuals, of distances between categories or modalities, the fundamental rules for the interpretation of axes in MCA, on how MCA can be integrated with variance analysis, and also on more general guidelines and coding principles.

We will then proceed to the more detailed exploration of the cloud of individuals, the introduction of supplementary variables, the use of concentration ellipses and how MCA also can be used in a confirmatory or explanatory mode by introducing variables as structuring factors in the constructed space. Tools for statistical inference, i.e. confidence ellipses around mean modality points in factorial planes, will also be presented, as will also the integration of MCA and hierarchical clustering as a way of further validation of the results.

Suivi et validation pour le master : Spécial : cf. le descriptif

Mentions & parcours :

Intitulés généraux :

Renseignements :

le cours se tiendra sous forme de trois journées bloquées au mois de mars 2020.  Il est réservé en priorité aux étudiants du Master Institutions, Organisations, Économie et Société (EHESS, Dauphine, Mines ParisTech) mais peut être ouvert sur demande dans la limite des effectifs disponibles. Les inscriptions additionnelles ne pourront pas être validées avant le mois de janvier.

Adresse(s) électronique(s) de contact : jhjellbrekke(at)msh-paris.fr

Dernière modification de cette fiche par le service des enseignements (sg12@ehess.fr) : 19 novembre 2019.

Contact : service des enseignements ✉ sg12@ehess.fr ☎ 01 49 54 23 17 ou 01 49 54 23 28
Réalisation : Direction des Systèmes d'Information
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