Hostname: page-component-5f745c7db-8qdnt Total loading time: 0 Render date: 2025-01-06T06:54:26.976Z Has data issue: true hasContentIssue false

A Stochastic Multidimensional Scaling Procedure for the Empirical Determination of Convex Indifference Curves for Preference/Choice Analysis

Published online by Cambridge University Press:  01 January 2025

Wayne S. DeSarbo*
Affiliation:
Departments of Marketing and Statistics, School of Business, The University of Michigan
Kamel Jedidi
Affiliation:
Graduate School of Business, Columbia University
Joel H. Steckel
Affiliation:
Department of Marketing, Stern School of Business, New York University
*
Requests for reprints should be sent to Wayne S. DeSarbo, Departments of Marketing and Statistics, School of Business, University of Michigan, Ann Arbor, MI 48109-1234.

Abstract

The vast majority of existing multidimensional scaling (MDS) procedures devised for the analysis of paired comparison preference/choice judgments are typically based on either scalar product (i.e., vector) or unfolding (i.e., ideal-point) models. Such methods tend to ignore many of the essential components of microeconomic theory including convex indifference curves, constrained utility maximization, demand functions, et cetera. This paper presents a new stochastic MDS procedure called MICROSCALE that attempts to operationalize many of these traditional microeconomic concepts. First, we briefly review several existing MDS models that operate on paired comparisons data, noting the particular nature of the utility functions implied by each class of models. These utility assumptions are then directly contrasted to those of microeconomic theory. The new maximum likelihood based procedure, MICROSCALE, is presented, as well as the technical details of the estimation procedure. The results of a Monte Carlo analysis investigating the performance of the algorithm as a number of model, data, and error factors are experimentally manipulated are provided. Finally, an illustration in consumer psychology concerning a convenience sample of thirty consumers providing paired comparisons judgments for some fourteen brands of over-the-counter analgesics is discussed.

