A comparison of algorithms for generating efficient choice experiments

Abstract

Stated choice (SC) studies typically rely on the use of an underlying experimental design to construct the hypothetical choice situations shown to respondents. These designs are constructed by the analyst, with several different ways of constructing these designs having been proposed in the past. Recently, there has been a move from so-called orthogonal designs to more efficient designs. Efficient designs optimize the design such that the data will lead to more reliable parameter estimates for the model under consideration. The literature dealing with the generation of efficient designs has examined and largely solved the issue of a requirement for a prior knowledge of the parameter estimates that will be obtained post data collection. However, unlike orthogonal designs, the efficient design methodology requires the evaluation of a number of designs, and hence is computationally expensive to undertake. As such, the literature has suggested and implemented a number of algorithms to locate efficient designs for SC experiments. In this paper, we compare and contrast the performance of these algorithms as well as introduce two new algorithms.