Multidimensional Scaling Versus Multiple Correspondence Analysis When Analyzing Calegorization Data

Marine Cadoret; Sébastien Lê; Jérome Pagès

doi:10.1007/978-3-642-13312-1_31

Multidimensional Scaling Versus Multiple Correspondence Analysis When Analyzing Calegorization Data

Marine Cadoret ¹ Sébastien Lê ¹ Jérome Pagès ^{1, 2}

1 IRMAR - Institut de Recherche Mathématique de Rennes

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.

Document type :

Conference papers

Domain :

Mathematics [math] / Statistics [math.ST]

Statistics [stat] / Statistics Theory [stat.TH]

Complete list of metadatas

https://hal-agrocampus-ouest.archives-ouvertes.fr/hal-00730150
Contributor : Céline Martel <>
Submitted on : Friday, September 7, 2012 - 4:11:24 PM
Last modification on : Thursday, November 28, 2019 - 4:50:12 PM

Multidimensional Scaling Versus Multiple Correspondence Analysis When Analyzing Calegorization Data

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Multidimensional Scaling Versus Multiple Correspondence Analysis When Analyzing Calegorization Data

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