Type
Original Paper
Copyright
Copyright © 1991 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Addelman, S. (1962). Orthogonal main effect plans for asymmetrical factorial experiments. Technometrics, 4, 2146.CrossRefGoogle Scholar
Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions on Automatic Control, AC-19, 716723.CrossRefGoogle Scholar
Bechtel, G. G., Tucker, L. R., & Chang, W. (1971). A scalar product model for the multidimensional scaling of choice. Psychometrika, 36, 369388.CrossRefGoogle Scholar
Bentler, P. M., & Weeks, D. G. (1978). Restricted multidimensional scaling models. Journal of Mathematical Psychology, 17, 138151.CrossRefGoogle Scholar
Bloxom, B. (1978). Constrained multidimensional scaling in N spaces. Psychometrika, 43, 397408.CrossRefGoogle Scholar
Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345370.CrossRefGoogle Scholar
Carroll, J. D. (1972). Individual differences and multidimensional scaling. In Shepard, R. N., Romney, A. K. & Nerlove, S. (Eds.), Multidimensional scaling: Theory and application in the behavioral sciences. Vol. I: Theory (pp. 105155). New York: Academic Press.Google Scholar
Carroll, J. D. (1980). Models and methods for multidimensional analysis of preferential choice (or other dominance data). In Lantermann, E. D. & Feger, H. (Eds.), Similarity and choice (pp. 234289). Bern: Hans Huber.Google Scholar
Carroll, J. D., Pruzansky, S., & Kruskal, J. B. (1980). CANDELINC: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters. Psychometrika, 45, 324.CrossRefGoogle Scholar
Coombs, C. H. (1964). A theory of data, New York: Wiley.Google Scholar
Cooper, L. G. (1973). A multivariate investigation of preferences. Multivariate Behavioral Research, 8, 253272.CrossRefGoogle ScholarPubMed
Cooper, L. G., & Nakanishi, M. (1983). Two logit models for external analysis of preference. Psychometrika, 48, 607620.CrossRefGoogle Scholar
de Leeuw, J., & Heiser, W. (1980). Multidimensional scaling with restrictions on the configuration. In Krishnaiah, P. R. (Eds.), Multivariate analysis V (pp. 285316). Amsterdam: North-Holland.Google Scholar
DeSarbo, W. S. (1982). GENNCLUS: New models for general nonhierarchical clustering analysis. Psychometrika, 47, 449475.CrossRefGoogle Scholar
DeSarbo, W. S., & Carroll, J. D. (1985). Three-way metric unfolding via alternating weighted least squares. Psychometrika, 50, 275300.CrossRefGoogle Scholar
DeSarbo, W. S., Carroll, J. D., Lehmann, D. R., & O'Shaughnessy, J. (1982). Three-way multivariate conjoint analysis. Marketing Science, 1, 323350.CrossRefGoogle Scholar
DeSarbo, W. S., De Soete, G., Carroll, J. D., & Ramaswamy, V. (1988). A new stochastic ultrametric tree unfolding methodology for assessing competitive market structure and deriving market segments. Applied Stochastic Models and Data Analysis, 4, 185204.CrossRefGoogle Scholar
DeSarbo, W. S., De Soete, G., & Eliashberg, J. (1986). A new stochastic multidimensional unfolding model for the investigation of paired comparisons in consumer preference/choice data. Journal of Economic Psychology, 8, 357384.CrossRefGoogle Scholar
DeSarbo, W. S., De Soete, G., & Jedidi, K. (1987). Probabilistic multidimensional scaling models for analyzing consumer choice behavior. Communication and Cognition, 20, 93116.Google Scholar
DeSarbo, W. S., Oliver, R. L., & De Soete, G. (1986). A probabilistic multidimensional scaling vector model. Applied Psychological Measurement, 10, 7998.CrossRefGoogle Scholar
DeSarbo, W. S., & Rao, V. R. (1984). GENFOLD2: A set of models and algorithms for the GENeral unFOLDing analysis of preference/dominance data. Journal of Classification, 1, 147186.CrossRefGoogle Scholar
DeSarbo, W. S., & Rao, V. R. (1986). A constrained unfolding model for product positioning. Market Science, 5, 119.CrossRefGoogle Scholar
De Soete, G., & Carroll, J. D. (1983). A maximum likelihood method for fitting the wandering vector model. Psychometrika, 48, 553566.CrossRefGoogle Scholar
De Soete, G., & Carroll, J. D. et al. (1986). Probabilistic multidimensional choice models for representing paired comparisons data. In Diday, E. et al. (Eds.), Data analysis and informatics IV (pp. 485497). Amsterdam: North-Holland.Google Scholar
De Soete, G., Carroll, J. D., & DeSarbo, W. S. (1986). The wandering ideal point model: A probabilistic multidimensional unfolding model for paired comparisons data. Journal of Mathematical Psychology, 30, 2841.CrossRefGoogle Scholar
Gill, P. E., Murray, W., & Wright, M. H. (1981). Practical optimization, New York: Academic Press.Google Scholar
Grandmount, J. M. (1978). Intermediate preferences and the majority rule. Econometrica, 46, 317330.CrossRefGoogle Scholar
