Comparing the State-of-the-Art Efficient Stated Choice Designs Based on Empirical Analysis

The stated choice (SC) experiment has been generally regarded as an effective method for behavior analysis. Among all the SC experimental design methods, the orthogonal design has been most widely used since it is easy to understand and construct. However, in recent years, a stream of research has put emphasis on the so-called efficient experimental designs rather than keeping the orthogonality of the experiment, as the former is capable of producing more efficient data in the sense that more reliable parameter estimates can be achieved with an equal or lower sample size. This paper provides two state-of-the-art methods called optimal orthogonal choice (OOC) and -efficient design. More statistically efficient data is expected to be obtained by either maximizing attribute level differences, or minimizing the -error, a statistic corresponding to the asymptotic variance-covariance (AVC) matrix of the discrete choice model, when using these two methods, respectively. Since comparison and validation in the field of these methods are rarely seen, an empirical study is presented. -error is chosen as the measure of efficiency. The result shows that both OOC and -efficient design are more efficient. At last, strength and weakness of orthogonal, OOC, and -efficient design are summarized.