Models and optimal designs for conjoint choice experiments including a no-choice option |
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Authors: | Bart Vermeulen Peter Goos Martina Vandebroek |
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Affiliation: | aFaculty of Business and Economics, Katholieke Universiteit Leuven, Belgium;bFaculty of Applied Economics, Universiteit Antwerpen, Belgium;cFaculty of Business and Economics, University Center for Statistics, Katholieke Universiteit Leuven, Belgium |
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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. |
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Keywords: | Bayesian optimal design Choice-based conjoint Conjoint analysis D-optimality Model-robust design Multinomial logit model Nested logit |
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