Using partial profile choice experiments to handle large numbers of attributes

International Journal of Market Research

Volumn 52, Issue 6, 2010, Pages 8-

  Chrzan, Keith ^a  

^a Maritz Research   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84861417711     PISSN: 14707853     EISSN: None     Source Type: Journal    
DOI: 10.2501/s1470785310201673     Document Type: Article

References (21)

1. Allenby, G.M. & Ginter, J.L. (1995) Using extremes to design products and segment markets. Journal of Marketing Research, 32, pp. 392-403.
2. Ben-Akiva, M. & Lerman, S. (1985) Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, MA: MIT Press.
3. Bradlow, E.T., Hu, Y. & Ho, T.-H. (2004) A learning-based model for imputing missing levels in partial conjoint models. Journal of Marketing Research, 41, pp. 369-381.
4. Chrzan, K. & Elrod, T. (1994) Choice models with many attributes: partial profile choice experiments. Paper presented at the TIMS Marketing Science Conference, Tucson, AZ.
5. Chrzan, K. & Elrod, T. (1995) Partial profile choice experiments: a choice-based approach for handling large numbers of attributes. Paper presented at the American Marketing Association's Advanced Research Techniques Forum, Monterey, CA.
6. Chrzan, K. & Patterson, M. (1999) Comparing the ability of full and partial profile choice experiments to predict holdout choices. Paper presented at the American Marketing Association's Advanced Research Techniques Forum, Santa Fe, NM.
7. Chrzan, K., Bunch, D. & Lockhart, D.C. (1996) Testing a multinomial extension of partial profile choice experiments: empirical comparisons to full profile experiments. Paper presented at the 1996 INFORMS Marketing Science Conference, Gainesville, FL.
8. DeShazo, J.R. & Fermo, G. (1999) Designing choice sets for stated preference methods: the effects of complexity on choice consistency. Journal of Environmental Economics and Management, 38, pp. 1-21.
9. Green, P.E. & Srinivasan, V. (1978) Conjoint analysis in consumer research: issues and outlook. Journal of Consumer Research, 5, pp. 103-123.
10. Green, P.E. & Srinivasan, V. (1990) Conjoint analysis in marketing research: new developments and directions. Journal of Marketing, 54 (October) pp. 3-19.
11. Haaijer, M.E., Wedel, M. & Kamakura, W.A. (2001) The 'no choice' alternative in conjoint choice experiments. International Journal of Market Research, 43, pp. 93-106.
12. Kuhfeld, W. (2009) Marketing Research Methods in SAS: Experimental Design, Choice, Conjoint, and Graphical Techniques. Cary, NC: SAS Institute.
13. Louviere, J.J. & Woodworth, G. (1983) Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data. Journal of Marketing Research, 20, pp. 350-367.
14. McFadden, D. (1974) Conditional logit analysis of qualitative choice behavior. In: P. Zarembka (ed.) Frontiers in Econometrics. New York: Academic Press, pp. 105-142.
15. Patterson, M. & Chrzan, K. (2003) Partial profile discrete choice: what's the optimal number of attributes? Proceedings of the Sawtooth Software Conference. Sequim, WA: Sawtooth Software, pp. 173-185.
16. Sawtooth Software, Inc. (2007) The CBC/HB Module, Version 4.0. Sequim, WA: Sawtooth Software.
17. Sawtooth Software, Inc. (2009) MaxDiff Designer. Sequim, WA: Sawtooth Software.
18. Severin, V. (2000) Comparing statistical efficiency and respondent efficiency in choice experiments. Unpublished PhD thesis, Faculty of Economics and Business, University of Sydney, Australia.
19. Street, D.J. & Burgess, L. (2007) The Construction of Optimal Stated Choice Experiments. Hoboken, NJ: Wiley.
20. Swait, J. & Louviere, J. (1993) The role of the scale parameter in the estimation and comparison of multinomial logit models. Journal of Marketing Research, 30, pp. 305-314.
21. Wittink, D.R. & Cattin, P. (1989) Commercial use of conjoint analysis: an update. Journal of Marketing, 53, pp. 91-96.