Stated choice methods have been widely used in transportation studies since 1980s. In recent years, much research attention has been paid to develop optimal or efficient designs for choice experiments, such as the so-called D-optimal design, which does not seek for orthogonality as the traditional approach does but aims at minimizing the determinant of the variance-covariance matrix of the parameter estimators. This paper examines the statistical properties of an alternative design method-uniform design, which also does not look for orthogonality but aims at maximizing uniformity-a measure that is closely related to model efficiency. We compare the estimation efficiency and prediction efficiency of uniform design with that of the traditional fractional factorial orthogonal design in stated choice modelling. Monte Carlo experiments are used to generate models, whose parameters vary in scale. The results show that though uniform design uses a lot fewer profiles than orthogonal designs do, its prediction and estimation efficiencies in stated choice modelling are comparable to that of orthogonal design.
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