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Analysis of matched matrices

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Abstract

We consider the joint visualization of two matrices which have common rows and columns, for example multivariate data observed at two time points or split accord-ing to a dichotomous variable. Methods of interest include principal components analysis for interval-scaled data, or correspondence analysis for frequency data or ratio-scaled variables on commensurate scales. A simple result in matrix algebra shows that by setting up the matrices in a particular block format, matrix sum and difference components can be visualized. The case when we have more than two matrices is also discussed and the methodology is applied to data from the International Social Survey Program.

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Bibliographic Info

Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 539.

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Date of creation: Mar 2001
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Handle: RePEc:upf:upfgen:539

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Web page: http://www.econ.upf.edu/

Related research

Keywords: Correspondence analysis; International Social Survey Program (ISSP); matched matrices; principal component analysis; singular-value decomposition;

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  1. Michael Greenacre, 2008. "Correspondence analysis of raw data," Economics Working Papers 1112, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2009.
  2. Michael Greenacre, 2000. "Correspondence analysis of square asymmetric matrices," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 297-310.
  3. Michael Greenacre & Anna Torres, 1999. "A note on the dual scaling of dominance data and its relationship to correspondence analysis," Economics Working Papers 430, Department of Economics and Business, Universitat Pompeu Fabra.
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Cited by:
  1. Michael Greenacre & Anna Torres, 2002. "Measuring asymmetries in brand associations using correspondence analysis," Economics Working Papers 630, Department of Economics and Business, Universitat Pompeu Fabra.

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