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A comparative study between latent class binomial segmentation and mixed-effects logistic regression to explore between-respondent variability in visual preference for horticultural products

Author

Listed:
  • E. Schrevens
  • H. Coppenolle
  • K. M. Portier

Abstract

A methodological concept is proposed to study between-respondent variability in visual preference for horticultural products using quantitative imaging techniques. Chicory, a typical Belgian vegetable, serves as a model product. Eight image sequences of high-quality chicory, each representing a different combination of two factor levels of length, width and ovality, were constructed to satisfy a 23 factorial design by using quantitative imaging techniques. The image sequences were pair-wise visualized using a computer-based image system to study visual preference. Twenty respondents chose which of two samples was preferred in all 28 pair-wise combinations of the eight constructed image sequences. The consistency of the respondents and the agreement between respondents was evaluated. The poor fit of a traditional binomial logit model that relates preference with quality descriptors was due to the low agreement in preference between respondents. Therefore, latent class binomial segmentation is compared to mixed-effects logistic regression. Both approaches relax the traditional assumption that the same model holds for all respondents by recognizing the typical between-respondent variability inherent in preference studies. Where the latent class model simultaneously estimates different logit models for different consumer segments, the mixed-effects model recognizes between-respondent variability by incorporating random effects varying by respondent in model formulation.

Suggested Citation

  • E. Schrevens & H. Coppenolle & K. M. Portier, 2005. "A comparative study between latent class binomial segmentation and mixed-effects logistic regression to explore between-respondent variability in visual preference for horticultural products," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(6), pages 589-605.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:589-605
    DOI: 10.1080/02664760500078987
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    References listed on IDEAS

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