A GENERALIZED PARTIAL CREDIT MODEL: APPLICATION OF AN EM ALGORITHM

The Partial Credit model with a varying slope parameter has been developed, and it is called the Generalized Partial Credit model. The item step parameter of this model is decomposed to a location and a threshold parameter, following Andrich's Rating Scale formulation. The EM algorithm for estimating the model parameters was derived. The performance of this generalized model is compared with a Rasch family of polytomous item response models based on both simulated and real data. Simulated data were generated and then analyzed by the various polytomous item response models. The results obtained demonstrate that the rating formulation of the Generalized Partial Credit model is quite adaptable to the analysis of polytomous item responses. The real data used in this study consisted of NAEP Mathematics data which was made up of both dichotomous and polytomous item types. The Partial Credit model was applied to this data using both constant and varying slope parameters. The Generalized Partial Credit model, which provides for varying slope parameters, yielded better fit to data than the Partial Credit model without such a provision. Index terms: item response model polytomous item response model the Partial Credit model the Rating Scale model the Nominal Response model NAEP

[1]  M. Kendall,et al.  The advanced theory of statistics , 1945 .

[2]  Melvin R. Novick,et al.  Some latent train models and their use in inferring an examinee's ability , 1966 .

[3]  A. Stroud,et al.  Gaussian quadrature formulas , 1966 .

[4]  F. Samejima Estimation of latent ability using a response pattern of graded scores , 1968 .

[5]  R. Darrell Bock Estimating item parameters and latent ability when responses are scored in two or more nominal categories , 1972 .

[6]  F. Samejima A General Model for Free Response Data. , 1972 .

[7]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[8]  D. Andrich A rating formulation for ordered response categories , 1978 .

[9]  B. Wright,et al.  Best test design , 1979 .

[10]  F. Lord Applications of Item Response Theory To Practical Testing Problems , 1980 .

[11]  R. D. Bock,et al.  Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm , 1981 .

[12]  David Andrich,et al.  An extension of the rasch model for ratings providing both location and dispersion parameters , 1982 .

[13]  G. Masters A rasch model for partial credit scoring , 1982 .

[14]  David Thissen,et al.  A taxonomy of item response models , 1986 .

[15]  D. Andrich A General Form of Rasch's Extended Logistic Model for Partial Credit Scoring , 1988 .

[16]  E. Muraki,et al.  Full-Information Item Factor Analysis , 1988 .

[17]  Eiji Muraki,et al.  Fitting a Polytomous Item Response Model to Likert-Type Data , 1990 .

[18]  Robert J. Mislevy,et al.  BILOG 3 : item analysis and test scoring with binary logistic models , 1990 .

[19]  SOME EMPIRICAL GUIDELINES FOR BUILDING TESTLETS1 , 1991 .

[20]  Eiji Muraki RESGEN ITEM RESPONSE GENERATOR , 1992 .