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Efficient Experimental Designs for Hyperparameter Estimation: Learning When Effect-Sizes are Large

38 Pages Posted: 18 Nov 2006  

Qing Liu

University of Wisconsin-Madison

Angela Dean

Ohio State University (OSU)

David Bakken

Harris Interactive

Greg M. Allenby

Ohio State University (OSU) - Department of Marketing and Logistics

Date Written: April 2007

Abstract

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

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

Qing Liu (Contact Author)

University of Wisconsin-Madison ( email )

United States
608-263-9298 (Phone)

HOME PAGE: http://bus.wisc.edu/faculty/qing-liu

Angela Dean

Ohio State University (OSU) ( email )

Blankenship Hall-2010
901 Woody Hayes Drive
Columbus, OH OH 43210
United States

David Bakken

Harris Interactive ( email )

United States

Greg M. Allenby

Ohio State University (OSU) - Department of Marketing and Logistics ( email )

Fisher Hall 524
2100 Neil Ave
Columbus, OH 43210
United States

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