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The Dependent Poisson Race Model and Modeling Dependence in Conjoint Choice Experiments

Published online by Cambridge University Press:  01 January 2025

Shiling Ruan,
Steven N. MacEachern,
Thomas Otter and
Angela M. Dean
Shiling Ruan
Affiliation:
The Ohio State University
Steven N. MacEachern*
Affiliation:
The Ohio State University
Thomas Otter
Affiliation:
The Ohio State University
Angela M. Dean
Affiliation:
The Ohio State University
*
Requests for reprints should be sent to Steven N. MacEachern, Department of Statistics, The Ohio State University, Cockins Hall, Room 404, Columbus, OH 43210-1247, USA. E-mail: snm@stat.ohio-state.edu
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Abstract

Conjoint choice experiments are used widely in marketing to study consumer preferences amongst alternative products. We develop a class of choice models, belonging to the class of Poisson race models, that describe a ‘random utility’ which lends itself to a process-based description of choice. The models incorporate a dependence structure which captures the relationship between the attributes of the choice alternatives and which appropriately moderates the randomness inherent in the race. The new models are applied to conjoint choice data and are shown to have performance markedly superior to that of independent Poisson race models and of the multinomial logit model.

Keywords

Bayesian modeldependent processesdominancemultinomial logit modelPoisson process

Information

Type
Theory and Methods
Information
Psychometrika , Volume 73 , Issue 2 , June 2008, pp. 261 - 288
Copyright
Copyright © 2007 The Psychometric Society

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Footnotes

This research was funded by the NSF under grant numbers SES-0437251 and DMS-0605041 and by the NSA under award number H98230-05-1-0065. The views expressed in this paper are those of the authors and are not necessarily those of the supporting institutions.

The authors would like to thank Trish Van Zandt, the Editor, Associate Editor, and Referees for their insightful comments.

Electronic Supplementary Material The online version of this article (http://dx.doi.org/10.1007/s11336-007-9035-y) contains supplementary material, which is available to authorized users.

References

Bettman, J.R., Luce, M.F., & Payne, J.W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25, 187–217.CrossRefGoogle Scholar
Boeckenholt, U., & Dillon, W.R. (1997). Modeling within subject dependencies in ordinal paired comparison data. Psychometrika, 62, 411–434.CrossRefGoogle Scholar
Bouyssou, D., & Pirlotm, M. (2004). Preferences for multi-attributed alternatives: Traces, dominance, and numerical representations. Journal of Mathematical Psychology, 48, 167–185.CrossRefGoogle Scholar
Bradlow, E.T., Hu, Y., & Ho, T.-H. (2004). A learning-based model for imputing missing levels in partial conjoint profiles (with discussion). Journal of Marketing Research, 41, 369–391.CrossRefGoogle Scholar
Busemeyer, J.R., & Wang, Y.-M. (2000). Model comparisons and model selections based on generalization criterion methodology. Journal of Mathematical Psychology, 44, 171–189.CrossRefGoogle ScholarPubMed
Chen, M.H., Shao, Q., & Ibrahim, J.G. (2000). Monte Carlo methods in Bayesian computation, New York: Springer.CrossRefGoogle Scholar
DeSarbo, W., Fong, D.K.H., Liechty, J., & Coupland, J.C. (2005). Evolutionary preference/utility functions: A dynamic perspective. Psychometrika, 70, 179–202.CrossRefGoogle Scholar
Dittrich, R., Hatzinger, R., & Katzenbeisser, W. (2002). Modeling dependencies in paired comparison data: A log-linear approach. Computational Statistics & Data Analysis, 40, 39–57.CrossRefGoogle Scholar
Fienberg, S.E., & Larntz, K. (1976). Log linear representation for paired and multiple comparisons models. Biometrika, 63, 245–254.CrossRefGoogle Scholar
French, S. (1988). Decision theory: An introduction to the mathematics of rationality, London: Ellis Horwood.Google Scholar
Huber, J., Sewall, M.A. (1979). Covariance bias of Thurstone case V scaling as applied to consumer preferences and purchase intentions. In Wilkie, W. (Eds.), Advances in consumer research (pp. 578–581). Urbana: Association for Consumer Research.Google Scholar
Karlin, S., & Taylor, H.M. (1975). A first course in stochastic processes, (2nd ed.). New York: Academic Press.Google Scholar
Kuhfeld, W.F. (2005). Marketing research methods in SAS (SAS 9.1 Edition). TS722, Cary: SAS Institute Inc.Google Scholar
LaBerge, D.A. (1994). Quantitative models of attention and response processes in shape identification tasks. Journal of Mathematical Psychology, 38, 198–243.CrossRefGoogle Scholar
Liechty, J.C., Fong, D.K.H., & DeSarbo, W. (2005). Dynamic models incorporating individual heterogeneity: Utility evolution in conjoint analysis. Marketing Science, 24, 285–293.CrossRefGoogle Scholar
Louviere, J.J., Hensher, D.A., & Swait, J. (2000). Stated choice methods: Analysis and application, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Luce, R.D. (1977). Thurstone’s discriminal process fifty years later. Psychometrika, 42, 461–489.CrossRefGoogle Scholar
Marley, A.A.J., & Colonius, H. (1992). The ‘horse-race–random utility model for choice probabilities and reaction times, and its competing risks interpretation. Journal of Mathematical Psychology, 36, 1–20.CrossRefGoogle Scholar
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In Zarembka, P. (Eds.), Frontiers in econometrics (pp. 105–142). New York: Academic Press.Google Scholar
O’Hagan, A. (1994). Kendall’s advanced theory of statistics, (6th ed.). London: Arnold.Google Scholar
Park, Y.-H., & Bradlow, E.T. (2005). An integrated model for whether, who, when and how much in internet auctions. Journal of Marketing Research, 42, 470–482.CrossRefGoogle Scholar
Ruan, S. (2007). Poisson race models for conjoint choice analysis: Theory and applications. Unpublished PhD dissertation. Department of Statistics, The Ohio State University.Google Scholar
Rummelhart, D.L., & Greeno, J.G. (1971). Similarity between stimuli: An experimental test of the Luce and Restle choice models. Journal of Mathematical Psychology, 8, 370–381.CrossRefGoogle Scholar
Townsend, J.T., & Ashby, F.G. (1983). The stochastic modeling of elementary psychological processes, Cambridge: Cambridge University Press.Google Scholar
Van Zandt, T., Colonius, H., & Proctor, R.W. (2000). A comparison of two responses models applied to perceptual matching. Psychonomic Bulletin and Review, 27, 208–256.CrossRefGoogle Scholar
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