Homogeneity Analysis for Partitioning Qualitative Variables

  • Takahiro Tsuchiya
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


This paper proposes a method to construct multiple uni-dimensional scales by partitioning qualitative variables into mutually exclusive groups. The method is based on homogeneity analysis, and fuzzy c-means criterion is introduced for partitioning. Also, some goodness of fit indexes are proposed. Two artificial data sets and one real data set are analyzed as numerical examples. The results illustrate that the proposed method is more effective for partitioning qualitative variables compared with PCA with optimal scaling and Hayashi’s Quantification Method III or HOMALS.


Global Minimum Qualitative Variable Multiple Correspondence Analysis Artificial Data Score Vector 
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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • Takahiro Tsuchiya
    • 1
  1. 1.The Institute of Statistical MathematicsMinato-ku, Tokyo 106Japan

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