38 Pages Posted: 18 Nov 2006
Date Written: April 2007
Research in marketing, and business in general, involves understanding when effect-sizes are expected to be large and when they are expected to be small. Understanding the contexts in which consumers are sensitive to offers, and variables such as price, is an important aspect of merchandising, selling and promotion. In this paper, we propose efficient methods of learning about contextual factors that influence consumer preference and sensitivities within the context of a hierarchical Bayes model. A design criterion is developed for hierarchical linear models, and validated in a study of the level effect in conjoint analysis using a national sample of respondents. Extensions to other model structures are discussed.
Keywords: Hierarchcial Bayes, Level Effect, Linear Model
JEL Classification: C11, C42, C91, M30
Suggested Citation: Suggested Citation
Liu, Qing and Dean, Angela and Bakken, David and Allenby, Greg M., Efficient Experimental Designs for Hyperparameter Estimation: Learning When Effect-Sizes are Large (April 2007). Fisher College of Business Working Paper No. #. Available at SSRN: https://ssrn.com/abstract=945836 or http://dx.doi.org/10.2139/ssrn.945836