arXiv 18 Sep 2025 · Econometrics
arXiv:2509.15326 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bliemer MC, Rose JM (2010) Serial choice conjoint analysis for estimating discrete choice models | 0.737 | 3 | 2 | 100% |
| 2 | Pérez-Troncoso D (2022) Optimal sequential strategy to improve the precision of the estimators in a discrete choice experiment: A simulation study | 0.644 | 2 | 2 | 100% |
| 3 | Bunch DS, Louviere JJ, Anderson D (1996) A comparison of experimental design strategies for multinomial logit models: The case of generic attributes | 0.405 | 1 | 1 | 100% |
| 4 | Hole AR (2015) DCREATE: Stata module to create efficient designs for discrete choice experiments [Internet] | 0.405 | 1 | 1 | 100% |
| 5 | Johnson FR, Lancsar E, Marshall D, Kilambi V, Mühlbacher A, Regier D… (2013) Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design goo… | 0.405 | 1 | 1 | 100% |
| 6 | Kessels R, Jones B, Goos P, Vandebroek M (2011) The usefulness of Bayesian optimal designs for discrete choice experiments | 0.405 | 1 | 1 | 100% |
| 7 | ChoiceMetrics (2018) Ngene 1.2 User Manual & Reference Guide | 0.405 | 1 | 1 | 100% |
| 8 | Posit Team (2025) RStudio: Integrated Development Environment for R | 0.405 | 1 | 1 | 100% |
| 9 | R Core Team (2025) R: A Language and Environment for Statistical Computing | 0.405 | 1 | 1 | 100% |
| 10 | Rose JM, Bliemer MC (2014) Stated choice experimental design theory: the who, the what and the why | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 12 scored citations.