IDEAS home Printed from https://ideas.repec.org/p/cte/wbrepe/wb037117.html
   My bibliography  Save this paper

A comparison between correspondence analysis and categorical conjoint measurement

Author

Listed:
  • Torres Lacomba, Anna

Abstract

We show the equivalence of using correspondence analysis of concatenated tables and a particular algorithm of conjoint analysis named categorical conjoint measurement. The connection is made using canonical correlation. However, although we have proved that equivalence, the standard practice of conjoint analyses to focus in one dimension (the optimal solution) has some shortcomings once we introduce interaction effects. In that case, the use of visual techniques, like correspondence analysis, provides a faster and easier way to compile the preference structure. Finally, we provide an application of our setting making use of an experiment of perfumes where interaction effects between type of essences and strength of essences are shown.

Suggested Citation

  • Torres Lacomba, Anna, 2003. "A comparison between correspondence analysis and categorical conjoint measurement," DEE - Working Papers. Business Economics. WB wb037117, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    • Handle: RePEc:cte:wbrepe:wb037117
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/cfde0d49-418d-4070-8f57-50c08563d4a4/content
    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.
    2. Anna Torres, 2001. "Correspondence analysis and categorical conjoint measurement," Economics Working Papers 569, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
    Full references (including those not matched with items 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. Cajo Braak, 1990. "Interpreting canonical correlation analysis through biplots of structure correlations and weights," Psychometrika, Springer;The Psychometric Society, vol. 55(3), pages 519-531, September.
    2. Ganiere, Pierre & Chern, Wen S. & Hahn, David E., 2004. "Who Are Proponents And Opponents Of Genetically Modified Foods In The United States?," Working Papers 28315, Ohio State University, Department of Agricultural, Environmental and Development Economics.
    3. Shizuhiko Nishisato, 1996. "Gleaning in the field of dual scaling," Psychometrika, Springer;The Psychometric Society, vol. 61(4), pages 559-599, December.
    4. Michel Tenenhaus & Forrest Young, 1985. "An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis and other methods for quantifying categorical multivariate data," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 91-119, March.
    5. Sewell, Daniel K., 2018. "Visualizing data through curvilinear representations of matrices," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 255-270.
    6. 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.
    7. 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.
    8. Michael Greenacre, 2012. "Fuzzy coding in constrained ordinations," Economics Working Papers 1325, Department of Economics and Business, Universitat Pompeu Fabra.
    9. Jushan Bai & Serena Ng, 2020. "Simpler Proofs for Approximate Factor Models of Large Dimensions," Papers 2008.00254, arXiv.org.
    10. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org.
    11. Alfredo García-Hiernaux & José Casals & Miguel Jerez, 2012. "Estimating the system order by subspace methods," Computational Statistics, Springer, vol. 27(3), pages 411-425, September.
    12. Mitzi Cubilla‐Montilla & Ana‐Belén Nieto‐Librero & Ma Purificación Galindo‐Villardón & Ma Purificación Vicente Galindo & Isabel‐María Garcia‐Sanchez, 2019. "Are cultural values sufficient to improve stakeholder engagement human and labour rights issues?," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(4), pages 938-955, July.
    13. 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.
    14. Jos Berge & Henk Kiers, 1993. "An alternating least squares method for the weighted approximation of a symmetric matrix," Psychometrika, Springer;The Psychometric Society, vol. 58(1), pages 115-118, March.
    15. Shimeng Huang & Henry Wolkowicz, 2018. "Low-rank matrix completion using nuclear norm minimization and facial reduction," Journal of Global Optimization, Springer, vol. 72(1), pages 5-26, September.
    16. Antti J. Tanskanen & Jani Lukkarinen & Kari Vatanen, 2016. "Random selection of factors preserves the correlation structure in a linear factor model to a high degree," Papers 1604.05896, arXiv.org, revised Dec 2018.
    17. Jin-Xing Liu & Yong Xu & Chun-Hou Zheng & Yi Wang & Jing-Yu Yang, 2012. "Characteristic Gene Selection via Weighting Principal Components by Singular Values," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    18. Kargin, V. & Onatski, A., 2008. "Curve forecasting by functional autoregression," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2508-2526, November.
    19. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    20. W. Gibson, 1962. "On the least-squares orthogonalization of an oblique transformation," Psychometrika, Springer;The Psychometric Society, vol. 27(2), pages 193-195, June.

    More about this item

    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:cte:wbrepe:wb037117. 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: Ana Poveda (email available below). General contact details of provider: http://www.business.uc3m.es/es/index .

    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.