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Analysis of choice behaviour via probabilistic ideal point and vector models

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  • I. Böckenholt
  • W. Gaul

Abstract

The aim of this paper is to add results to the everlasting attempt to find appropriate models for the description and analysis of choice behaviour. As stochastic generalizations within choice models which allow estimation and testing in a straightforward way should be of great interest, probabilistic ideal point and vector approaches are used to handle inter‐ and intra‐individual irregularities in paired comparisons preference data. For choice behaviour analysis maximum likelihood estimates of the model parameters are computed and possibilities of testing various model variants by means of log‐likelihood ratio tests are discussed. The proposed models offer information to decide whether an ideal point or a vector approach is more appropriate for the description of the pairwise choice data. Examples of data sets known from previous research in choice behaviour are used for the comparison and for the demonstration of the merits of the proposed procedures.

Suggested Citation

  • I. Böckenholt & W. Gaul, 1986. "Analysis of choice behaviour via probabilistic ideal point and vector models," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 2(4), pages 209-226.
    • Handle: RePEc:wly:apsmda:v:2:y:1986:i:4:p:209-226
    DOI: 10.1002/asm.3150020405
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    Cited by:

    1. DeSarbo, Wayne S. & Kim, Youngchan & Wedel, Michel & Fong, Duncan K. H., 1998. "A Bayesian approach to the spatial representation of market structure from consumer choice data," European Journal of Operational Research, Elsevier, vol. 111(2), pages 285-305, December.
    2. Ulf Böckenholt & William Dillon, 1997. "Modeling within-subject dependencies in ordinal paired comparison data," Psychometrika, Springer;The Psychometric Society, vol. 62(3), pages 411-434, September.
    3. Ulf Böckenholt, 1990. "Multivariate thurstonian models," Psychometrika, Springer;The Psychometric Society, vol. 55(2), pages 391-403, June.
    4. de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
    5. Tomoya Okubo & Shin-ichi Mayekawa, 2015. "Modeling Viewpoint Shifts in Probabilistic Choice," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 412-427, June.
    6. Baier, Daniel & Gaul, Wolfgang, 1998. "Optimal product positioning based on paired comparison data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 365-392, November.

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