Abstract
This article empirically tests a proposal (due to Hagerty) to use Q-type factor analysis to maximize predictive accuracy in conjoint analysis. Data sets from three different studies are used to compare the accuracy of predictions from optimal weighting with those from individual conjoint parameter estimation. The results do not support the contention that optimal weighting significantly improves cross-validity, as compared to individual conjoint prediction.
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He has been honored for his research by the American Marketing Association, the American Statistical Association, the American Psychological Association, and the Market Research Society (England). He has authored or coauthored several books, including the widely used textResearch for Marketing Decisions, now in its fifth edition. He is also a prolific contributor to marketing and business journals.
He is the author or coauthor of many articles in statistical methodology and the interface between statistical methodology and optimization theory. His current research interests include theoretical and empirical analyses of the bootstrap resampling technique and application of statistical methods and operations research to problems in marketing research.
She received her Ph.D. in marketing from Drexel University in 1990. Her research interests focus on consumer preference models including conjoint analysis and new-product development. Her work has been published in theJournal of Marketing Research, Journal of Advertising Research, andJournal of the Market Research Society.
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Green, P.E., Krieger, A.M. & Schaffer, C.M. An empirical test of optimal respondent weighting in conjoint analysis. JAMS 21, 345–351 (1993). https://doi.org/10.1007/BF02894527
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DOI: https://doi.org/10.1007/BF02894527