IDEAS home Printed from https://ideas.repec.org/a/kap/mktlet/v28y2017i1d10.1007_s11002-015-9389-3.html
   My bibliography  Save this article

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

Author

Listed:
  • Yu-Cheng Ku

    (North Carolina State University)

  • Tsun-Feng Chiang

    (Henan University)

  • Sheng-Mao Chang

    (National Cheng Kung University)

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.

Suggested Citation

  • Yu-Cheng Ku & Tsun-Feng Chiang & Sheng-Mao Chang, 2017. "Is what you choose what you want?—outlier detection in choice-based conjoint analysis," Marketing Letters, Springer, vol. 28(1), pages 29-42, March.
    • Handle: RePEc:kap:mktlet:v:28:y:2017:i:1:d:10.1007_s11002-015-9389-3
    DOI: 10.1007/s11002-015-9389-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11002-015-9389-3
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11002-015-9389-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Harvey Goldstein & Michael J. R. Healy, 1995. "The Graphical Presentation of a Collection of Means," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 175-177, January.
    3. 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.
    4. John R. Hauser, 1978. "Testing the Accuracy, Usefulness, and Significance of Probabilistic Choice Models: An Information-Theoretic Approach," Operations Research, INFORMS, vol. 26(3), pages 406-421, June.
    5. 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.
    6. 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.
    7. Louviere, Jordan J, 2001. "What If Consumer Experiments Impact Variances as Well as Means? Response Variability as a Behavioral Phenomenon," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(3), pages 506-511, December.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Benedikt M. Brand & Theresa Maria Rausch & Jannika Brandel, 2022. "The Importance of Sustainability Aspects When Purchasing Online: Comparing Generation X and Generation Z," Sustainability, MDPI, vol. 14(9), pages 1-28, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ku, Yu-Cheng & Wu, John, 2018. "Measuring respondent uncertainty in discrete choice experiments via utility suppression," Journal of choice modelling, Elsevier, vol. 27(C), pages 1-18.
    2. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    3. Lynd Bacon & Peter Lenk, 2012. "Augmenting discrete-choice data to identify common preference scales for inter-subject analyses," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 453-474, December.
    4. Alex Burnap & John Hauser, 2018. "Predicting "Design Gaps" in the Market: Deep Consumer Choice Models under Probabilistic Design Constraints," Papers 1812.11067, arXiv.org.
    5. Peter Lenk, 2014. "Bayesian estimation of random utility models," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 20, pages 457-497, Edward Elgar Publishing.
    6. Joseph Pancras, 2010. "A Framework to Determine the Value of Consumer Consideration Set Information for Firm Pricing Strategies," Computational Economics, Springer;Society for Computational Economics, vol. 35(3), pages 269-300, March.
    7. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    8. Michael Yee & Ely Dahan & John R. Hauser & James Orlin, 2007. "Greedoid-Based Noncompensatory Inference," Marketing Science, INFORMS, vol. 26(4), pages 532-549, 07-08.
    9. 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.
    10. Charles Cunningham & Ken Deal & Yvonne Chen, 2010. "Adaptive Choice-Based Conjoint Analysis," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 3(4), pages 257-273, December.
    11. Alexandre Belloni & Mitchell J. Lovett & William Boulding & Richard Staelin, 2012. "Optimal Admission and Scholarship Decisions: Choosing Customized Marketing Offers to Attract a Desirable Mix of Customers," Marketing Science, INFORMS, vol. 31(4), pages 621-636, July.
    12. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    13. B. P. S. Murthi & Sumit Sarkar, 2003. "The Role of the Management Sciences in Research on Personalization," Management Science, INFORMS, vol. 49(10), pages 1344-1362, October.
    14. Marc R. Dotson & Joachim Büschken & Greg M. Allenby, 2020. "Explaining Preference Heterogeneity with Mixed Membership Modeling," Marketing Science, INFORMS, vol. 39(2), pages 407-426, March.
    15. Max J. Pachali & Peter Kurz & Thomas Otter, 2020. "How to generalize from a hierarchical model?," Quantitative Marketing and Economics (QME), Springer, vol. 18(4), pages 343-380, December.
    16. Sha Yang & Gerg M. Allenby & Geraldine Fennel, 2002. "Modeling Variation in Brand Preference: The Roles of Objective Environment and Motivating Conditions," Marketing Science, INFORMS, vol. 21(1), pages 14-31, May.
    17. Jeffrey D. Camm & James J. Cochran & David J. Curry & Sriram Kannan, 2006. "Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem," Management Science, INFORMS, vol. 52(3), pages 435-447, March.
    18. Ali Aouad & Vivek Farias & Retsef Levi, 2021. "Assortment Optimization Under Consider-Then-Choose Choice Models," Management Science, INFORMS, vol. 67(6), pages 3368-3386, June.
    19. repec:cup:judgdm:v:4:y:2009:i:3:p:200-213 is not listed on IDEAS
    20. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    21. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.

    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:kap:mktlet:v:28:y:2017:i:1:d:10.1007_s11002-015-9389-3. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.