Abstract
Discrete choice experiments are a popular method to measure part worths of economic goods and in health science. These models include several attributes as explanatory variables. The commonly used multinomial logit model assumes independent utilities for different choice options. In Graßhoff et al. [Optimal design for discrete choice experiments. J Statist Plann Inference. 2013;143:167–175] we pointed out that for such a model designs turn out to be formally optimal which may comprise choice sets containing identical or nearly identical options and which are not reasonable for use in empirical discrete choice studies. To overcome this problem we introduce a novel model based on probit part-worth utilities which can account for similarities in the alternatives by supposing a dependence structure. For this model we derive locally D-optimal designs which appear to be more reasonable for applications.
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