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The Equivalence of Two Methods of Parameter Estimation for the Rasch Model

Published online by Cambridge University Press:  01 January 2025

Larry G. Blackwood  and
Edwin L. Bradley
Larry G. Blackwood*
Affiliation:
Department of Biostatistics and Biomathematics, University of Alabama at Birmingham
Edwin L. Bradley
Affiliation:
Department of Biostatistics and Biomathematics, University of Alabama at Birmingham
*
Requests for reprints should be sent to Larry Blackwood, EG&G Idaho, Inc., PO Box 1625, Idaho Falls, ID 83415.
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Abstract

Two methods of estimating parameters in the Rasch model are compared. It is shown that estimates for a certain loglinear model for the score × item × response table are equivalent to the unconditional maximum likelihood estimates for the Rasch model.

Keywords

Rasch modelcontingency tablelatent trait theoryitem response theory
Type
Original Paper
Information
Psychometrika , Volume 54 , Issue 4 , December 1989 , pp. 751 - 754
Copyright
Copyright © 1989 The Psychometric Society

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References

Andersen, E. B. (1980). Discrete statistical models with social science applications, Amsterdam: North Holland.Google Scholar
Kelderman, H. (1984). Loglinear Rasch model tests. Psychometrika, 49, 223–245.CrossRefGoogle Scholar
Mellenbergh, G. J.,& Vijn, P. (1981). The Rasch model as a loglinear model. Applied Psychological Measurement, 5, 369–376.CrossRefGoogle Scholar
Perline, R., Wright, B. D., & Wainer, H. (1979). The Rasch model as additive conjoint measurement. Applied Psychological Measurement, 3, 237–255.CrossRefGoogle Scholar
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests, Kopenhagen: Danish Institute for Educational Research.Google Scholar
Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests, expanded edition, Chicago: University of Chicago Press.Google Scholar
Vijn, P., & Mellenbergh, G. J. (1982). The Rasch model versus the loglinear model (Rèvèsz Berichten No. 42), Amsterdam: Universiteit van Amsterdam, Psychologisch Laboratorium.Google Scholar
Wright, B. D., & Douglas, G. A. (1977). Conditional versus unconditional procedures for sample-free item analysis. Educational and Psychological Measurement, 37, 573–586.CrossRefGoogle Scholar