Individual differences in paired comparison data

British Journal of Mathematical and Statistical Psychology

Volumn 54, Issue 2, 2001, Pages 265-277

  Böckenholt, Ulf ^a   Tsai, Rung Ching ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ATTITUDE; DECISION MAKING; HUMAN; INDIVIDUALITY; MATHEMATICAL COMPUTING; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ATTITUDE; CHOICE BEHAVIOR; DATA INTERPRETATION, STATISTICAL; HUMAN; INDIVIDUALITY; LIKELIHOOD FUNCTIONS; MATHEMATICAL COMPUTING; MODELS, STATISTICAL; SUPPORT, U.S. GOV'T, NON-P.H.S.;

EID: 0035514763     PISSN: 00071102     EISSN: None     Source Type: Journal    
DOI: 10.1348/000711001159555     Document Type: Article

References (25)

1. Andrich, D. (1989). A probabilistic item response theory model for unfolding preference data. Applied Psychological Measurement, 13, 193-216.
2. Bock, R. D. (1958). Remarks on the test of significance for the method of paired comparisons. Psychometrika, 23, 323-334.
3. Bock, R. D., & Jones, L. V. (1968). The measurement and prediction of judgment and choice. San Francisco: Holden-Day.
4. Böckenholt, U., & Dillon, W. R. (1997). Modeling within-subject dependencies in ordinal paired comparison data. Psychometrika, 62, 414-434.
5. Brady, H. E. (1989). Factor and ideal point analysis for interpersonally incomparable data. Psychometrika, 54, 181-202.
6. Chan, S. K., & Kuk, A. Y. C. (1997). Maximum likelihood estimation for probit-linear mixed models with correlated random effects. Biometrics, 53, 86-97.
7. Dansie, B. R. (1986). Normal order statistics as permutation probability models. Applied Statistics, 35, 269-275.
8. David, H. A. (1988). The method of paired comparisons. London: Griffin.
9. Geman, D., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
10. Geweke, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica, 57, 1317-1340.
11. Hajivassiliou, V. A. (1993). Simulation estimation methods for limited dependent variable models. In G. S. Maddala, C. R. Rao & H. D. Vinod (Eds.), Handbook of Statistics, Vol. 11. Amsterdam: Elsevier.
12. Hajivassiliou, V. A., McFadden, D., & Ruud, P. (1996). Simulation of multivariate normal rectangle probabilities and their derivatives. Theoretical and computational results. Journal of Econometrics, 72, 85-134.
13. Keane, M. (1994). A computationally practical simulation estimator for panel data. Econometrica, 62, 95-116.
14. Kroeger, K. (1992). {Modeling intransitivity through log-bilinear models.} Unpublished M.A. thesis. Department of Psychology, University of Illinois, Urbana-Champaign.
15. Luo, G. (1998). A general formulation of unidimensional unfolding and pairwise preference models: Making explicit the latitude of acceptance. Journal of Mathematical Psychology, 42, 400-417.
16. McCulloch, C. E. (1994). Maximum likelihood variance components estimation for binary data. Journal of the American Statistical Association, 89, 330-335.
17. Meng, X.-L., & Schilling, S. (1996). Fitting full-information item factor models and an empirical investigation of bridge sampling. Journal of the American Statistical Association, 91, 1254-1267.
18. Rumelhart, D. L., & Greeno, J. G. (1971). Similarity between stimuli. An experimental test of the Luce and Restle choice models. Journal of Mathematical Psychology, 8, 370-381.
19. Takane, Y. (1987). Analysis of covariance structures and probabilistic binary choice data. Cognition and Communication, 20, 45-62.
20. Tanner, M. A. (1996). Tools for statistical inference. New York: Springer-Verlag.
21. Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273-286.
22. Tsai, R. (2000). Remarks on the identifiability of Thurstonian ranking models: Case V, Case III, or Neither? Psychometrika, 65, 233-240.
23. Vijverberg, W. P. M. (1997). Monte Carlo evaluation of multivariate normal probabilities. Journal of Econometrics, 76, 281-307.
24. Wei, G. C. G., & Tanner, M. A. (1990). A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Journal of the American Statistical Association, 85, 699-704.
25. Yao, G., & Böckenholt, U. (1999). Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler. British Journal of Mathematical and Statistical Psychology, 52, 79-92.