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Errata for “Nonparametric Goodness-of-Fit Tests for the Rasch Model”

Published online by Cambridge University Press:  01 January 2025

Ivo Ponocny
Ivo Ponocny*
Affiliation:
University of Vienna
*
Requests for reprints should be sent to Ivo Ponocny, Institut für Psychologie, Universität Wien, Liebiggasse 5, A-1010 Wien, AUSTRIA. E-Mail: [email protected]
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Abstract

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A Monte Carlo-algorithm realizing a family of nonpaxametric tests for the Rasch model is introduced which are conditional on the item and subject marginals. The algorithm is based on random changes of elements of data matrices without changing the maxginals; most powerful tests against all alternative hypotheses are given for which a monotone characteristic may be computed from the data matrix; alternatives may also be composed. Computation times are long, but exact p-values are approximated with the quality of approximation only depending on calculation time, but not on the number of persons. The power and the flexibility of the procedure is demonstrated by means of an empirical example where, among others, indicators for increased item similarities, the existence of subscales, violations of sufficiency of the raw score as well as learning processes were found. Many of the features described are implemented in the program T-Rasch 1.0 by Ponocny and Ponocny-Seliger (1999).

Keywords

nonpaxametric testsIRTgoodness-of-fit testsRasch modelexact tests
Type
Erratum
Information
Psychometrika , Volume 67 , Issue 2 , June 2002 , pp. 315
Copyright
Copyright © 2002 The Psychometric Society

Footnotes

The author wishes to thank Gerhard Fischer, Steffen Lauritzen and Tom Snijders for useful information.

References

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Ponocny, I. (1996). Kombinatorische Modelltestfar das Raschmodell [Combinatorial goodness-of-fit tests for the Rasch model]. Unpublished doctoral dissertation, University of Vienna, Austria.Google Scholar
Ponocny, I. (2001). Nonparametric goodness-of-fit tests for the Rasch model. Psychometrika, 66, 437-460.CrossRefGoogle Scholar
Rao, A.R., Jana, R., & Bandyopadhyay, S. (1996). A Markov chain Monte Carlo method for generating random (0, 1)- matrices with given marginals. Sankhya: The Indian Journal of Statistics, Series A, 58(2), 225-242.Google Scholar
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