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Sufficiency and Conditional Estimation of Person Parameters in the Polytomous Rasch Model

Published online by Cambridge University Press:  01 January 2025

David Andrich*
Affiliation:
The University of Western Australia
*
Requests for reprints should be sent to David Andrich, Graduate School of Education, The University of Western Australia, Crawley, Western Australia 6009, Australia. E-mail: david.andrich@uwa.edu.au

Abstract

Rasch models are characterised by sufficient statistics for all parameters. In the Rasch unidimensional model for two ordered categories, the parameterisation of the person and item is symmetrical and it is readily established that the total scores of a person and item are sufficient statistics for their respective parameters. In contrast, in the unidimensional polytomous Rasch model for more than two ordered categories, the parameterisation is not symmetrical. Specifically, each item has a vector of item parameters, one for each category, and each person only one person parameter. In addition, different items can have different numbers of categories and, therefore, different numbers of parameters. The sufficient statistic for the parameters of an item is itself a vector. In estimating the person parameters in presently available software, these sufficient statistics are not used to condition out the item parameters. This paper derives a conditional, pairwise, pseudo-likelihood and constructs estimates of the parameters of any number of persons which are independent of all item parameters and of the maximum scores of all items. It also shows that these estimates are consistent. Although Rasch’s original work began with equating tests using test scores, and not with items of a test, the polytomous Rasch model has not been applied in this way. Operationally, this is because the current approaches, in which item parameters are estimated first, cannot handle test data where there may be many scores with zero frequencies. A small simulation study shows that, when using the estimation equations derived in this paper, such a property of the data is no impediment to the application of the model at the level of tests. This opens up the possibility of using the polytomous Rasch model directly in equating test scores.

Type
Original Paper
Copyright
Copyright © 2010 The Psychometric Society

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References

Andersen, E.B. (1977). Sufficient statistics and latent trait models. Psychometrika, 42, 6981.CrossRefGoogle Scholar
Anderson, C.J., Li, Z., & Vermunt, J.K. (2007). Estimation of models in a Rasch family for polytomous items and multiple latent variables. Journal of Statistical Software, 20(6), 136.CrossRefGoogle Scholar
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561574.CrossRefGoogle Scholar
Andrich, D. (1995). Models for measurement, precision and the non-dichotomization of graded responses. Psychometrika, 60, 726.CrossRefGoogle Scholar
Andrich, D., & Luo, G. (2003). Conditional estimation in the Rasch model for ordered response categories using principal components. Journal of Applied Measurement, 4, 205221.Google ScholarPubMed
Andrich, D., Sheridan, B., & Luo, G. (2008). RUMM2020. Perth, Australia: RUMM Laboratory.Google Scholar
Bock, R.D., & Jones, L.V. (1968). The measurement and prediction of judgement and choice, San Francisco: Holden Day.Google Scholar
Bock, R.D., & Mislevy, R.J. (1982). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6, 431444.CrossRefGoogle Scholar
Choppin, B. (1968). An item bank using sample-free calibration. Nature, 219, 870872.CrossRefGoogle ScholarPubMed
Fischer, G.H., & Molenaar, I.W. (1995). Rasch models: foundations, recent developments, and applications, New York: Springer.CrossRefGoogle Scholar
Fisher, R.A. (1934). Two new properties of mathematical likelihood. Proceedings of Royal Society, A, 144, 285307.Google Scholar
Jansen, P.G.W., & Roskam, E.E. (1986). Latent trait models and dichotomization of graded responses. Psychometrika, 51(1), 6991.CrossRefGoogle Scholar
Leunbach, G. (1976). A probabilistic measurement model for assessing whether tests measure the same personal factor. Danish Institute for Educational Research. Unpublished paper.Google Scholar
Masters, G.N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149174.CrossRefGoogle Scholar
Masters, G.N., & Wright, B.D. (1984). The essential process in a family of measurement models. Psychometrika, 49, 529544.CrossRefGoogle Scholar
Mood, A.M., Graybill, F.A., & Boes, D.C. (1974). Introduction to the theory of statistics, (3rd ed.). Yokyo: McGraw Hill.Google Scholar
Noack, A. (1950). A class of random variables with discrete distributions. Annals of Mathematical Statistics, 21, 127132.CrossRefGoogle Scholar
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests, Chicago: The University of Chicago Press. (Copenhagen, Danish Institute for Educational Research). Reprinted Chicago: MESA Press (1993). Expanded edition (1980) with foreword and afterword by B.D. Wright (1980)Google Scholar
Rasch, G. (1961). On general laws and the meaning of measurement in psychology. In Neyman, J. (Eds.), Proceedings of the fourth Berkeley symposium on mathematical statistics and probability. IV (pp. 321334). Berkeley: University of California Press.Google Scholar
Rasch, G. (1966). An individualistic approach to item analysis. In Lazarsfeld, P.F., & Henry, N.W. (Eds.), Readings in mathematical social science (pp. 89108). Chicago: Science Research Associates.Google Scholar
Warm, T.A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427450.CrossRefGoogle Scholar
Wilson, M., & Masters, G.N. (1993). The partial credit model and null categories. Psychometrika, 58, 8799.CrossRefGoogle Scholar
Wright, B.D., & Masters, G.N. (1982). Rating scale analysis: Rasch measurement, Chicago: MESA Press.Google Scholar
Wu, M.L., Adams, R.J., Wilson, M.R., & Haldane, S.A. (2007). ACERConQuest Version 2: Generalised item response modelling software, Camberwell: Australian Council for Educational Research. (Computer program)Google Scholar
Zwinderman, A.H. (1995). Pairwise parameter estimation in Rasch models. Applied Psychological Measurement, 19(4), 369375.CrossRefGoogle Scholar