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A Generalized Partial Credit Model: Application of an EM Algorithm

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Muraki, Eiji·1992·Applied Psychological Measurement

Abstract

The partial credit model (PCM) with a varying slope parameter is developed and called the generalized partial credit model (GPCM). The item step parameter of this model is decomposed to a location and a threshold parameter, following Andrich's (1978) rating scale formulation. The EM algorithm for estimating the model parameters is derived. The performance of this generalized model is compared on both simulated and real data to a Rasch family of polytomous item response models. Simulated data were generated and then analyzed by the various polytomous item response models. The results demonstrate that the rating formulation of the GPCM is quite adaptable to the analysis of polytomous item responses. The real data used in this study consisted of the National Assessment of Educational Progress (Johnson & Allen, 1992) mathematics data that used both dichotomous and polytomous items. The PCM was applied to these data using both constant and varying slope parameters. The GPCM, which provides for varying slope parameters, yielded better fit to the data than did the PCM.

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Metadata

Authors
Muraki, E.
Venue
Applied Psychological Measurement
Published
1992-06
Volume
16
Issue
2
Pages
159-176
ISSN
  • 0146-6216 (ISSN-L)
  • 1552-3497
OpenAlex
W2160900637
Cited by
1,535
References
22
FWCI
14.03
Type
journal article
Language
en
Open access
green

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Muraki, E. (1992). A Generalized Partial Credit Model: Application of an EM Algorithm. Applied Psychological Measurement. https://doi.org/10.1177/014662169201600206

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