Haines, G. (1975). Commentary on Ratchford, ‘the new economic theory of consumer behavior’. Journal of Consumer Research, 2, 7778.Google Scholar
Hauser, J. R. (1988). Competitive price and positioning strategies. Marketing Science, 7, 7691.CrossRefGoogle Scholar
Hauser, J. R., & Shugan, S. (1983). Defensive marketing strategies. Marketing Science, 2, 319360.CrossRefGoogle Scholar
Hauser, J. R., & Simmie, P. (1981). Profit maximizing perceptual positions: An integrated theory for the selection of product features and price. Management Science, 27, 3356.CrossRefGoogle Scholar
Henderson, J. M., & Quandt, R. E. (1984). Microeconomic theory 3rd ed.,, New York: McGraw-Hill.Google Scholar
Hendler, R. (1975). Lancaster's new approach to consumer demand and its limitations. American Economic Review, 64, 194199.Google Scholar
Krishnan, K. S. (1977). Incorporating thresholds of indifference in probabilistic choice models. Management Science, 23, 12241233.CrossRefGoogle Scholar
Ladd, G. W., & Zober, M. (1977). A model of consumer reaction to product characteristics. Journal of Consumer Research, 4, 89101.CrossRefGoogle Scholar
Lancaster, K. (1966). A new approach to consumer theory. Journal of Political Economy, 84, 132157.CrossRefGoogle Scholar
Lancaster, K. (1971). Consumer demand: A new approach, New York: Columbia University Press.Google Scholar
Lioukas, S. K. (1984). Thresholds and transitivity in stochastic consumer choice: A multinomial logit analysis. Management Science, 30, 110122.CrossRefGoogle Scholar
Lucas, R. E. (1975). Hedonic price functions. Economic Inquiry, 13, 157178.CrossRefGoogle Scholar
MacCrimmon, K. R., & Toda, M. (1969). The experimental determination of indifference curves. Review of Economic Studies, 36, 432451.CrossRefGoogle Scholar
McCullagh, P., & Nelder, J. A. (1983). Generalized linear models, New York: Chapman & Hall.CrossRefGoogle Scholar
McFadden, D. (1976). Quantal choice analysis: A survey. Annals of Economic and Social Measurement, 5, 363390.Google Scholar
Nakanishi, M., & Cooper, L. G. (1974). Parameter estimation for a multiplicative competitive interaction model—A least squares approach. Journal of Marketing Research, 11, 303311.Google Scholar
Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A, 135, 370384.CrossRefGoogle Scholar
Pekelman, D., & Sen, S. (1975, August). A Lancasterian approach to multiattribute marketing models. Paper presented at the American Marketing Association Meeting. Rochester, NY.Google Scholar
Powell, M. J. D. (1977). Restart procedures for the conjugate gradient method. Mathematical Programming, 12, 241254.CrossRefGoogle Scholar
Quandt, R. E. (1956). A probabilistic theory of consumer behavior. Quarterly Journal of Economics, 70, 507536.CrossRefGoogle Scholar
Ratchford, B. T. (1975). The new economic theory of consumer behavior: An interpretive essay. Journal of Consumer Research, 2, 6575.CrossRefGoogle Scholar
Ratchford, B. T. (1979). Operationalizing economic models of demand for product characteristics. Journal of Consumer Research, 6, 7687.CrossRefGoogle Scholar
Rousseas, S. W., & Hart, A. G. (1951). Experimental verification of a composite indifference map. Journal of Political Economy, 59, 288318.CrossRefGoogle Scholar
Russell, R. R., & Wilkinson, M. (1979). Microeconomics: A synthesis of modern and neoclassical theory, New York: Wiley & Sons.Google Scholar
Ryans, A. B. (1974). Estimating consumer preferences for a new durable brand on an established product class. Journal of Marketing Research, 11, 434443.CrossRefGoogle Scholar
Schönemann, P. H., & Wang, M. M. (1972). An individual difference model for the multidimensional analysis of preference data. Psychometrika, 37, 275305.CrossRefGoogle Scholar
Slater, P. (1960). The analysis of personal preference. British Journal of Statistical Psychology, 13, 119135.CrossRefGoogle Scholar
Snedecor, G. W., & Cochran, W. G. (1981). Statistical methods 7th ed., Ames: Iowa State University Press.Google Scholar
Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273286.CrossRefGoogle Scholar
Thurstone, L. L. (1931). The indifference function. Journal of Social Psychology, 2, 139167.CrossRefGoogle Scholar
Tucker, L. R. (1960). Intra-individual and inter-individual multidimensionality. In Gulliksen, H., Messick, S. (Eds.), Psychological scaling: Theory and applications (pp. 155167). New York: Wiley.Google Scholar
Varian, H. R. (1984). Microeconomic analysis, New York: W. W. Norton.Google Scholar
Wang, M. M., Schönemann, P. H., & Rusk, J. G. (1975). A conjugate gradient algorithm for the multidimensional analysis of preference data. Multivariate Behavioral Research, 10, 4599.CrossRefGoogle ScholarPubMed
Zinnes, J. L., & Griggs, R. A. (1974). Probabilistic multidimensional unfolding analysis. Psychometrika, 39, 327350.CrossRefGoogle Scholar