IDEAS home Printed from https://ideas.repec.org/p/upf/upfgen/630.html
   My bibliography  Save this paper

Measuring asymmetries in brand associations using correspondence analysis

Author

Abstract

Correspondence analysis is introduced in the brand association literature as an alternative tool to measure dominance, for the particular case of free choice data. The method is also used to analyse differences, or asymmetries, between brand-attribute associations where attributes are associated with evoked brands, and brand-attribute associations where brands are associated with the attributes. An application to a sample of deodorants is used to illustrate the proposed methodology.

Suggested Citation

  • Michael Greenacre & Anna Torres, 2002. "Measuring asymmetries in brand associations using correspondence analysis," Economics Working Papers 630, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:630
    as

    Download full text from publisher

    File URL: https://econ-papers.upf.edu/papers/630.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.
    2. John Aitchison & Michael Greenacre, 2002. "Biplots of compositional data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 375-392, October.
    3. Michael Greenacre, 2001. "Analysis of matched matrices," Economics Working Papers 539, Department of Economics and Business, Universitat Pompeu Fabra.
    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. Greenacre, Michael, 2009. "Power transformations in correspondence analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3107-3116, June.
    2. Michael Greenacre, 2006. "Tying up the loose ends in simple correspondence analysis," Economics Working Papers 940, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Michael Greenacre & Paul Lewi, 2009. "Distributional Equivalence and Subcompositional Coherence in the Analysis of Compositional Data, Contingency Tables and Ratio-Scale Measurements," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 29-54, April.
    4. Michael Greenacre, 2003. "Singular value decomposition of matched matrices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1101-1113.
    5. Udina, Frederic, 2005. "Interactive Biplot Construction," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 13(i05).
    6. Michael Greenacre, 2002. "Ratio maps and correspondence analysis," Economics Working Papers 598, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Michael Greenacre & Rafael Pardo, 2004. "Subset correspondence analysis: Visualizing relationships among a selected set of response categories from a questionnaire survey," Economics Working Papers 791, Department of Economics and Business, Universitat Pompeu Fabra.
    8. B. Baris Alkan & Afsin Sahin, 2011. "Measuring inequalities in the distribution of health workers by bi-plot approach: The case of Turkey," Journal of Economics and Behavioral Studies, AMH International, vol. 2(2), pages 57-66.
    9. 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.
    10. 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.
    11. Michael Greenacre, 2011. "A Simple Permutation Test for Clusteredness," Working Papers 555, Barcelona School of Economics.
    12. 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.
    13. Michael Greenacre, 2012. "Fuzzy coding in constrained ordinations," Economics Working Papers 1325, Department of Economics and Business, Universitat Pompeu Fabra.
    14. Michael Greenacre, 2016. "Selection and statistical analysis of compositional ratios," Economics Working Papers 1551, Department of Economics and Business, Universitat Pompeu Fabra.
    15. 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.
    16. Giovanni C. Porzio & Giancarlo Ragozini & Domenico Vistocco, 2008. "On the use of archetypes as benchmarks," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 419-437, September.
    17. 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.
    18. Javier Palarea-Albaladejo & Josep Martín-Fernández & Jesús Soto, 2012. "Dealing with Distances and Transformations for Fuzzy C-Means Clustering of Compositional Data," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 144-169, July.
    19. 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.
    20. 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.

    More about this item

    Keywords

    Brand dominance; attribute dominance; measure of assymetries; correspondence analysis;
    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

    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:630. 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.