This paper examines the statistical properties of uniform design for stated choice modeling. Estimation efficiency, prediction efficiency and test power (i.e., the ability to pick up significant effects or exclude insignificant effects) are selected as measures of statistical properties. Both uniform design and orthogonal design are used to generate profiles. The major experimental design strategies for multinomial logit k models including shifted pairs, L 2 J, 2 block, all pairs and McFadden’s sampling rule, are used to construct choice sets from the profiles generated by both orthogonal design and uniform design. Monte Carlo experiments are used to generate models, whose parameters vary in scale. The results show that the performance of uniform design in stated choice modeling is comparable to that of orthogonal design. 1.

This paper aims to generalize and unify classical criteria for comparisons of balanced lattice designs, including fractional factorial designs, supersaturated designs and uniform designs. We present a general majorization framework for assessing designs, which includes a stringent criterion of majorization via pairwise coincidences and flexible surrogates via convex functions. Classical orthogonality, aberration and uniformity criteria are unified by choosing combinatorial and exponential kernels. A construction method is also sketched out.