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Obtaining more information from conjoint experiments by best-worst choices

Author

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  • Vermeulen, Bart
  • Goos, Peter
  • Vandebroek, Martina

Abstract

Conjoint choice experiments elicit individuals' preferences for the attributes of a good by asking respondents to indicate repeatedly their most preferred alternative in a number of choice sets. However, conjoint choice experiments can be used to obtain more information than that revealed by the individuals' single best choices. A way to obtain extra information is by means of best-worst choice experiments in which respondents are asked to indicate not only their most preferred alternative but also their least preferred one in each choice set. To create D-optimal designs for these experiments, an expression for the Fisher information matrix for the maximum-difference model is developed. Semi-Bayesian D-optimal best-worst choice designs are derived and compared with commonly used design strategies in marketing in terms of the D-optimality criterion and prediction accuracy. Finally, it is shown that best-worst choice experiments yield considerably more information than choice experiments.

Suggested Citation

  • Vermeulen, Bart & Goos, Peter & Vandebroek, Martina, 2010. "Obtaining more information from conjoint experiments by best-worst choices," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1426-1433, June.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1426-1433
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    References listed on IDEAS

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    1. Barbara Baarsma, 2003. "The Valuation of the IJmeer Nature Reserve using Conjoint Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 25(3), pages 343-356, July.
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    5. Kevin J. Boyle & Thomas P. Holmes & Mario F. Teisl & Brian Roe, 2001. "A Comparison of Conjoint Analysis Response Formats," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(2), pages 441-454.
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    Cited by:

    1. David A. Hensher & Chinh Ho, 2016. "Identifying a behaviourally relevant choice set from stated choice data," Transportation, Springer, vol. 43(2), pages 197-217, March.
    2. Denise Doiron & Jane Hall & Patricia Kenny & Deborah J. Street, 2014. "Job preferences of students and new graduates in nursing," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 924-939, March.
    3. de Palma, André & Kilani, Karim, 2017. "Identities for maximum, minimum, and maxmin random utility models," Economics Letters, Elsevier, vol. 155(C), pages 135-139.
    4. Balcombe, Paul & Rigby, Dan & Azapagic, Adisa, 2014. "Investigating the importance of motivations and barriers related to microgeneration uptake in the UK," Applied Energy, Elsevier, vol. 130(C), pages 403-418.
    5. Crabbe, M. & Vandebroek, M., 2012. "Improving the efficiency of individualized designs for the mixed logit choice model by including covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2059-2072.
    6. Lancsar, Emily & Louviere, Jordan & Donaldson, Cam & Currie, Gillian & Burgess, Leonie, 2013. "Best worst discrete choice experiments in health: Methods and an application," Social Science & Medicine, Elsevier, vol. 76(C), pages 74-82.
    7. Phillips, Yvonne, 2011. "When the Tide is High: Estimating the Welfare Impact of Coastal Erosion Management," 2011 Conference, August 25-26, 2011, Nelson, New Zealand 115414, New Zealand Agricultural and Resource Economics Society.

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