, Volume 67, Issue 2, pp 211–224 | Cite as

Generalized constrained multiple correspondence analysis

  • Heungsun Hwang
  • Yoshio Takane


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.

Key words

multiple correspondence analysis linear constraints projection operators generalized singular value decomposition 


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Copyright information

© The Psychometric Society 2002

Authors and Affiliations

  1. 1.McGill UniversityUSA

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