Fractional factorial and D-optimal design for discrete choice experiments (DCE)

Abstract

Discrete choice experiments (DCE) are a method to measure people’s preferences in various fields of application that used to estimate the importance of attributes of the product or service based on the respondent’s choice. The design of DCE consists of several choices called profiles in each choice set. The best DCE’s design depends on how to divide the profiles into each choice set so it needs a strategy to construct the appropriate DCE’s design. So, the purpose of our study is looking for the most appropriate strategy from two strategies used namely fractional factorial design as the first strategy and D-optimal design with a point-exchange algorithm as the second strategy. The quality of the two strategies is evaluated with the D-optimality criterion value. Both of them are simulated in the case that involves four attributes with two levels, respectively, so all possible profiles are 24 = 16 profiles. The first strategy gained DCE’s design that consisted of two choice sets and eight profiles, respectively. The second strategy gained two different DCE’s designs. The first design consisted of four choice sets and four profiles, respectively. The second design consisted of eight choice sets and two profiles, respectively. Both the first and second strategies have the same D-optimality criterion values but a difference in structure. In terms of statistics, both strategies have the same quality. However, in terms of application, the second strategy is more appropriate in the structure than the first strategy. Hence, the D-optimal design is an alternative to construct DCE’s design based on applications.