IDEAS home Printed from https://ideas.repec.org/h/elg/eechap/14820_7.html
   My bibliography  Save this book chapter

Stated choice experimental design theory: the who, the what and the why

In: Handbook of Choice Modelling

Author

Listed:
  • John M. Rose
  • Michiel C.J. Bliemer

Abstract

Choice modelling is an increasingly important technique for forecasting and valuation, with applications in fields such as transportation, health and environmental economics. For this reason it has attracted attention from leading academics and practitioners and methods have advanced substantially in recent years. This Handbook, composed of contributions from senior figures in the field, summarises the essential analytical techniques and discusses the key current research issues. It will be of interest to academics, students and practitioners in a wide range of areas.

Suggested Citation

  • John M. Rose & Michiel C.J. Bliemer, 2014. "Stated choice experimental design theory: the who, the what and the why," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 7, pages 152-177, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:14820_7
    as

    Download full text from publisher

    File URL: https://www.elgaronline.com/view/9781781003145.00013.xml
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeff Bennett (ed.), 2011. "The International Handbook on Non-Market Environmental Valuation," Books, Edward Elgar Publishing, number 13490.
    2. Scarpa, Riccardo & Rose, John M., 2008. "Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 1-30.
    3. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304.
    4. Jie Yu & Peter Goos & Martina Vandebroek, 2009. "Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity," Marketing Science, INFORMS, vol. 28(1), pages 122-135, 01-02.
    5. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    6. Kessels, Roselinde & Jones, Bradley & Goos, Peter & Vandebroek, Martina, 2009. "An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 279-291.
    7. Frederick Mosteller & Philip Nogee, 1951. "An Experimental Measurement of Utility," Journal of Political Economy, University of Chicago Press, vol. 59, pages 371-371.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    9. Bliemer, Michiel C.J. & Rose, John M., 2011. "Experimental design influences on stated choice outputs: An empirical study in air travel choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(1), pages 63-79, January.
    10. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2010. "Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1268-1289, December.
    11. Jordan J. Louviere & Towhidul Islam & Nada Wasi & Deborah Street & Leonie Burgess, 2008. "Designing Discrete Choice Experiments: Do Optimal Designs Come at a Price?," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(2), pages 360-375, March.
    12. Kessels, Roselinde & Goos, Peter & Vandebroek, Martina, 2008. "Optimal designs for conjoint experiments," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2369-2387, January.
    13. Louviere, Jordan J & Hensher, David A, 1983. "Using Discrete Choice Models with Experimental Design Data to Forecast Consumer Demand for a Unique Cultural Event," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 10(3), pages 348-361, December.
    14. Jayson L. Lusk & F. Bailey Norwood, 2005. "Effect of Experimental Design on Choice-Based Conjoint Valuation Estimates," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(3), pages 771-785.
    15. Fredrik Carlsson & Peter Martinsson, 2003. "Design techniques for stated preference methods in health economics," Health Economics, John Wiley & Sons, Ltd., vol. 12(4), pages 281-294, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chorus, Caspar G., 2015. "Models of moral decision making: Literature review and research agenda for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 16(C), pages 69-85.
    2. Lusk, Jayson L. & Tonsor, Glynn T. & Schroeder, Ted C. & Hayes, Dermot J., 2018. "Effect of government quality grade labels on consumer demand for pork chops in the short and long run," Food Policy, Elsevier, vol. 77(C), pages 91-102.
    3. Marcucci, Edoardo & Gatta, Valerio & Le Pira, Michela, 2018. "Gamification design to foster stakeholder engagement and behavior change: An application to urban freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 119-132.
    4. Xu, Chenchen & Luo, Yiyang & Fuellhart, Kurt & Shao, Quan & Witlox, Frank, 2023. "Modeling exit choice behavior in airplane emergency evacuations," Journal of Air Transport Management, Elsevier, vol. 112(C).
    5. Großmann, Heiko, 2019. "A practical approach to designing partial-profile choice experiments with two alternatives for estimating main effects and interactions of many two-level attributes," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    6. Solvi Hoen, Fredrik & Díez-Gutiérrez, María & Babri, Sahar & Hess, Stephane & Tørset, Trude, 2023. "Charging electric vehicles on long trips and the willingness to pay to reduce waiting for charging. Stated preference survey in Norway," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    7. Lehmann, Nico & Sloot, Daniel & Ardone, Armin & Fichtner, Wolf, 2022. "Consumer preferences for the design of a demand response quota scheme – Results of a choice experiment in Germany," Energy Policy, Elsevier, vol. 167(C).
    8. Anciaes, Paulo & Metcalfe, Paul & Heywood, Chris & Sheldon, Rob, 2019. "The impact of fare complexity on rail demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 224-238.
    9. Fosgerau, Mogens & Börjesson, Maria, 2015. "Manipulating a stated choice experiment," Journal of choice modelling, Elsevier, vol. 16(C), pages 43-49.
    10. Mokas, Ilias & Lizin, Sebastien & Brijs, Tom & Witters, Nele & Malina, Robert, 2021. "Can immersive virtual reality increase respondents’ certainty in discrete choice experiments? A comparison with traditional presentation formats," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    11. Hess, Stephane & Lancsar, Emily & Mariel, Petr & Meyerhoff, Jürgen & Song, Fangqing & van den Broek-Altenburg, Eline & Alaba, Olufunke A. & Amaris, Gloria & Arellana, Julián & Basso, Leonardo J. & Ben, 2022. "The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents," Social Science & Medicine, Elsevier, vol. 298(C).
    12. Pérez-Troncoso, Daniel, 2022. "Optimal sequential strategy to improve the precision of the estimators in a discrete choice experiment: A simulation study," Journal of choice modelling, Elsevier, vol. 43(C).
