IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v4y1985i1p62-73.html
   My bibliography  Save this article

Generalizing the Rasch Model for Consumer Rating Scales

Author

Listed:
  • Gordon G. Bechtel

    (University of Florida)

Abstract

A probabilistic Rasch model is advocated for brand-attribute measurements which replace ubiquitous mean ratings. The usefulness of this model is then extended by showing that distinct latent processes, one extreme value and the other logistic, imply common probability structures for both its classical form and the generalization developed here. If given data reject the classical structure, an extended analysis is carried out in which logistic coefficients are estimated for the general model. These values are then used in a generalized-least-squares (GLS) procedure for estimating and testing the brand-attribute locations. An illustrative multiattribute analysis is given in which logistic coefficients and locations are found for 16 soft drinks on the continua of , and .

Suggested Citation

  • Gordon G. Bechtel, 1985. "Generalizing the Rasch Model for Consumer Rating Scales," Marketing Science, INFORMS, vol. 4(1), pages 62-73.
    • Handle: RePEc:inm:ormksc:v:4:y:1985:i:1:p:62-73
    DOI: 10.1287/mksc.4.1.62
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.4.1.62
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.4.1.62?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Gordon Bechtel & Chezy Ofir, 1988. "Aggregate item response analysis," Psychometrika, Springer;The Psychometric Society, vol. 53(1), pages 93-107, March.
    2. de Jong, M.G., 2006. "Response bias in international marketing research," Other publications TiSEM 5d4031be-97b5-4db3-962b-2, Tilburg University, School of Economics and Management.
    3. Maud Dampérat & Ping Lei & Florence Jeannot, 2019. "IRT Approach for rating scales: applications for normal and non-normal distributions," Post-Print hal-04325043, HAL.
    4. Salzberger, Thomas & Koller, Monika, 2013. "Towards a new paradigm of measurement in marketing," Journal of Business Research, Elsevier, vol. 66(9), pages 1307-1317.
    5. Ling Peng & Geng Cui & Yuho Chung & Chunyu Li, 2019. "A multi-facet item response theory approach to improve customer satisfaction using online product ratings," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 960-976, September.

    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:inm:ormksc:v:4:y:1985:i:1:p:62-73. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.