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An empirical test of optimal respondent weighting in conjoint analysis

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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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References

  • DeSarbo, Wayne S., Richard L. Oliver, and Arvind Rangaswamy. 1989. “A Simulated Annealing Methodology for Clusterwise Linear Regression.”Psychometrika 54 (December): 707–736.

    Article  Google Scholar 

  • DeSarbo, Wayne S., Michel Wedel, Marco Vriens, and Venkatram Ramaswamy. 1992. “Latent Class Metric Conjoint Analysis.”Marketing Letters 3 (July): 273–289.

    Article  Google Scholar 

  • Green, Paul E. and Kristiaan Helsen. 1989. “Cross-Validation Assessment of Alternatives to Individual-level Conjoint Analysis: A Case Study.”Journal of Marketing Research 26 (August): 346–350.

    Article  Google Scholar 

  • Green, Paul E., Abba M. Krieger, and Catherine M. Schaffer. 1993. “A Hybrid Conjoint Model with Individual-Level Interaction Estimation.”Advances in Consumer Research. Volume 20. Eds. Leigh McAlister and Michael L. Rothschild. Provo, UT: Association for Consumer Research, 149–154.

    Google Scholar 

  • Green, Paul E. and Vithala R. Rao. 1971. “Conjoint Measurement for Quantifying Judgmental Data.”Journal of Marketing Research 8 (August): 355–363.

    Article  Google Scholar 

  • Green Paul E. and Catherine M. Schaffer. 1991. “Importance Weight Effects on Self-Explicated Models: Some Empirical Findings.”Advances in Consumer Research Volume 18. Eds. Rebecca M. Holman and Michael R. Solomon. Provo, UT: Association of Consumer Research, 476–482.

    Google Scholar 

  • Hagerty, Michael R. 1985. “Improving the Predictive Power of Conjoint Analysis: The Use of Factor Analysis and Cluster Analysis.”Journal of Marketing Research 22 (May): 168–184.

    Article  Google Scholar 

  • Kamakura, Wagner A. 1988. “A Least Squares Procedure for Benefit Segmentation for Conjoint Experiments.”Journal of Marketing Research 25 (May): 157–167.

    Article  Google Scholar 

  • Mallows, Colin. 1973. “Some Comments on Cp.”Technometrics 15 (November): 661–676.

    Article  Google Scholar 

  • Ogawa, Kohsuke. 1987. “An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis.”Marketing Science 6 (Winter): 66–81.

    Article  Google Scholar 

  • Pekelman, Dov and Subrata K. Sen. 1979. “Improving Prediction in Conjoint Analysis.”Journal of Marketing Research 16 (May): 211–220.

    Article  Google Scholar 

  • Srinivasan, V., Arun K. Jain, and Naresh K. Malhotra. 1983. “Improving the Predictive Power of Conjoint Analysis by Constrained Parameter Estimation.”Journal of Marketing Research 20 (November): 433–438.

    Article  Google Scholar 

  • van der Lans, Ivo A. and William J. Heiser. 1992. “Constrained Part-Worth Estimation in Conjoint Analysis Using the Self-Explicated Utility Model.”International Journal of Research in Marketing 9 (December): 325–344.

    Article  Google Scholar 

  • Wedel, Michel and Cor Kistemaker. 1989. “Consumer Benefit Segmentation Using Clusterwise Linear Regression.”International Journal of Research in Marketing 6 (March): 45–59.

    Article  Google Scholar 

  • Wedel, Michel and Jan-Benedict E. M. Steenkamp. 1989. “A Fuzzy Clusterwise Regression Approach to Benefit Segmentation.”International Journal of Research in Marketing 6 (September): 241–258.

    Article  Google Scholar 

  • Wedel, Michel and Jan-Benedict E. M. Steenkamp. 1991. “A Clusterwise Regression Method for Simultaneous Fuzzy Market Structuring and Benefit Segmentation.”Journal of Marketing Research 28 (November): 385–396.

    Article  Google Scholar 

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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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