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Response willingness in consecutive travel surveys: an investigation based on the National Household Travel Survey using a sample selection model

Author

Listed:
  • Xinyi Wang

    (Georgia Institute of Technology)

  • F. Atiyya Shaw

    (University of Michigan, Ann Arbor)

  • Patricia L. Mokhtarian

    (Georgia Institute of Technology)

  • Kari E. Watkins

    (University of California, Davis)

Abstract

Declining survey response rates have increased the costs of travel survey recruitment. Recruiting respondents based on their expressed willingness to participate in future surveys, obtained from a preceding survey, is a potential solution but may exacerbate sample biases. In this study, we analyze the self-selection biases of survey respondents recruited from the 2017 U.S. National Household Travel Survey (NHTS), who had agreed to be contacted again for follow-up surveys. We apply a probit with sample selection (PSS) model to analyze (1) respondents’ willingness to participate in a follow-up survey (the selection model) and (2) their actual response behavior once contacted (the outcome model). Results verify the existence of self-selection biases, which are related to survey burden, sociodemographic characteristics, travel behavior, and item non-response to sensitive variables. We find that age, homeownership, and medical conditions have opposing effects on respondents’ willingness to participate and their actual survey participation. The PSS model is then validated using a hold-out sample and applied to the NHTS samples from various geographic regions to predict follow-up survey participation. Effect size indicators for differences between predicted and actual (population) distributions of select sociodemographic and travel-related variables suggest that the resulting samples may be most biased along age and education dimensions. Further, we summarized six model performance measures based on the PSS model structure. Overall, this study provides insight into self-selection biases in respondents recruited from preceding travel surveys. Model results can help researchers better understand and address such biases, while the nuanced application of various model measures lays a foundation for appropriate comparison across sample selection models.

Suggested Citation

  • Xinyi Wang & F. Atiyya Shaw & Patricia L. Mokhtarian & Kari E. Watkins, 2023. "Response willingness in consecutive travel surveys: an investigation based on the National Household Travel Survey using a sample selection model," Transportation, Springer, vol. 50(6), pages 2339-2373, December.
  • Handle: RePEc:kap:transp:v:50:y:2023:i:6:d:10.1007_s11116-022-10312-w
    DOI: 10.1007/s11116-022-10312-w
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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. James Heckman & Justin L. Tobias & Edward Vytlacil, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Southern Economic Journal, John Wiley & Sons, vol. 68(2), pages 210-223, October.
    3. Circella, Giovanni & Tiedeman, Kate & Handy, Susan & Alemi, Farzad & Mokhtarian, Patricia, 2016. "What Affects Millennials’ Mobility? Part I: Investigating the Environmental Concerns, Lifestyles, Mobility-Related Attitudes and Adoption of Technology of Young Adults in California," Institute of Transportation Studies, Working Paper Series qt6wm51523, Institute of Transportation Studies, UC Davis.
    4. Couper, Mick P. & Kapteyn, Arie & Schonlau, Matthias & Winter, Joachim, 2007. "Noncoverage and nonresponse in an Internet survey," Munich Reprints in Economics 20093, University of Munich, Department of Economics.
    5. James Heckman & Justin L. Tobias & Edward Vytlacil, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Southern Economic Journal, John Wiley & Sons, vol. 68(2), pages 210-223, October.
    6. Van de Ven, Wynand P. M. M. & Van Praag, Bernard M. S., 1981. "The demand for deductibles in private health insurance : A probit model with sample selection," Journal of Econometrics, Elsevier, vol. 17(2), pages 229-252, November.
    7. Xinyu (Jason) Cao, 2009. "Disentangling the influence of neighborhood type and self-selection on driving behavior: an application of sample selection model," Transportation, Springer, vol. 36(2), pages 207-222, March.
    8. Andrew Collins & John Rose & Stephane Hess, 2012. "Interactive stated choice surveys: a study of air travel behaviour," Transportation, Springer, vol. 39(1), pages 55-79, January.
    9. Parady, Giancarlos & Ory, David & Walker, Joan, 2021. "The overreliance on statistical goodness-of-fit and under-reliance on model validation in discrete choice models: A review of validation practices in the transportation academic literature," Journal of choice modelling, Elsevier, vol. 38(C).
    10. Sun, Hao & Wang, Hai & Wan, Zhixi, 2019. "Model and analysis of labor supply for ride-sharing platforms in the presence of sample self-selection and endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 76-93.
    11. Shaw, F. Atiyya & Wang, Xinyi & Mokhtarian, Patricia L. & Watkins, Kari E., 2021. "Supplementing transportation data sources with targeted marketing data: Applications, integration, and internal validation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 150-169.
    12. V. Kerry Smith & Kelli L. Larson & Abigail York, 2020. "Using quality signaling to enhance survey response rates," Applied Economics Letters, Taylor & Francis Journals, vol. 27(11), pages 951-954, June.
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