IDEAS home Printed from https://ideas.repec.org/p/upf/upfgen/569.html
   My bibliography  Save this paper

Correspondence analysis and categorical conjoint measurement

Author

Abstract

We show the equivalence between the use of correspondence analysis (CA) of concadenated tables and the application of a particular version of conjoint analysis called categorical conjoint measurement (CCM). The connection is established using canonical correlation (CC). The second part introduces the interaction e¤ects in all three variants of the analysis and shows how to pass between the results of each analysis.

Suggested Citation

  • Anna Torres, 2001. "Correspondence analysis and categorical conjoint measurement," Economics Working Papers 569, Department of Economics and Business, Universitat Pompeu Fabra.
  • Handle: RePEc:upf:upfgen:569
    as

    Download full text from publisher

    File URL: https://econ-papers.upf.edu/papers/569.pdf
    File Function: Whole Paper
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael Greenacre, 2008. "Correspondence analysis of raw data," Economics Working Papers 1112, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2009.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:cte:wbrepe:wb037117 is not listed on IDEAS

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eric Beh & Luigi D’Ambra, 2009. "Some Interpretative Tools for Non-Symmetrical Correspondence Analysis," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 55-76, April.
    2. Pilar García Gómez & Ángel López Nicolás, 2005. "Socio-economic inequalities in health in Catalonia," Hacienda Pública Española / Review of Public Economics, IEF, vol. 175(4), pages 103-121, december.
    3. Michael Greenacre, 2011. "A Simple Permutation Test for Clusteredness," Working Papers 555, Barcelona School of Economics.
    4. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    5. Michael Greenacre, 2012. "Fuzzy coding in constrained ordinations," Economics Working Papers 1325, Department of Economics and Business, Universitat Pompeu Fabra.
    6. Rémi Bazillier & Nicolas Sirven, 2006. "Les normes fondamentales du travail contribuent-elles à réduire les inégalités ?," Revue Française d'Économie, Programme National Persée, vol. 21(2), pages 111-146.
    7. Alfonso Gambardella & Walter Garcia Fontes, 1996. "European research funding and regional technological capabilities: Network composition analysis," Economics Working Papers 174, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Paul Green & Jonathan Kim & Frank Carmone, 1990. "A preliminary study of optimal variable weighting in k-means clustering," Journal of Classification, Springer;The Classification Society, vol. 7(2), pages 271-285, September.
    9. Michael J. Greenacre & Patrick J. F. Groenen, 2016. "Weighted Euclidean Biplots," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 442-459, October.
    10. Malcolm Dow & Peter Willett & Roderick McDonald & Belver Griffith & Michael Greenacre & Peter Bryant & Daniel Wartenberg & Ove Frank, 1987. "Book reviews," Journal of Classification, Springer;The Classification Society, vol. 4(2), pages 245-278, September.
    11. Vartan Choulakian, 1988. "Exploratory analysis of contingency tables by loglinear formulation and generalizations of correspondence analysis," Psychometrika, Springer;The Psychometric Society, vol. 53(2), pages 235-250, June.
    12. W. Krzanowski & Gregory Cermak & Jan Leeuw & Fionn Murtagh & Peter Bryant & Bernard Monjardet & Chikio Hayashi, 1985. "Book reviews," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 277-299, December.
    13. François Bavaud, 2011. "On the Schoenberg Transformations in Data Analysis: Theory and Illustrations," Journal of Classification, Springer;The Classification Society, vol. 28(3), pages 297-314, October.
    14. Maura Vásquez & Guillermo Ramírez & Alberto Camardiel & Tomás Aluja, 2008. "A Biplot graphical tool to model the relationships between two sets of variables," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 33(25), pages 117-130, january-j.
    15. Jurlin, Kresimir & Malekovic, Sanja & Puljiz, Jaksa & Cziraky, Dario & Polic, Mario, 2002. "Covariance structure analysis of regional development data: an application to municipality development assessment," ERSA conference papers ersa02p469, European Regional Science Association.
    16. Robert Boik, 1996. "An efficient algorithm for joint correspondence analysis," Psychometrika, Springer;The Psychometric Society, vol. 61(2), pages 255-269, June.
    17. Jos Berge, 1995. "Review," Psychometrika, Springer;The Psychometric Society, vol. 60(2), pages 313-315, June.
    18. Evert Meijers, 2005. "High-level consumer services in polycentric urban regions - hospital care and higher education between duplication and complementarity," ERSA conference papers ersa05p208, European Regional Science Association.
    19. Laurent Lesnard & Thibaut Saint Pol, 2009. "Patterns of Workweek Schedules in France," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 93(1), pages 171-176, August.
    20. Warrens, Matthijs J. & Heiser, Willem J., 2009. "Diagnostics for regression dependence in tables re-ordered by the dominant correspondence analysis solution," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3139-3144, June.

    More about this item

    Keywords

    Correspondence analysis; conjoint analysis; canonical correlation; categorical data;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:upf:upfgen:569. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: http://www.econ.upf.edu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.