Estimating independent locally shifted random utility models for ranking data

Multivariate Behavioral Research

Volumn 46, Issue 5, 2011, Pages 756-778

  Lam, Kar Yin ^a   Koning, Alex J ^a   Franses, Philip Hans ^a  

^a ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84855806191     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1080/00273171.2011.606754     Document Type: Article

References (35)

1. Abramowitz, M., & Stegun, I. A. (1964). Handbook of mathematical functions with formulas, graphs, and mathematical tables (Vol. 55). Washington, DC: U.S. Government Printing Office.
2. Barron, F. H. (1977). Axiomatic conjoint measurement. Decision Sciences, 8, 548-559.
3. Ben-Akiva, M., Morikawa, T., & Shiroishi, F. (1992).Analysis of the reliability of preference ranking data. Journal of Business Research, 24, 149-164.
4. Böckenholt, U. (2006). Thurstonian-based analyses: Past, present, and future utilities. Psychometrika, 71, 615-629.
5. Bradlow, E. T. (2005). Current issues and a 'wish list' for conjoint analysis. Applied Stochastic Models in Business and Industry, 21, 319-323.
6. Browne, M.W. (1982). Covariance structures. In D. M. Hawkins (Ed.), Topics in applied multivariate analysis (pp. 72-141). Cambridge, MA: Cambridge University Press.
7. Chrzan, K., & Golovashkina, N. (2006). An empirical test of six stated importance measures. International Journal of Market Research, 48, 717-740.
8. Critchlow, D. E., Fligner, M. A., & Verducci, J. S. (1991). Probability models on rankings. Journal of Mathematical Psychology, 35, 294-318.
9. Finn, A., & Louviere, J. J. (1992). Determining the appropriate response to evidence of public concern: The case of food safety. Journal of Public Policy & Marketing, 11, 12-25.
10. Flynn, T. N., Louviere, J. J., Peters, T. J., & Coast, J. (2007). Best-worst scaling: What it can do for health care research and how to do it. Journal of Health Economics, 26, 171-189.
11. Green, P. E., Krieger, A. M., & Wind, Y. (2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31, 56-73.
12. Green, P. E., & Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355-363.
13. Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. The Journal of Consumer Research, 5, 103-123.
14. Hájek, J., & Šidák, Z. (1967). Theory of rank tests. New York, NY: Academic Press.
15. Harter, H. L. (1961). Expected values of normal order statistics. Biometrika, 48, 151-165.
16. Hauser, J. R., & Rao, V. R. (2004). Conjoint analysis, related modeling, and applications. In P. E. Green & Y. J. Wind (Eds.), Marketing research and modeling: Progress and prospects: A tribute to Paul E. Green (pp. 141-168). Dordrecht the Netherlands: Kluwer Academic.
17. Henery, R. J. (1981). Permutation probabilities as models for horse races. Journal of the Royal Statistical Society. Series B (Methodological), 43, 86-91. Retrieved from http://www.jstor.org/stable/2985154
18. Joe, H. (1997). Multivariate models and dependence concepts (Vol. 73). London, England: Chapman & Hall.
19. Johnson, R. M. (1974). Trade-off analysis of consumer values. Journal of Marketing Research, 11, 121-127.
20. Krantz, D. H., & Tversky, A. (1971). Conjoint measurement analysis of composition rules in psychology. Psychological Review, 78, 151-169.
21. Kruskal, J. B. (1965). Analysis of factorial experiments by estimating monotone transformations of the data. Journal of the Royal Statistical Society. Series B (Methodological), 27, 251-263. Retrieved from http://www.jstor.org/stable/2984194
22. Louviere, J. J., & Woodworth, G. G. (1991). Best-worst scaling: A model for the largest difference judgments (Working paper). Edmonton, Canada: University of Alberta.
23. Luce, R. D., & Suppes, P. (1965). Preference, utility, and subjective probability. In R. D. Luce, R. R. Bush, & E. Galanter (Eds.), Handbook of mathematical psychology (Vol. III, pp. 235-306).New York, NY: Wiley.
24. Luce, R. D., & Tukey, J. W. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1, 1-27.
25. Marden, J. I. (1995). Analyzing and modeling rank data (Vol. 64). London, England: Chapman & Hall.
26. Marley, A., & Louviere, J. (2005). Some probabilistic models of best, worst, and best-worst choices. Journal of Mathematical Psychology, 49, 464-480.
27. Maydeu-Olivares, A. (1999). Thurstonian modeling of ranking data via mean and covariance structure analysis. Psychometrika, 64, 325-340.
28. Maydeu-Olivares, A., & Böckenholt, U. (2005). Structural equation modeling of paired-comparison and ranking data. Psychological Methods, 10, 285-304.
29. Maydeu-Olivares, A., & Böckenholt, U. (2009). Modeling preference data. In R. E. Millsap & A. Maydeu-Olivares (Eds.), Handbook of quantitative methods in psychology (pp. 264-282). London, England: Sage.
30. Maydeu-Olivares, A., & Hernández, A. (2007). Identification and small sample estimation of Thurstone's unrestricted model for paired comparisons data. Multivariate Behavioral Research, 42, 323-347.
31. Plackett, R. L. (1975). The analysis of permutations. Applied Statistics, 24, 193-202. Retrieved from http://www.jstor.org/stable/2346567
32. Rewtrakunphaiboon, W., & Oppewal, H. (2008). Effects of package holiday information presentation on destination choice. Journal of Travel Research, 47, 127-136.
33. Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273-286.
34. Thurstone, L. L. (1931). Rank order as a psychophysical method. Journal of Experimental Psychol-ogy, 14, 187-201.
35. Yellott, J. I. Jr. (1977). The relationship between Luce's choice axiom, Thurstone's theory of comparative judgment, and the double exponential distribution. Journal of Mathematical Psychology, 15, 109-144.