Abstract
A comprehensive approach for imposing both row and column constraints on multivariate discrete data is proposed that may be called generalized constrained multiple correspondence analysis (GCMCA). In this method each set of discrete data is first decomposed into several submatrices according to its row and column constraints, and then multiple correspondence analysis (MCA) is applied to the decomposed submatrices to explore relationships among them. This method subsumes existing constrained and unconstrained MCA methods as special cases and also generalizes various kinds of linearly constrained correspondence analysis methods. An example is given to illustrate the proposed method.
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Heungsun Hwang is now at Claes Fornell International Group. The work reported in this paper was supported by Grant A6394 from the Natural Sciences and Engineering Research Council of Canada to the second author.
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Hwang, H., Takane, Y. Generalized constrained multiple correspondence analysis. Psychometrika 67, 211–224 (2002). https://doi.org/10.1007/BF02294843
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DOI: https://doi.org/10.1007/BF02294843