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One of the major objectives in conjoint analysis has been to predict the choices made by a sample of individuals for a new item which is described in terms of a set of attributes used in a conjoint study. Ratings-based conjoint studies involve the conversion of an individual’s stated utility for an item to predict the probability of choice of an alternative under various conditions (e.g. when other alternatives available). As described in Chap. 3, such a prediction is made using preference data (ratings or rankings) collected on a set of hypothetical choice alternatives. A parallel stream of research pursues the path of choice experiments in which an individual makes a choice among a set of choice alternatives, each of which is typically described by a set of attributes; several choice sets are presented to each individual. These choice data, across all the choice sets and all individuals, are then analyzed using a choice model (usually a multinomial logit model and sometimes multinomial probit model) to obtain a function that relates the attribute levels to probability of choice. This approach has come to be known as choice-based conjoint analysis and has its roots in discrete choice analysis; these methods are also called “stated” choice methods (or stated choice experimental methods) because they represent intended choices of respondents among hypothetical choice possibilities. This chapter describes these methods.
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Allenby, G. M., Arora, N., & Ginter, J. L. (1998). On the heterogeneity of demand. Journal of Marketing Research, 35(August), 384–389. CrossRef
Allenby, G. M., & Lenk, P. J. (1994). Modeling household purchase behavior with logistic regression. Journal of the American Statistical Association, 89(December), 1218–1231. CrossRef
Arora, N., & Huber, J. (2001). Improving parameter estimates and model prediction by aggregate customization in choice experiments. Journal of Consumer Research, 28, 273–283. CrossRef
Batsell, R. R., & Louviere, J. (1991). Experimental analysis of choice. Marketing Letters, 2(3), 199–214. CrossRef
Becker, G. M., DeGroot, M. H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9(July), 226–232. CrossRef
Ben-Akiva, M., & Lerman, S. R. (1991). Discrete choice analysis. Cambridge, MA: The MIT Press.
Bradlow, E. T., & Rao, V. R. (2000). A hierarchical model for assortment choice. Journal of Marketing Research, 37(May), 259–264. CrossRef
Bradlow, E. T., Hu, Y., & Ho, T.-H. (2004). A learning-based model for imputing missing levels in partial conjoint profiles. Journal of Marketing Research, 41(November), 369–381. CrossRef
Burgess, L., & Street, D. J. (2003). Optimal designs for 2 k choice experiments. Communications in Statistics: Theory and Methods, 32(11), 2185–2206. CrossRef
Burgess, L., & Street, D. J. (2005). Optimal designs for choice experiments with asymmetric attributes. Journal of Statistical Planning and Inference, 134, 288–301. CrossRef
Chakraborty, G., Woodworth, G., & Gaeth, G. J. (1991). Screening for interactions between design factors and demographics in choice-based conjoint. Journal of Business Research, 23, 219–237. CrossRef
Chung, J., & Rao, V. R. (2003). A general choice model for bundles with multiple-category products: Application to market segmentation and optimal pricing for bundles. Journal of Marketing Research, 40(May), 115–130. CrossRef
Ding, M. (2007). An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research, 44(May), 214–223. CrossRef
Ding, M., Agarwal, R., & Liechty, J. (2005). Incentive-aligned conjoint analysis. Journal of Marketing Research, 42(February), 67–82. CrossRef
Elrod, T., Louviere, J. J., & Davey, K. S. (1992). An empirical comparison of ratings-based and choice-based conjoint models. Journal of Marketing Research, 29(August), 368–377. CrossRef
Erdem, T., & Swait, J. (1998). Brand equity as a signaling phenomenon. Journal of Consumer Psychology, 7(2), 131–157. CrossRef
Fiebig, D., Keane, M. P., Louviere, J., & Wasi, N. (2010). The generalized multinomial logit model: Accounting for scale and coefficient heterogeneity. Marketing Science, 29(3 (May-June)), 393–421. CrossRef
Greene, W. H. (2012). Econometric analysis (7th ed.). Boston: Prentice-Hall.
