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Joint analysis of preferences and drop out data in discrete choice experiments

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  • Maaya, Leonard
  • Meulders, Michel
  • Vandebroek, Martina

Abstract

Choice data appear together with drop out data indicating if respondents completed the exercise. In case of non-completion, the choice sequences end at the tasks where the respondents exited the study. In the analysis of choice data, the focus is always on choices made while the drop out behavior is completely ignored. However, the choice making and the drop out process could be latently related. For instance, respondents who are more likely to drop out of the exercise could give less consistent choices throughout or just before they exit. In such cases, ignoring the drop out dimension could lead to biased or inefficient results. In this paper, we use shared random effects and covariate effects to model the association between a scaled multinomial logit model for the choices and two different models for the drop out component. Through simulations, we show that a joint model provides less biased and more precise estimates and its 95% credible intervals have better coverage for true parameter values.

Suggested Citation

  • Maaya, Leonard & Meulders, Michel & Vandebroek, Martina, 2021. "Joint analysis of preferences and drop out data in discrete choice experiments," Journal of choice modelling, Elsevier, vol. 41(C).
    • Handle: RePEc:eee:eejocm:v:41:y:2021:i:c:s1755534521000415
    DOI: 10.1016/j.jocm.2021.100308
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    References listed on IDEAS

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    1. Hess, Stephane & Train, Kenneth, 2017. "Correlation and scale in mixed logit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 1-8.
    2. Sanko, Nobuhiro & Hess, Stephane & Dumont, Jeffrey & Daly, Andrew, 2014. "Contrasting imputation with a latent variable approach to dealing with missing income in choice models," Journal of choice modelling, Elsevier, vol. 12(C), pages 47-57.
    3. Irannezhad, Elnaz & Prato, Carlo & Hickman, Mark, 2019. "A joint hybrid model of the choices of container terminals and of dwell time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 119-133.
    4. Mikolaj Czajkowski & Marek Giergiczny & William H. Greene, 2014. "Learning and Fatigue Effects Revisited: Investigating the Effects of Accounting for Unobservable Preference and Scale Heterogeneity," Land Economics, University of Wisconsin Press, vol. 90(2), pages 324-351.
    5. Varotto, Silvia F. & Glerum, Aurélie & Stathopoulos, Amanda & Bierlaire, Michel & Longo, Giovanni, 2017. "Mitigating the impact of errors in travel time reporting on mode choice modelling," Journal of Transport Geography, Elsevier, vol. 62(C), pages 236-246.
    6. Lang Wu & Wei Liu & Grace Y. Yi & Yangxin Huang, 2012. "Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues," Journal of Probability and Statistics, Hindawi, vol. 2012, pages 1-17, December.
    7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    8. Vishva Danthurebandara & Jie Yu & Martina Vandebroek, 2015. "Designing choice experiments by optimizing the complexity level to individual abilities," Quantitative Marketing and Economics (QME), Springer, vol. 13(1), pages 1-26, March.
    9. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    10. Sarrias, Mauricio & Daziano, Ricardo, 2017. "Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i02).
    11. 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.
    12. Hess, Stephane & Hensher, David A. & Daly, Andrew, 2012. "Not bored yet – Revisiting respondent fatigue in stated choice experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 626-644.
    13. Yi Qian & Hui Xie, 2011. "No Customer Left Behind: A Distribution-Free Bayesian Approach to Accounting for Missing Xs in Marketing Models," Marketing Science, INFORMS, vol. 30(4), pages 717-736, July.
    14. John Whitehead, 1980. "Fitting Cox's Regression Model to Survival Data Using Glim," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(3), pages 268-275, November.
    15. Mariel, Petr & Artabe, Alaitz, 2020. "Interpreting correlated random parameters in choice experiments," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
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