    13. Guevara, C. Angelo & Hess, Stephane, 2019. "A control-function approach to correct for endogeneity in discrete choice models estimated on SP-off-RP data and contrasts with an earlier FIML approach by Train & Wilson," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 224-239.
    14. Hess, Stephane & Daly, Andrew & Dekker, Thijs & Cabral, Manuel Ojeda & Batley, Richard, 2017. "A framework for capturing heterogeneity, heteroskedasticity, non-linearity, reference dependence and design artefacts in value of time research," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 126-149.
    15. Lehmann, Nico & Sloot, Daniel & Ardone, Armin & Fichtner, Wolf, 2021. "The limited potential of regional electricity marketing – Results from two discrete choice experiments in Germany," Energy Economics, Elsevier, vol. 100(C).
    16. Kemperman, Astrid, 2021. "A review of research into discrete choice experiments in tourism: Launching the Annals of Tourism Research Curated Collection on Discrete Choice Experiments in Tourism," Annals of Tourism Research, Elsevier, vol. 87(C).
    17. Hess, Stephane & Spitz, Greg & Bradley, Mark & Coogan, Matt, 2018. "Analysis of mode choice for intercity travel: Application of a hybrid choice model to two distinct US corridors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 547-567.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Richard Yao & Riccardo Scarpa & John Rose & James Turner, 2015. "Experimental Design Criteria and Their Behavioural Efficiency: An Evaluation in the Field," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(3), pages 433-455, November.
    2. Palhazi Cuervo, Daniel & Kessels, Roselinde & Goos, Peter & Sörensen, Kenneth, 2016. "An integrated algorithm for the optimal design of stated choice experiments with partial profiles," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 648-669.
    3. van Cranenburgh, Sander & Rose, John M. & Chorus, Caspar G., 2018. "On the robustness of efficient experimental designs towards the underlying decision rule," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 50-64.
    4. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2010. "Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1268-1289, December.
    5. John Rose & Michiel Bliemer, 2013. "Sample size requirements for stated choice experiments," Transportation, Springer, vol. 40(5), pages 1021-1041, September.
    6. Robert J. Johnston & Kevin J. Boyle & Wiktor (Vic) Adamowicz & Jeff Bennett & Roy Brouwer & Trudy Ann Cameron & W. Michael Hanemann & Nick Hanley & Mandy Ryan & Riccardo Scarpa & Roger Tourangeau & Ch, 2017. "Contemporary Guidance for Stated Preference Studies," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(2), pages 319-405.
    7. KESSELS, Roselinde & BRADLEY, Jones & GOOS, Peter, 2012. "A comparison of partial profile designs for discrete choice experiments with an application in software development," Working Papers 2012004, University of Antwerp, Faculty of Business and Economics.
    8. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2011. "Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 378-388.
    9. Elizabeth Kinter & Thomas Prior & Christopher Carswell & John Bridges, 2012. "A Comparison of Two Experimental Design Approaches in Applying Conjoint Analysis in Patient-Centered Outcomes Research," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 5(4), pages 279-294, December.
    10. Crabbe, M. & Vandebroek, M., 2012. "Improving the efficiency of individualized designs for the mixed logit choice model by including covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2059-2072.
    11. Bart Vermeulen & Peter Goos & Riccardo Scarpa & Martina Vandebroek, 2011. "Bayesian Conjoint Choice Designs for Measuring Willingness to Pay," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(1), pages 129-149, January.
    12. Ferrini, Silvia & Scarpa, Riccardo, 2007. "Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study," Journal of Environmental Economics and Management, Elsevier, vol. 53(3), pages 342-363, May.
    13. Frischknecht, Bart D. & Eckert, Christine & Geweke, John & Louviere, Jordan J., 2014. "A simple method for estimating preference parameters for individuals," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 35-48.
    14. Silvia Ferrini & Riccardo Scarpa, 2005. "Experimental Designs for Environmental Valuation with Choice-Experiments: A Monte-Carlo Investigation," Working Papers in Economics 05/08, University of Waikato.
    15. J. DeShazo & Trudy Cameron & Manrique Saenz, 2009. "The Effect of Consumers’ Real-World Choice Sets on Inferences from Stated Preference Surveys," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 42(3), pages 319-343, March.
    16. Andreas Falke & Harald Hruschka, 2017. "A Monte Carlo study of design-generating algorithms for the latent class mixed logit model," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 1035-1053, October.
    17. Ndebele, Tom & Marsh, Dan, 2014. "Environmental attitude and the demand for green electricity in the context of supplier choice: A case study of the New Zealand retail electricity market," 2014 Conference, August 28-29, 2014, Nelson, New Zealand 188376, New Zealand Agricultural and Resource Economics Society.
    18. Bliemer, Michiel C.J. & Rose, John M., 2011. "Experimental design influences on stated choice outputs: An empirical study in air travel choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(1), pages 63-79, January.
    19. Catalina M. Torres Figuerola & Nick Hanley & Sergio Colombo, 2011. "Incorrectly accounting for taste heterogeneity in choice experiments: Does it really matter for welfare measurement?," CRE Working Papers (Documents de treball del CRE) 2011/1, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    20. Torres, Cati & Hanley, Nick & Riera, Antoni, 2011. "How wrong can you be? Implications of incorrect utility function specification for welfare measurement in choice experiments," Journal of Environmental Economics and Management, Elsevier, vol. 62(1), pages 111-121, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:elg:eechap:14820_7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Darrel McCalla (email available below). General contact details of provider: http://www.e-elgar.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.