📝 SummarXiv

Abstract

Discrete Choice Experiments (DCEs) are widely used to elicit preferences for products or services by analyzing choices among alternatives described by their attributes. The quality of the insights obtained from a DCE heavily depends on the properties of its experimental design. While early DCEs often relied on linear criteria such as orthogonality, these approaches were later found to be inappropriate for discrete choice models, which are inherently non-linear. As a result, statistically efficient design methods, based on minimizing the D-error to reduce parameter variance, have become the standard. Although such methods are implemented in several commercial tools, researchers seeking free and accessible solutions often face limitations. This paper presents DCEtool, an R package with a Shiny-based graphical interface designed to support both novice and experienced users in constructing, decoding, and analyzing statistically efficient DCE designs. DCEtool facilitates the implementation of serial DCEs, offers flexible design settings, and enables rapid estimation of discrete choice models. By making advanced design techniques more accessible, DCEtool contributes to the broader adoption of rigorous experimental practices in choice modelling.