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Accounting for attribute non-attendance and common-metric aggregation in a probabilistic decision process mixed multinomial logit model: a warning on potential confounding

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  • David Hensher
  • Andrew Collins
  • William Greene

Abstract

Latent class models offer an alternative perspective to the popular mixed logit form, replacing the continuous distribution with a discrete distribution in which preference heterogeneity is captured by membership of distinct classes of utility description. Within each class, preference homogeneity is usually assumed, although interactions with observed contextual effects are permissible. A natural extension of the fixed parameter latent class model is a random parameter latent class model which allows for another layer of preference heterogeneity within each class. A further extension is to overlay attribute processing rules such as attribute non-attendance (ANA) and aggregation of common-metric attributes (ACMA). This paper sets out the random parameter latent class model with ANA and ACMA, and illustrates its application using a stated choice data set in the context of car commuters and non-commuters choosing amongst alternative packages of travel times and costs pivoted around a recent trip in Australia. What we find is that for the particular data set analysed, in the presence of attribute processing together with the discrete distributions defined by latent classes, that adding an additional layer of heterogeneity through random parameters within a latent class only very marginally improves on the statistical contribution of the model. Nearly all of the additional fit over the fixed parameter latent class model is added by the account for attribute processing. This is an important finding that might suggest the role that attribute processing rules play in accommodating attribute heterogeneity, and that random parameters within class are essentially a potentially confounding effect. An interesting finding, however, is that the introduction of random parameters increases the probability of membership to full attribute attendance classes, which may suggest that some individuals assign a very low marginal disutility (but not zero) to specific attributes or that there are very small differences in the marginal disutility of common-metric attributes, and this is being accommodated by random parameters, but not observed under a fixed parameter latent class model. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • 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.
  • Handle: RePEc:kap:transp:v:40:y:2013:i:5:p:1003-1020
    DOI: 10.1007/s11116-012-9447-0
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    1. 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.
    2. Danny Campbell & W. Hutchinson & Riccardo Scarpa, 2008. "Incorporating Discontinuous Preferences into the Analysis of Discrete Choice Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 41(3), pages 401-417, November.
    3. Hole, Arne Risa, 2011. "A discrete choice model with endogenous attribute attendance," Economics Letters, Elsevier, vol. 110(3), pages 203-205, March.
    4. Fredrik Carlsson & Mitesh Kataria & Elina Lampi, 2010. "Dealing with Ignored Attributes in Choice Experiments on Valuation of Sweden’s Environmental Quality Objectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(1), pages 65-89, September.
    5. Rose, John M. & Bliemer, Michiel C.J. & Hensher, David A. & Collins, Andrew T., 2008. "Designing efficient stated choice experiments in the presence of reference alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 395-406, May.
    6. Stephane Hess & Amanda Stathopoulos & Andrew Daly, 2012. "Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies," Transportation, Springer, vol. 39(3), pages 565-591, May.
    7. William H. Greene & David A. Hensher, 2013. "Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1897-1902, May.
    8. Hess, Stephane & Hensher, David A., 2010. "Using conditioning on observed choices to retrieve individual-specific attribute processing strategies," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 781-790, July.
    9. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
    10. Ben McNair & David Hensher & Jeff Bennett, 2012. "Modelling Heterogeneity in Response Behaviour Towards a Sequence of Discrete Choice Questions: A Probabilistic Decision Process Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 51(4), pages 599-616, April.
    11. Danny Campbell & David A. Hensher & Riccardo Scarpa, 2011. "Non-attendance to attributes in environmental choice analysis: a latent class specification," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 54(8), pages 1061-1076, December.
    12. Stephane Hess & Amanda Stathopoulos & Danny Campbell & Vikki O’Neill & Sebastian Caussade, 2013. "It’s not that I don’t care, I just don’t care very much: confounding between attribute non-attendance and taste heterogeneity," Transportation, Springer, vol. 40(3), pages 583-607, May.
    13. Everitt, B. S., 1988. "A finite mixture model for the clustering of mixed-mode data," Statistics & Probability Letters, Elsevier, vol. 6(5), pages 305-309, April.
    14. Hensher, David A. & Rose, John M., 2009. "Simplifying choice through attribute preservation or non-attendance: Implications for willingness to pay," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(4), pages 583-590, July.
    15. David A. Hensher, 2006. "How do respondents process stated choice experiments? Attribute consideration under varying information load," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 861-878.
    16. Angel Bujosa & Antoni Riera & Robert Hicks, 2010. "Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(4), pages 477-493, December.
    17. Riccardo Scarpa & Timothy J. Gilbride & Danny Campbell & David A. Hensher, 2009. "Modelling attribute non-attendance in choice experiments for rural landscape valuation," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 36(2), pages 151-174, June.
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    5. Rakotonarivo, O. Sarobidy & Bredahl Jacobsen, Jette & Poudyal, Mahesh & Rasoamanana, Alexandra & Hockley, Neal, 2018. "Estimating welfare impacts where property rights are contested: methodological and policy implications," Land Use Policy, Elsevier, vol. 70(C), pages 71-83.
    6. Yegoryan, Narine & Guhl, Daniel & Klapper, Daniel, 2018. "Inferring Attribute Non-Attendance Using Eye Tracking in Choice-Based Conjoint Analysis," Rationality and Competition Discussion Paper Series 111, CRC TRR 190 Rationality and Competition.
    7. Gonçalves, Tânia & Pinto, Lígia M. Costa & Lourenço-Gomes, Lina, 2020. "Attribute non-attendance in wine choice: Contrasts between stated and inferred approaches," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 262-275.
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    9. Collins, Andrew T. & Rose, John M. & Hensher, David A., 2013. "Specification issues in a generalised random parameters attribute nonattendance model," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 234-253.
    10. 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.
    11. Hole, Arne Risa & Kolstad, Julie Riise & Gyrd-Hansen, Dorte, 2013. "Inferred vs. stated attribute non-attendance in choice experiments: A study of doctors’ prescription behaviour," Journal of Economic Behavior & Organization, Elsevier, vol. 96(C), pages 21-31.
    12. Owusu, V., 2018. "Credit-Constraints and Preferences for Crop Insurance in Ghana: Implications of Attribute Non-Attendance in Discrete Choice Experiments," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276967, International Association of Agricultural Economists.
    13. 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.
    14. Yegoryan, Narine & Guhl, Daniel & Klapper, Daniel, 2020. "Inferring attribute non-attendance using eye tracking in choice-based conjoint analysis," Journal of Business Research, Elsevier, vol. 111(C), pages 290-304.
    15. Chen, Xuqi & Shen, Meng & Gao, Zhifeng, 2017. "Impact of Intra-respondent Variations in Attribute Attendance on Consumer Preference in Food Choice," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258509, Agricultural and Applied Economics Association.
    16. Campbell, Danny & Hensher, David A. & Scarpa, Riccardo, 2014. "Bounding WTP distributions to reflect the ‘actual’ consideration set," Journal of choice modelling, Elsevier, vol. 11(C), pages 4-15.
    17. 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.
    18. 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.
    19. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "Heterogeneous variable selection in nonlinear panel data models: A semiparametric Bayesian approach," Tinbergen Institute Discussion Papers 20-061/III, Tinbergen Institute.
    20. 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.
    21. Weller, Priska & Oehlmann, Malte & Mariel, Petr & Meyerhoff, Jürgen, 2014. "Stated and inferred attribute non-attendance in a design of designs approach," Journal of choice modelling, Elsevier, vol. 11(C), pages 43-56.

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