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Multiple Correspondence Analysis Herv Abdi1
 

Summary: Multiple Correspondence Analysis
Hervé Abdi1
& Dominique Valentin
1 Overview
Multiple correspondence analysis (MCA) is an extension of corre-
spondence analysis (CA) which allows one to analyze the pattern of
relationships of several categorical dependent variables. As such,
it can also be seen as a generalization of principal component anal-
ysis when the variables to be analyzed are categorical instead of
quantitative. Because MCA has been (re)discovered many times,
equivalent methods are known under several different names such
as optimal scaling, optimal or appropriate scoring, dual scaling,
homogeneity analysis, scalogram analysis, and quantification me-
thod.
Technically MCA is obtained by using a standard correspon-
dence analysis on an indicator matrix (i.e., a matrix whose entries
are 0 or 1). The percentages of explained variance need to be cor-
rected, and the correspondence analysis interpretation of inter-
point distances needs to be adapted.
1

  

Source: Abdi, Hervé - School of Behavioral and Brain Sciences, University of Texas at Dallas

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences