Reconsidering optimal experimental design for conjoint analysis
The quality of Conjoint Analysis estimations heavily depends on the alternatives presented in the experiment. An efficient selection of the experiment design matrix allows more information to be elicited about consumer preferences from a small number of questions, thus reducing experimental cost and respondent's fatigue. The statistical literature considers optimal design algorithms (Kiefer, 1959), and typically selects the same combination of stimuli more than once. However in the context of conjoint analysis, replications do not make sense for individual respondents. In this paper we present a general approach to compute optimal designs for conjoint experiments in a variety of scenarios and methodologies: continuous, discrete and mixed attributes types, customer panels with random effects, and quantile regression models. We do not compute good designs, but the best ones according to the size (determinant or trace) of the information matrix of the associated estimators without repeating profiles as in Kiefer's methodology. We handle efficient optimization algorithms to achieve our goal, avoiding the use of widespread ad-hoc intuitive rules.
|Date of creation:||Nov 2012|
|Contact details of provider:|| Web page: http://www.business.uc3m.es/es/index|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Currim, Imran S & Weinberg, Charles B & Wittink, Dick R, 1981. " Design of Subscription Programs for a Performing Arts Series," Journal of Consumer Research, Oxford University Press, vol. 8(1), pages 67-75, June.
- Green, Paul E, 1974. " On the Design of Choice Experiments Involving Multifactor Alternatives," Journal of Consumer Research, Oxford University Press, vol. 1(2), pages 61-68, Se.
- Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
- Wittink, Dick R & Krishnamurthi, Lakshman & Nutter, Julia B, 1982. " Comparing Derived Importance Weights Across Attributes," Journal of Consumer Research, Oxford University Press, vol. 8(4), pages 471-474, March.
When requesting a correction, please mention this item's handle: RePEc:cte:wbrepe:wb121405. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ana Poveda)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.