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

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  • Rosaria Lombardo
  • Eric Beh

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.

Suggested Citation

  • Rosaria Lombardo & Eric Beh, 2010. "Simple and multiple correspondence analysis for ordinal-scale variables using orthogonal polynomials," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2101-2116.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2101-2116
    DOI: 10.1080/02664760903247692
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    References listed on IDEAS

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    1. Rosaria Lombardo & Jacqueline Meulman, 2010. "Multiple Correspondence Analysis via Polynomial Transformations of Ordered Categorical Variables," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 191-210, September.
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    Cited by:

    1. Rosaria Lombardo & Ida Camminatiello & Eric J. Beh, 2019. "Assessing Satisfaction with Public Transport Service by Ordered Multiple Correspondence Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(1), pages 355-369, May.
    2. Rosaria Lombardo & Eric Beh & Antonello D'Ambra, 2011. "Studying the dependence between ordinal-nominal categorical variables via orthogonal polynomials," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2119-2132.

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