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Is what you choose what you want?—outlier detection in choice-based conjoint analysis

Listed author(s):
  • Yu-Cheng Ku

    ()

    (North Carolina State University)

  • Tsun-Feng Chiang

    ()

    (Henan University)

  • Sheng-Mao Chang

    ()

    (National Cheng Kung University)

Registered author(s):

    Abstract Choice-based conjoint (CBC) analysis has long been a popular technique in market research. Because CBC is dependent upon respondents’ stated preferences, respondent variability should be taken into account in part-worth estimation. In the spirit of Bayesian residuals within the probit framework, this paper proposes a novel respondent variability measure for CBC, called the “utility deviation” (UD), to detect outliers who have unusually high respondent variability. UD is constructed based on the standardized deviation between a respondent’s true and representative utilities on the made choices. We compare UD with the largest absolute realized deviation (LARD) statistic and the typically used metric, root likelihood (RLH), in the performance of outlier detection using simulated and empirical data. The results show that UD performs slightly better than LARD and significantly outperforms RLH. Finally, we show that performing outlier detection to exclude misleading data can significantly improve the quality of estimation and resultant applications.

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    File URL: http://link.springer.com/10.1007/s11002-015-9389-3
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    Article provided by Springer in its journal Marketing Letters.

    Volume (Year): 28 (2017)
    Issue (Month): 1 (March)
    Pages: 29-42

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    Handle: RePEc:kap:mktlet:v:28:y:2017:i:1:d:10.1007_s11002-015-9389-3
    DOI: 10.1007/s11002-015-9389-3
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    Order Information: Web: http://www.springer.com/business+%26+management/marketing/journal/11002/PS2?detailsPage=societies

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    1. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    2. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    3. Aizaki, Hideo, 2012. "Basic Functions for Supporting an Implementation of Choice Experiments in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(c02).
    4. Rinus Haaijer & Michel Wedel & Marco Vriens & Tom Wansbeek, 1998. "Utility Covariances and Context Effects in Conjoint MNP Models," Marketing Science, INFORMS, vol. 17(3), pages 236-252.
    5. Louviere, Jordan J, 2001. " What If Consumer Experiments Impact Variances as Well as Means? Response Variability as a Behavioral Phenomenon," Journal of Consumer Research, Oxford University Press, vol. 28(3), pages 506-511, December.
    6. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
    7. Robert Zeithammer & Peter Lenk, 2006. "Bayesian estimation of multivariate-normal models when dimensions are absent," Quantitative Marketing and Economics (QME), Springer, vol. 4(3), pages 241-265, September.
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