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Using an integrated choice and latent variable model to understand the impact of “professional” respondents in a stated preference survey

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  • Sandorf, Erlend Dancke
  • Persson, Lars
  • Broberg, Thomas

Abstract

Internet panels are increasingly used for stated preference research. Because members of such panels receive compensation for each completed survey, one concern is that over time this creates professional respondents who answer surveys solely for the monetary compensation. We identify professional respondents using data on panel tenure, survey response frequency, completion rate and total number of completed surveys. We find evidence of two types of professional respondents: “hyperactives” who answer surveys frequently and “experienced” who have long panel tenure and a large number of completed surveys. Using an integrated choice and latent variable model on stated preference survey data, we find that “hyperactive” respondents are less likely to choose the 'status quo’ and have a more stochastic choice process as seen from the econometrician's point of view, whereas “experienced” respondents have a relatively more deterministic choice process. Our results show that “hyperactive” respondents significantly impact estimated values.

Suggested Citation

  • Sandorf, Erlend Dancke & Persson, Lars & Broberg, Thomas, 2020. "Using an integrated choice and latent variable model to understand the impact of “professional” respondents in a stated preference survey," Resource and Energy Economics, Elsevier, vol. 61(C).
    • Handle: RePEc:eee:resene:v:61:y:2020:i:c:s0928765518304548
    DOI: 10.1016/j.reseneeco.2020.101178
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    References listed on IDEAS

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    1. Jürgen Meyerhoff & Ulf Liebe, 2009. "Status Quo Effect in Choice Experiments: Empirical Evidence on Attitudes and Choice Task Complexity," Land Economics, University of Wisconsin Press, vol. 85(3), pages 515-528.
    2. Hess, Stephane & Train, Kenneth, 2017. "Correlation and scale in mixed logit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 1-8.
    3. Ulf Liebe & Jürgen Meyerhoff & Volkmar Hartje, 2012. "Test–Retest Reliability of Choice Experiments in Environmental Valuation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 53(3), pages 389-407, November.
    4. Ole Bonnichsen & S�ren B�ye Olsen, 2016. "Correcting for non-response bias in contingent valuation surveys concerning environmental non-market goods: an empirical investigation using an online panel," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 59(2), pages 245-262, February.
    5. Danny Campbell & Morten Raun Mørkbak & Søren Bøye Olsen, 2017. "Response time in online stated choice experiments: the non-triviality of identifying fast and slow respondents," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 6(1), pages 17-35, January.
    6. Stephane Hess & Nesha Beharry-Borg, 2012. "Accounting for Latent Attitudes in Willingness-to-Pay Studies: The Case of Coastal Water Quality Improvements in Tobago," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 52(1), pages 109-131, May.
    7. Toepoel, V. & Das, J.W.M. & van Soest, A.H.O., 2008. "Design Effects in Web Surveys : Comparing Trained and Fresh Respondents," Other publications TiSEM 4c8b40aa-7557-4844-9474-b, Tilburg University, School of Economics and Management.
    8. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
    9. Gregory L. Poe & Kelly L. Giraud & John B. Loomis, 2005. "Computational Methods for Measuring the Difference of Empirical Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 353-365.
    10. Lindhjem, Henrik & Navrud, Ståle, 2011. "Using Internet in Stated Preference Surveys: A Review and Comparison of Survey Modes," International Review of Environmental and Resource Economics, now publishers, vol. 5(4), pages 309-351, September.
    11. Czajkowski, Mikołaj & Vossler, Christian A. & Budziński, Wiktor & Wiśniewska, Aleksandra & Zawojska, Ewa, 2017. "Addressing empirical challenges related to the incentive compatibility of stated preferences methods," Journal of Economic Behavior & Organization, Elsevier, vol. 142(C), pages 47-63.
    12. Zhifeng Gao & Lisa A. House & Jing Xie, 2016. "Online Survey Data Quality and Its Implication for Willingness-to-Pay: A Cross-Country Comparison," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 64(2), pages 199-221, June.
    13. Dekker, Thijs & Hess, Stephane & Brouwer, Roy & Hofkes, Marjan, 2016. "Decision uncertainty in multi-attribute stated preference studies," Resource and Energy Economics, Elsevier, vol. 43(C), pages 57-73.
    14. Hess, Stephane & Stathopoulos, Amanda, 2013. "Linking response quality to survey engagement: A combined random scale and latent variable approach," Journal of choice modelling, Elsevier, vol. 7(C), pages 1-12.
    15. Fosgerau, Mogens & Bjørner, Thomas Bue, 2006. "Joint models for noise annoyance and willingness to pay for road noise reduction," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 164-178, February.
    16. 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.
    17. Stephane Hess & John Rose, 2012. "Can scale and coefficient heterogeneity be separated in random coefficients models?," Transportation, Springer, vol. 39(6), pages 1225-1239, November.
    18. Andrew Daly & Stephane Hess & Bhanu Patruni & Dimitris Potoglou & Charlene Rohr, 2012. "Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour," Transportation, Springer, vol. 39(2), pages 267-297, March.
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    Cited by:

    1. Broberg, Thomas & Daniel, Aemiro Melkamu & Persson, Lars, 2021. "Household preferences for load restrictions: Is there an effect of pro-environmental framing?," Energy Economics, Elsevier, vol. 97(C).
    2. Seojeong Oh & Benjamin M. Gramig, 2023. "Valuing Ecosystem Services and Downstream Water Quality Improvement in the U.S. Corn Belt," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 85(3), pages 823-872, August.
    3. Erlend Dancke Sandorf & Kristine Grimsrud & Henrik Lindhjem, 2022. "Ponderous, Proficient or Professional? Survey Experience and Smartphone Effects in Stated Preference Research," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 81(4), pages 807-832, April.
    4. Kaitlynn Sandstrom‐Mistry & Frank Lupi & Hyunjung Kim & Joseph A. Herriges, 2023. "Comparing water quality valuation across probability and non‐probability samples," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(2), pages 744-761, June.

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    More about this item

    Keywords

    Professional respondents; Internet panels; Discrete choice experiments; Integrated choice and latent variable model;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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