The nominal categories item response model

Handbook of Polytomous Item Response Theory Models

Volumn , Issue , 2011, Pages 43-75

  Thissen, David ^a   Cai, Li ^b   Bock, R Darrell ^c  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85077081626     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (61)

1. Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: Wiley.
2. Allen, N. L., Carlson, J. E., & Zelenak, C. A. (1999). The NAEP 1996 technical report (NCES 1999-452). Washington, DC: National Center for Education Statistics, Office of Educational Research and Improvement, U.S. Department of Education.
3. Andersen, E. B. (1977). Sufficient statistics and latent trait models. Psychometrika, 42, 69-81.
4. Andersen, E. B. (1997). The rating scale model. In W. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 67-84). New York: Springer.
5. Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561-573.
6. Andrich, D. (1985). An elaboration of Guttman scaling with Rasch models for measurment. In N. Brandon-Tuma (Ed.), Sociological methodology (pp. 33-80). San Francisco: Jossey-Bass.
7. Anscombe, F. J. (1956). On estimating binomial response relations. Biometrika, 35, 246-254.
8. Baker, F. B., & Kim, S.-H. (2004). Item response theory: Parameter estimation techniques (2nd ed., revised and expanded). New York: Marcel Dekker.
9. Bergan, J. R., & Stone, C. A. (1985). Latent class models for knowledge domains. Psychological Bulletin, 98, 166-184.
10. Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39, 357-375.
11. Berkson, J. (1953). A statistically precise and relatively simple method of estimating the bio-assay with quantal response, based on the logistic function. Journal of the American Statistical Association, 48, 565-599.
12. Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores (pp. 395-479). Reading, MA: Addison-Wesley.
13. Bock, R. D. (1956). A generalization of the law of comparative judgment applied to a problem in the prediction of choice [Abstract]. American Psychologist, 11, 442.
14. Bock, R. D. (1970). Estimating multinomial response relations. In E. A. R. C. Bose (Ed.), Contribution to statistics and probability (pp. 111-132). Chapel Hill, NC: University of North Carolina Press.
15. Bock, R. D. (1972). Estimating item parameters and latent ability when responses are scored in two or more latent categories. Psychometrika, 37, 29-51.
16. Bock, R. D. (1975). Multivariate statistical methods in behavioral research. New York: McGraw-Hill.
17. Bock, R. D. (1997). The nominal categories model. In W. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 33-50). New York: Springer.
18. Bock, R. D., & Jones, L. V. (1968). The measurement and prediction of judgment and choice. San Francisco: Holden-Day.
19. Bock, R. D., & Moustaki, I. (2007). Item response theory in a general framework. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics: Psychometrics (Vol. 26, pp. 469-513). Amsterdam: Elsevier.
20. Bradley, R. A., & Terry, M. E. (1952). Rank analysis of incomplete block designs. I. Method of paired comparisons. Biometrika, 39, 324-345.
21. Childs, R. A., & Chen, W.-H. (1999). Software note: Obtaining comparable item parameter estimates in MULTILOG and PARSCALE for two polytomous IRT models. Applied Psychological Measurement, 23, 371-379.
22. de Ayala, R. J. (1992). The nominal response model in computerized adaptive testing. Applied Psychological Measurement, 16, 327-343.
23. de Ayala, R. J. (1993). An introduction to polytomous item response theory models. Measurement and Evaluation in Counseling and Development, 25, 172-189.
24. Dubey, S. D. (1969). A new derivation of the logistic distribution. Naval Research Logistics Quarterly, 16, 37-40.
25. du Toit, M. (Ed.). (2003). IRT from SSI: BILOG-MG MULTILOG PARSCALE TESTFACT. Lincolnwood, IL: Scientific Software International.
26. Gumbel, E. J. (1961). Bivariate logistic distributions. Journal of the American Statistical Association, 56, 335-349.
27. Hoskens, M., & Boeck, P. D. (1997). A parametric model for local dependence among test items. Psychological Methods, 2, 261-277.
28. Huber, M. (1993). An item response theoretical approach to scoring the Short Portable Mental Status Questionnaire for assessing cognitive status of the elderly. Unpublished master's thesis, Department of Psychology, University of North Carolina, Chapel Hill
29. Johnson, N. L., Kotz, N., & Balakrishnan, N. (1995). Continuous univariate distributions (2nd ed., Vol. 2). New York: Wiley.
30. Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
31. Luce, R. D., & Suppes, P. (1965). Preference, utility, and subjective probability. In R. D. Luce & R. R. Bush (Eds.), Handbook of mathematical psychology (Vol. 3 pp. 249-410). New York: Wiley.
32. Malik, H., & Abraham, B. (1973). Multivariate logistic distributions. Annals of Statistics, 1, 588-590.
33. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
34. Masters, G. N., & Wright, B. D. (1984). The essential process in a family of measurement models. Psychometrika, 49, 529-544.
35. Masters, G. N., & Wright, B. D. (1997). The partial credit model. In W. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 101-122). New York: Springer.
36. McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers of econometrics (pp. 105-142). New York: Academic Press.
37. Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159-176.
38. Muraki, E. (1997). A generalized partial credit model. In W. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 153-164). New York: Springer.
39. National Bureau of Standards (1956). Tables of the bivariate normal distribution function and related functions. Applied Mathematic Series, Number 50.
40. Ramsay, J. O. (1995). Testgraf: A program for the graphical analysis of multiple-choice test and questionnaire data (Technical Report). Montreal: McGill University (Psychology Department).
41. Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Denmarks Paedagogiske Institut.
42. Rasch, G. (1961). On general laws and the meaning of measurement in psychology. In Proceedings of the Fourth Annual Berkeley Symposium on Mathematical Statistics and Probability (Vol. 4, pp. 321-333). Berkeley: University of California Press.
43. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded cores. Psychometric Monograph, No. 18.
44. Samejima, F. (1979). A new family of models for the multiple choice item (Research Report 79-4). Knoxville: University of Tennessee (Department of Psychology).
45. Samejima, F. (1988). Comprehensive latent trait theory. Behaviormetrika, 15, 1-24.
46. Samejima, F. (1996). Evaluation of mathematical responses for ordered polychotomous responses. Behaviormetrika, 23, 17-35.
47. Samejima, F. (1997). Graded response model. In W. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85-100). New York: Springer.
48. Steck, G. P. (1958). A table for computing trivariate normal probabilities. Annals of Mathematical Statistics, 29, 780-800.
49. Sympson, J. B. (1983, June). A new IRT model for calibrating multiple choice items. Paper presented at the annual meeting of the Psychometric Society, Los Angeles.
50. Thissen, D. (1982). Marginal maximum likelihood estimation for the one-parameter logistic model. Psychometrika, 47, 175-186.
51. Thissen, D., Nelson, L., Rosa, K., & McLeod, L. D. (2001). Item response theory for items scored in more than two categories. In D. Thissen & H. Wainer (Eds.), Test scoring (Chap. 4, pp. 141-186). Mahwah, NJ: Lawrence Erlbaum Associates.
52. Thissen, D., & Orlando, M. (2001). Item response theory for items scored in two categories. In D. Thissen & H. Wainer (Eds.), Test scoring (Chap. 3, pp. 73-140). Mahwah, NJ: Lawrence Erlbaum Associates.
53. Thissen, D., & Steinberg, L. (1984). A response model for multiple-choice items. Psychometrika, 49, 501-519.
54. Thissen, D., & Steinberg, L. (1986). A taxonomy of item response models. Psychometrika, 51, 567-577.
55. Thissen, D., & Steinberg, L. (1988). Data analysis using item response theory. Psychological Bulletin, 104, 385-395.
56. Thissen, D., Steinberg, L., & Mooney, J. A. (1989). Trace lines for testlets: A use of multiple-categorical-response models. Journal of Educational Measurement, 26, 247-260.
57. Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 278-286.
58. Wainer, H., & Kiely, G. L. (1987). Item clusters and computerized adaptive testing: A case for testlets. Journal of Educational Measurement, 24, 185-201.
59. Wainer, H., Thissen, D., & Sireci, S. G. (1991). DIFferential testlet functioning: Definitions and detection. Journal of Educational Measurement, 28, 197-219.
60. Yao, L., & Schwarz, R. D. (2006). A multidimensional partial credit model with associated item and test statistics: An application to mixed-format tests. Applied Psychological Measurement, 30, 469-492.
61. Yen, W. M. (1993). Scaling performance assessments: Strategies for managing local item dependence. Journal of Educational Measurement, 30, 187-214.