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Models and optimal designs for conjoint choice experiments including a no-choice option

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

Abstract

In a classical conjoint choice experiment, respondents choose one profile from each choice set that has to be evaluated. However, in real life, the respondent does not always make a choice: often he/she does not prefer any of the options offered. Therefore, including a no-choice option in a choice set makes a conjoint choice experiment more realistic. In the literature, three different models are used to analyze the results of a conjoint choice experiment with a no-choice option: the no-choice multinomial logit model, the extended no-choice multinomial logit model, and the nested no-choice multinomial logit model. We develop optimal designs for the two most appealing of these models using the D-optimality criterion and the modified Fedorov algorithm and compare these optimal designs with a reference design, which is constructed while ignoring the no-choice option, in terms of estimation and prediction accuracy. We conclude that taking into account the no-choice option when designing a no-choice experiment only has a marginal effect on the estimation and prediction accuracy as long as the model used for estimation matches the data-generating model.

Suggested Citation

  • Vermeulen, Bart & Goos, Peter & Vandebroek, Martina, 2008. "Models and optimal designs for conjoint choice experiments including a no-choice option," International Journal of Research in Marketing, Elsevier, vol. 25(2), pages 94-103.
    • Handle: RePEc:eee:ijrema:v:25:y:2008:i:2:p:94-103
    DOI: 10.1016/j.ijresmar.2007.12.004
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    References listed on IDEAS

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    1. Dhar, Ravi, 1997. "Consumer Preference for a No-Choice Option," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(2), pages 215-231, September.
    2. Arora, Neeraj & Huber, Joel, 2001. "Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 273-283, September.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    4. GOOS, Peter & VERMEULEN, Bart & VANDEBROEK, Martina, 2008. "D-optimal conjoint choice designs with no-choice options for a nested logit model," Working Papers 2008020, University of Antwerp, Faculty of Business and Economics.
    5. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
    6. Baron, Jonathan & Ritov, Ilana, 1994. "Reference Points and Omission Bias," Organizational Behavior and Human Decision Processes, Elsevier, vol. 59(3), pages 475-498, September.
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