Rasch models for partial-credit scoring are discussed
and a multidimensional version of the model is formulated.
A model may be specified in which consecutive
item responses depend on an underlying latent trait. In
the multidimensional partial-credit model, different responses
may be explained by different latent traits. Data
from van Kuyk’s (1988) size concept test and the Raven
Progressive Matrices test were analyzed. Maximum
likelihood estimation and goodness-of-fit testing are discussed
and applied to these datasets. Goodness-of-fit
statistics show that for both tests, multidimensional partial-credit models were more appropriate than the unidimensional
partial-credit model. Index terms: X2 testing,
exponential family model, multidimensional item response
theory, multidimensional Rasch model, partial-credit
models, Progressive Matrices test, Rasch model.