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Simultaneous analysis and multiple factor analysis for contingency tables: Two methods for the joint study of contingency tables

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  • Zárraga, A.
  • Goitisolo, B.

Abstract

When studying more than one contingency table at the same time, it should be considered that factorial results may be affected by the differences between the totals of the tables and by the different structures of the relationships between such tables. Two new methods have recently appeared that seek to solve this problem based on correspondence analysis, using certain characteristics of multiple factorial analysis. These methods are Simultaneous Analysis (SA) and Multiple Factorial Analysis for Contingency Tables (MFACT). The two methods are very similar, but the main difference between them lies in the allocation of the weights attributed to each table. Similarities and differences between them are discussed and a brief example is provided to show the factorial results provided by each one.

Suggested Citation

  • Zárraga, A. & Goitisolo, B., 2009. "Simultaneous analysis and multiple factor analysis for contingency tables: Two methods for the joint study of contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3171-3182, June.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:3171-3182
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    References listed on IDEAS

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    1. Vallejo-Arboleda, Amparo & Vicente-Villardon, Jose L. & Galindo-Villardon, M.P., 2007. "Canonical STATIS: Biplot analysis of multi-table group structured data based on STATIS-ACT methodology," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4193-4205, May.
    2. J. Gower, 1975. "Generalized procrustes analysis," Psychometrika, Springer;The Psychometric Society, vol. 40(1), pages 33-51, March.
    3. Michael Greenacre, 2003. "Singular value decomposition of matched matrices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1101-1113.
    4. Escofier, B. & Pages, J., 1994. "Multiple factor analysis (AFMULT package)," Computational Statistics & Data Analysis, Elsevier, vol. 18(1), pages 121-140, August.
    5. Gardner, Sugnet & Gower, John C. & le Roux, N.J., 2006. "A synthesis of canonical variate analysis, generalised canonical correlation and Procrustes analysis," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 107-134, January.
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    Cited by:

    1. Blasius, J. & Greenacre, M. & Groenen, P.J.F. & van de Velden, M., 2009. "Special issue on correspondence analysis and related methods," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3103-3106, June.
    2. K. Fernández-Aguirre & M. Garín-Martín & J. Modroño-Herrán, 2014. "Visual displays: analytical study and applications to graphs and real data," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2209-2224, July.

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