Hahn, G.J., & Shapiro, S.S. (1966). A catalog and computer program for the design and analysis of orthogonal and asymmetric fractional factorial experiments. No. 66- C- 165. Schenectady, NY: Research and Development Center.
Huber, J., & Train, K. (2001). On the similarity of classical and Bayesian estimates of individual mean partworths. Marketing Letters, 12(3), 259–269. CrossRef
Huber, J., & Zwerina, K. (1996). The importance of utility balance in efficient choice designs. Journal of Marketing Research, 33(August), 307–17. CrossRef
Khuri, A. I., Mukherjee, B., Sinha, B. K., & Ghose, M. (2006). Design issues for generalized linear models: A review. Statistical Science, 21(3), 3760399. CrossRef
Krieger, A., & Green, P. E. (1991). Designing pareto optimal stimuli for multiattribute choice experiments. Marketing Letters, 2(4), 337–348. CrossRef
Kuhfeld, W. F. (2000). Experimental design, efficiency, coding, and choice designs. Chapter (TS-722C). http://support.sas.com/techsup/tnote_stat.html#market
Kuhfeld, W. F., Tobias, R. D., & Garratt, M. (1994). Efficient experimental designs with marketing research applications. Journal of Marketing Research, 31, 545–557. CrossRef
Lazari, A. G., & Anderson, D. A. (1994). Designs of discrete choice set experiments for estimating both attribute and availability cross effects. Journal of Marketing Research, 31(August), 375–383. CrossRef
Louviere, J. J. (1984). Using discrete choice experiments and multinomial logit choice models to forecast trial in a competitive retail environment in a differentiated market with price competition. Journal of Retailing, 60(4), 81–107.
Louviere, J. J. (1988). Modeling individual decisions: Metric conjoint analysis, theory, methods and applications (Sage University Press series). Newbury Park: Sage.
Louviere, J. J. (1991). Experimental choice analysis: Introduction and overview. Journal of Business Research, 23, 291–297. CrossRef
Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated choice or allocated experiments: An approach based on aggregated data. Journal of Marketing Research, 20(November), 350–467. CrossRef
Louviere, J. J., Hensher, D. A., & Swait, J. D. (2001). Stated choice models: Analysis and application. Cambridge, UK: Cambridge University Press.
Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge, MA: Cambridge University Press. CrossRef
McFadden, D. (1986). The choice theory approach to market research. Marketing Science, 5(4), 275–297. CrossRef
Meyer, R., & Johnson, E. J. (1995). Empirical generalizations in the modeling of consumer choice. Marketing Science, 14(3), G180–G189. Part 2. CrossRef
Orme, B. (1999). CBC user manual, Version 2.0. Sequim, WA: Sawtooth Software.
Sandor, Z., & Wedel, M. (2001). Designing conjoint choice experiments using managers’ prior beliefs. Journal of Marketing Research, 38(November), 430–444. CrossRef
SPSS (1997 and 2012). http://www-01.ibm.com/software/analysis/spss/
Street, D. J., & Burgess, L. (2007). The construction of optimal stated choice experiments: Theory and methods. Hoboken, NJ: Wiley. CrossRef
Toubia, O., & Hauser, J. R. (2007). On managerial efficiency for experimental designs. Marketing Science, 26(6), 851–858. CrossRef
Toubia, O., Hauser, J. R., & Simester, D. (2004). Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41(February), 116–131. CrossRef
Toubia, O., Hauser, J. R., & Garcia, R. (2007). Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Science, 26(5 (September-October)), 596–610. CrossRef
Wertenbroch, K., & Skiera, B. (2002). Measuring consuemrs’ willingness to pay at the point of purchase. Journal of Marketing Research, 39(May), 228–241. CrossRef
- Choice Based Conjoint Studies: Design and Analysis
Vithala R. Rao
- Springer Berlin Heidelberg
- Chapter 4