Multidimensional Scaling Versus Multiple Correspondence Analysis When Analyzing Calegorization Data

Abstract : Categorization is a cognitive process in which subjects are asked to group a set of object according to their similarities. This task was used for the first time in psychology and is becoming now more and more popular in sensory analysis. Categorization data are usually analyzed by multidimensional scaling (MDS). In this article we propose an original approach based on multiple correspondence analysis (MCA) this new methodology which provides new insights on the data will be compared to one specified procedure of MDS.
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Communication dans un congrès
1st Joint Meeting of the Societe-Francophone-de-Classification and the Classification and Data Analysis Group of the Italian-Statistical-Society, Jun 2008, Caserta (ITA), Italy. Springer-verlag berlin, pp.8, 2011, 〈10.1007/978-3-642-13312-1_31〉
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https://hal-agrocampus-ouest.archives-ouvertes.fr/hal-00730150
Contributeur : Céline Martel <>
Soumis le : vendredi 7 septembre 2012 - 16:11:24
Dernière modification le : jeudi 21 juin 2018 - 01:22:10

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Marine Cadoret, Sébastien Lê, Jérome Pagès. Multidimensional Scaling Versus Multiple Correspondence Analysis When Analyzing Calegorization Data. 1st Joint Meeting of the Societe-Francophone-de-Classification and the Classification and Data Analysis Group of the Italian-Statistical-Society, Jun 2008, Caserta (ITA), Italy. Springer-verlag berlin, pp.8, 2011, 〈10.1007/978-3-642-13312-1_31〉. 〈hal-00730150〉

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