Modeling Simultaneity in Survey Data
Tóm tắt
Responses to questions in a survey can reflect a behavior process that influences multiple response items. Respondent ratings of brand attributes, for example, can be affected by past purchases by making a brand more salient, or by respondents attributing higher performance to justify their purchases. When multiple response items are influenced by a common underlying process, there is simultaneity in the data. This paper proposes an approach to model the simultaneity in different survey responses by using common parameters and structural relationships motivated by behavioral theories on how consumers respond to surveys. Specifically, the proposed models show how brand usage and attribute perception responses are jointly determined by justification, order, and brand halo effects in two brand positioning studies. We detect a significant tendency for respondents to inflate their reported beliefs for particular brands as well as the selected brand across five countries in an international survey as well as in a domestic study.
Tài liệu tham khảo
Albert, James H. and Siddhartha Chib. (1993). “Bayesian Analysis of Binary and Polychotomous Response Data.” Journal of the American Statistical Association 88, 669–679.
Allenby, Greg M., Nereraj Arora, and L. James Ginter. (1995). “Incorporating Prior Knowledge in the Analysis of Conjoint Studies.” Journal of Marketing Research 32 (May), 152–162.
Baumgartner, Hans and E.M. Jan-Benedict Steenkamp. (2001). “Response Styles in Marketing Research: A Cross-National Investigation.” Journal of Marketing Research 38 (May),143–156.
Brown, Christina L. and Fred Feinberg. (2004). “Bolstering: How Does Choice Distort Product Evaluations?” Working paper, The University of Michigan.
Cattin, Philippe and Dick R. Wittink. (1982). “Commercial Use of Conjoint Analysis: A Survey.” Journal of Marketing 46 (Summer), 44–53.
Day, George S. (1972). “Evaluating Models of Attitude Structure.” Journal of Marketing Research 9(August), 279–286.
Dillon, William R., Thomas J. Madden, Amna Kirmani, and Soumen Mukherjee.(2001). “Understanding What's in a Brand Rating: A Model for Assessing Brandand Attribute Effects and Their Relationship of Brand Equity.” Journal of Marketing Research 38(November), 415–429.
Edwards, Yancy D. and Greg M. Allenby. (2003). “Multivariate Analysis of Multiple Response Data.” Journal of Marketing Research 40 (August), 321–334.
Engle, Robert F., David F. Hendry, and Jean-Francois Richard. (1983). “Exogeneity.” Econometrica 51, 277–304.
Gelfand, Alan E., Susan E. Hills, Amy Racine-Poon, and F. M. Adrian Smith. (1990). “Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling.” Journal of the American Statistical Association 85, 972–985.
Geyer, Charles J. (1992). “Practical Markov Chain Monte Carlo.” Statistical Science 7 (November), 473–483.
Green, Paul E. and Vithala R. Rao. (1971). “Conjoint Measurement for Quantifying Judgmental Data.” Journal of Marketing Research 8 (August), 355–363.
Hajivassiliou, V., D. McFadden, and P. Rudd. (1996). “Simulation of Multivariate Normal Rectangle Probabilities and their Derivatives.” Journal of Econometrics 72, 85–134.
Janiszewski, Chris, Tim Silk, and Alan D. J. Cooke. (2003). “Different Scales for Different Frames: The Role of Subjective Scales and Experience in Explaining Attribute Framing Effects.” Journal of Consumer Research 30, 311–325.
Keane, M. (1994). “A Computationally Practical Simulation Estimator for Panel Data.” Econometrica 62, 95–116.
Maddala, G.S. (1983). Limited Dependent and Qualitative Variables in Econometrics. New York: Cambridge University Press.
Maddala, G.S. and Lung-Fei Lee, (1976). “Recursive Models with Qualitative Endogenous Variables.” Annals of Economic and Social Measurement 5/4, 525–545.
McCulloch, Robert and Peter E. Rossi. (1994). “An Exact Likelihood Analysisof the Multinomial Probit Model.” Journal of Econometrics 64, 207–240.
Nisbett, R. and T. D. Wilson. (1977). “Telling More Than We Know: Verbal Reports on Mental Processes.” Psychological Review 84, 231–259.
Pettibone, Jonathan C. and Douglas H. Wedell. (2000). “Examining Models of Nondominated Decoy Effects across Judgment and Choice.” Organizational Behavior and Human Decision Processes 81 (March),300–328.
Rossi, Peter E. and Greg M. Allenby. (2003). “Bayesian Statistics and Marketing.” Marketing Science 22, 304–328.
Rossi, Peter E., Zvi Gilula, and Greg M. Allenby. (2001). “Overcoming Scale Use Heterogeneity: A Bayesian Hierarchical Approach.” Journal of the American Statistical Association 96, 20–31.
Rossi, Peter E., Robert E. McCulloch, and Greg M. Allenby. (1996). “The Value of Purchase History Data in Target Marketing.” Marketing Science 15, 321–340.
Simonson, Itamar. (1989). “Choice Based on Reasons: The Case of Attractionand Compromise Effects.” Journal of Consumer Research 16 (September), 158–174.
Tanner, M. A. and W. Wong (1987) “The Calculation of Posterior Distributions by Data Augmentation.” Journal of the American Statistical Association 82, 528–549.
Wertenbroch, Klaus and Bernd Skiera. (2002). “Measuring Consumers Willingnessto Pay at the Point of Purchase.” Journal of Marketing Research 39, 228–241.
Yang, Sha, Yuxin Chen, and Greg Allenby. (2003). “Bayesian Analysis of Simultaneous Demand and Supply.” Quantitative Marketing and Economics 1 (September), 251–275.
Zellner, Arnold. (1971). An Introduction to Bayesian Inference in Econometrics. New York: John Wiley & Sons, Inc.