Constructing Efficient Stated Choice Experimental Designs
Abstract
Stated choice (SC) experiments are often used in transportation studies for estimating and forecasting behaviour of travellers, road authorities, etc. This kind of experiment relies on underlying experimental designs. Whilst orthogonal designs are mainstream for practitioners, many researchers now realize that so‐called efficient designs are able to produce more efficient data in the sense that more reliable parameter estimates can be achieved with an equal or lower sample size. This paper describes several processes for generating SC experiments and is intended to give an overview of the current state‐of‐the‐art. Different methods are described.
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The screening alternatives were allocated into choice sets using a multinomial logit d-efficient design with zero priors, optimising the likelihood of estimating statistically efficient preference parameters despite the relatively small sample size and inclusion of opt-out alternatives.
“The screening alternatives described by the attributes and levels in Table 1 were allocated into choice sets using a multinomial logit (mnl) d-efficient design, assuming no prior information about the likely magnitude or direction of coefficients (zero priors) [29,30].”
From Consumer preferences for teledermoscopy screening to detect melanoma early (2016) · cites this paper as [30]
A D-efficient design will outperform an orthogonal design, and given reliable priors, a Dp-efficient design will outperform a Dz-efficient design.
“Hence, a D-efficient design will outperform an orthogonal design, and (given reliable priors), a Dp-efficient design will outperform a Dz-efficient design [21].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [21]
When parameter priors are different from zero, the efficient design generated produces smaller prediction errors than orthogonal designs.
“It is noteworthy that when the parameter priors are different from zero, the efficient design generated produces smaller prediction errors than orthogonal designs [21,49,50].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [21]
The most widely used efficiency measure is the D-error, which refers to the efficiency of the experimental design in extracting information from respondents.
“The most widely used efficiency measure is the D-error, which may be easily estimated using various software packages, such as Ngene, and refers to the efficiency of the experimental design in extracting information from respondents [21].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [21]
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- Authors
- Rose, J. M., & Bliemer, M. C. J.
- Affiliations
- The University of Sydney (AU)
- Delft University of Technology (NL)
- Corresponding author
- Rose, John M.
- Venue
- Transport Reviews
- Publisher
- Informa UK Limited
- Published
- 2009-09
- Volume
- 29
- Issue
- 5
- Pages
- 587-617
- ISSN
- 0144-1647 (ISSN-L)
- 1464-5327
- OpenAlex
- W2093584567
- Cited by
- 797
- References
- 42
- Citations by year
- 2026: 87 · 2025: 81 · 2024: 80 · 2023: 66 · 2022: 60 · 2021: 68 · 2020: 53 · 2019: 36 · 2018: 49 · 2017: 43 · 2016: 42 · 2015: 34 · 2014: 27 · 2013: 26 · 2012: 15
- FWCI
- 32.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transport Reviews (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- stated choice experiment, orthogonal experimental design, estimator efficiency, sample size reduction, travel behavior
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