The DOI matches its Crossref record; 41 of the 42 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
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Train, K. E. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511753930 · Cited by 3,973
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(1969) · Transportation Research · doi:10.1016/0041-1647(69)90163-4 · Cited by 1,578
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Huber, J. C., & Zwerina, K. (1996) · Journal of Marketing Research · doi:10.1177/002224379603300305 · Cited by 1,023
(1955) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.1955.tb38334.x · Cited by 891
Research works printed but not linked: 7
- Bliemer, M. C. J. and Rose, J. M. Designing stated choice experiments: state‐of‐the‐art. Paper presented at the 11th international conference on Travel Behaviour Research. August2006, Kyoto, Japan.
- Bliemer M. C. J., Transportation Research Part B
- Cochran W. G., 1957, Experimental Designs
- Fowkes A. S., 2000, Stated Preference Modelling Techniques, 37
- Rose, J. M. and Bliemer, M. C. J. Designing efficient data for stated choice: accounting for socio‐demographic and contextual effects in designing stated choice experiments. Paper presented at the 11th international conference on Travel Behaviour Research. August2006, Kyoto, Japan.
- Rose J. M., 2008, Handbook of Transport Modelling, Ch 8, 151
- Watson S. M., 2000, Stated Preference Modelling Techniques, 91
Cited by
Sorted by citations
Contemporary Guidance for Stated Preference Studies
Johnston, R. J., Boyle, K., Adamowicz, W., Bennett, J., Brouwer, R., Cameron, T. A., Hanemann, W. M., Hanley, N. D., Ryan, M., Scarpa, R., et al. (2017) · Journal of the Association of Environmental and Resource Economists · doi:10.1086/691697 · Cited by 1,566
Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide
de Bekker-Grob, E. W., Donkers, B., Jonker, M. F., & Stolk, E. A. (2015) · Patient · doi:10.1007/s40271-015-0118-z · Cited by 987
Best-Worst Scaling: Theory, Methods and Applications
Louviere, J. J., Flynn, T. N., & Marley, A. A. J. (2015) · Thammasat University Digital Collections · Cited by 434
Experimental design influences on stated choice outputs: An empirical study in air travel choice
Bliemer, M. C. J., & Rose, J. M. (2010) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2010.09.003 · Cited by 258
Self-citationHow might potential future plug-in electric vehicle buyers differ from current “Pioneer” owners?
Axsen, J., Goldberg, S., & Bailey, J. (2016) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2016.05.015 · Cited by 250
Metadata
- 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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