Simple and multiple correspondence analysis for ordinal-scale variables using orthogonal polynomials

Rosaria Lombardo and Eric Beh ()

Journal of Applied Statistics, 2010, vol. 37, issue 12, 2101-2116

Abstract: Correspondence analysis (CA) has gained a reputation for being a very useful statistical technique for determining the nature of association between two or more categorical variables. For simple and multiple CA, the singular value decomposition (SVD) is the primary tool used and allows the user to construct a low-dimensional space to visualize this association. As an alternative to SVD, one may consider the bivariate moment decomposition (BMD), a method of decomposition that involves using orthogonal polynomials to reflect the structure of ordered categorical responses. When the features of BMD are combined with SVD, a hybrid decomposition (HD) is formed. The aim of this paper is to show the applicability of HD when performing simple and multiple CA.

Keywords: multiple correspondence analysis; ordinal-scale variables; singular value decomposition; bivariate moment decomposition; orthogonal polynomials; hybrid decomposition (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1080/02664760903247692

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