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Clustering and Scaling: Grouping Variables in Burt Matrices

  • S. Gabler
  • J. Blasius
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Although clustering and scaling are different techniques, it has been shown that in the case of a two-way cross-table, cluster analysis and correspondence analysis provide similar solutions (Greenacre 1988a, 1993; Lebart 1994). In this paper we extend this approach to the multiple case. Instead of analyzing two variables we use a set of variables. When scaling the data we apply joint correspondence analysis which is the generalization of simple correspondence analysis (Greenacre 1988b, 1993). The suggested clustering process is hierarchical whereby the similarity matrix consists of standardized χ2-values computed from the subtables of the Burt matrix. As an example we use 25 variables of cultural competences taken from the German General Social Survey 1986 (ALLBUS).

Keywords

Correspondence Analysis Cultural Competence Ride Bicycle Housing Satisfaction Play Chess 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Fahrmeir, L. and Hamerle, A. (eds.) (1984): Multivariate statistische Verfahren. Walter de Gruyter, Berlin.Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • S. Gabler
    • 1
  • J. Blasius
    • 2
  1. 1.Zentrum für Umfragen, Methoden und AnalysenMannheimGermany
  2. 2.Zentralarchiv für Empirische Sozialforschung Universität zu KölnKölnGermany

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