Efficient conjoint choice designs in the presence of respondent heterogeneity.

The authors propose a fast and efficient algorithm for constructing D-optimal conjoint choice designs for mixed logit models in the presence of respondent heterogeneity. With this new algorithm, the construction of semi-Bayesian D-optimal mixed logit designs with large numbers of attributes and attribute levels becomes practically feasible. The results from the comparison of eight designs (ranging from the simple locally D-optimal design for the multinomial logit model and the nearly orthogonal design generated by Sawtooth (CBC) to the complex semi-Bayesian mixed logit design) across wide ranges of parameter values show that the semi-Bayesian mixed logit approach outperforms the competing designs not only in terms of estimation efficiency but also in terms of prediction accuracy. In particular, it was found that semi-Bayesian mixed logit designs constructed with large heterogeneity parameters are most robust against the misspecification of the values for the mean of the individual level coefficients for making precise estimations and predictions.Keywords:semi-Bayesianmixedlogitdesign,heterogeneity,predictionaccuracy,multinomiallogitdesign,model-robustdesign,D-optimality,algorithmAlgorithm; D-Optimality; Heterogeneity; Model-robust design; Multinomial logit design; Prediction accuracy; Semi-Bayesian mixed logit design;