Models and optimal designs for conjoint choice experiments including a no-choice option
Peter Goos () and
International Journal of Research in Marketing, 2008, vol. 25, issue 2, 94-103
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.
Keywords: Bayesian optimal design; Choice-based conjoint; Conjoint analysis; D-optimality; Model-robust design; Multinomial logit model; Nested logit (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijrema:v:25:y:2008:i:2:p:94-103
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