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Generalizing the Rasch Model for Consumer Rating Scales

Author

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  • 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
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    Cited by:

    1. Maud Dampérat & Ping Lei & Florence Jeannot, 2019. "IRT Approach for rating scales: applications for normal and non-normal distributions," Post-Print hal-04325043, HAL.
    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. 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.
    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. Gordon Bechtel & Chezy Ofir, 1988. "Aggregate item response analysis," Psychometrika, Springer;The Psychometric Society, vol. 53(1), pages 93-107, March.

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