IDEAS home Printed from https://ideas.repec.org/p/ags/aare11/100585.html

Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a probabilistic decision process model

Author

Listed:
  • McNair, Ben J.
  • Heshner, David A.
  • Bennett, Jeffrey W.

Abstract

There is a growing body of evidence in the non-market valuation literature suggesting that responses to a sequence of discrete choice questions tend to violate the assumptions typically made by analysts regarding independence of responses and stability of preferences. Decision processes (or heuristics) such as value learning and strategic misrepresentation have been offered as explanations for these results. While a few studies have tested these heuristics as competing hypotheses, none has investigated the possibility that each explains the response behaviour of a subgroup of the population. In this paper, we make a contribution towards addressing this research gap by presenting a probabilistic decision process model designed to estimate the proportion of respondents employing defined heuristics. We demonstrate the model on binary and multinomial choice data sources and find three distinct types of response behaviour. The results suggest that accounting for heterogeneity in response behaviour may be a better way forward than attempting to identify a single heuristic to explain the behaviour of all respondents.

Suggested Citation

  • McNair, Ben J. & Heshner, David A. & Bennett, Jeffrey W., 2011. "Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a probabilistic decision process model," 2011 Conference (55th), February 8-11, 2011, Melbourne, Australia 100585, Australian Agricultural and Resource Economics Society.
    • Handle: RePEc:ags:aare11:100585
    DOI: 10.22004/ag.econ.100585
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/100585/files/McNair%20B.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.100585?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Petrolia, Daniel & Interis, Matthew & Hwang, Joonghyun, 2015. "Single-Choice, Repeated-Choice, and Best-Worst Elicitation Formats: Do Results Differ and by How Much?," Working Papers 212479, Mississippi State University, Department of Agricultural Economics.
    2. Boxebeld, Sander, 2024. "Ordering effects in discrete choice experiments: A systematic literature review across domains," Journal of choice modelling, Elsevier, vol. 51(C).
    3. Mandy Ryan & Nicolas Krucien & Frouke Hermens, 2018. "The eyes have it: Using eye tracking to inform information processing strategies in multi‐attributes choices," Health Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 709-721, April.
    4. Gonzalez-Valdes, Felipe & Raveau, Sebastián, 2018. "Identifying the presence of heterogeneous discrete choice heuristics at an individual level," Journal of choice modelling, Elsevier, vol. 28(C), pages 28-40.
    5. Hensher, David A. & Balbontin, Camila & Collins, Andrew T., 2018. "Heterogeneity in decision processes: Embedding extremeness aversion, risk attitude and perceptual conditioning in multiple process rules choice making," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 316-325.
    6. David Hensher & Andrew Collins & William Greene, 2013. "Accounting for attribute non-attendance and common-metric aggregation in a probabilistic decision process mixed multinomial logit model: a warning on potential confounding," Transportation, Springer, vol. 40(5), pages 1003-1020, September.
    7. Thiene, Mara & Meyerhoff, Jürgen & De Salvo, Maria, 2012. "Scale and taste heterogeneity for forest biodiversity: Models of serial nonparticipation and their effects," Journal of Forest Economics, Elsevier, vol. 18(4), pages 355-369.
    8. Balbontin, Camila & Hensher, David A. & Collins, Andrew T., 2017. "Is there a systematic relationship between random parameters and process heuristics?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 160-177.
    9. Wiktor L. Adamowicz & Klaus Glenk & Jürgen Meyerhoff, 2014. "Choice modelling research in environmental and resource economics," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 27, pages 661-674, Edward Elgar Publishing.
    10. Kazagli, Evanthia & de Lapparent, Matthieu, 2023. "A discrete choice modeling framework of heterogenous decision rules accounting for non-trading behavior," Journal of choice modelling, Elsevier, vol. 48(C).
    11. Hancock, Thomas O. & Hess, Stephane & Marley, A.A.J. & Choudhury, Charisma F., 2021. "An accumulation of preference: Two alternative dynamic models for understanding transport choices," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 250-282.
    12. Leong, Waiyan & Hensher, David A., 2012. "Embedding multiple heuristics into choice models: An exploratory analysis," Journal of choice modelling, Elsevier, vol. 5(3), pages 131-144.
    13. David Hensher, 2014. "Attribute processing as a behavioural strategy in choice making," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 12, pages 268-289, Edward Elgar Publishing.
    14. Anna Bartczak & Jürgen Meyerhoff, 2012. "Valuing the chances of survival of two distinct Eurasian lynx populations in Poland – do people want to keep doors open?," Working Papers 2012-14, Faculty of Economic Sciences, University of Warsaw.
    15. Balbontin, Camila & Hensher, David A. & Collins, Andrew T., 2019. "How to better represent preferences in choice models: The contributions to preference heterogeneity attributable to the presence of process heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 218-248.
    16. Balbontin, Camila & Hensher, David A. & Collins, Andrew T., 2017. "Integrating attribute non-attendance and value learning with risk attitudes and perceptual conditioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 172-191.
    17. Daniel R. Petrolia & Matthew G. Interis & Joonghyun Hwang, 2018. "Single-Choice, Repeated-Choice, and Best-Worst Scaling Elicitation Formats: Do Results Differ and by How Much?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 365-393, February.
    18. Campbell, Danny & Boeri, Marco & Doherty, Edel & George Hutchinson, W., 2015. "Learning, fatigue and preference formation in discrete choice experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 345-363.
    19. Gonzalez-Valdes, Felipe & Heydecker, Benjamin G. & Ortúzar, Juan de Dios, 2022. "Quantifying behavioural difference in latent class models to assess empirical identifiability: Analytical development and application to multiple heuristics," Journal of choice modelling, Elsevier, vol. 43(C).

    More about this item

    Keywords

    ;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ags:aare11:100585. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaresea.html .

    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.