Optimal sequential strategy to improve the precision of the estimators in a discrete choice experiment: A simulation study

Abstract

Introduction: In order to solve the problems related to prior parameter misspecification in DCEs, Bliemer and Rose (2010) proposed a sequential approach where the design is updated after each respondent. This paper tries to find a more efficient alternative sequential method since the original proposal could be very time-consuming and expensive under some circumstances. Methods: 11 different strategies were simulated using 8 to 16 choice sets following a Monte Carlo approach. The accuracy and bias of the estimates of each strategy were studied using the relative error and mean value of their estimates. Results: The DCE performs similarly to the original strategy by updating the design after five respondents. Among the other strategies, we discovered that, under certain circumstances, updating the design after 20 or 10 respondents led to accurate and not significantly biased estimates. Conclusions: For a strategy to be efficient it might not be necessary to update the DCE after each respondent, but we found that updating the prior information relatively often and regularly can be almost as efficient as the original sequential proposal (for example, updating after five respondents might be a good choice). In addition, our findings suggest that each DCE has different efficient strategies depending on the number of attributes, levels, sets, and alternatives, so it can be concluded that a universal “optimal sequential strategy” does not exist.