The partial credit model with a varying slope parameter is developed and called the generalized partial credit model (GPCM). Analysis results for simulated data by this and other polytomous item-response models demonstrate that the rating formulation of the GPCM is adaptable to the analysis of polytomous item responses. (SLD)

This paper presents a study on the generation of mathematics test items using algorithmic methods. The history of this approach is briefly reviewed and is followed by a survey of the research to date on the statistical parallelism of algorithmically generated mathematics items. Results are presented for 8 parallel test forms generated using 16…

The derivation of several item selection algorithms for use in fitting test items to target information functions is described. These algorithms circumvent iterative solutions by using the criteria of moving averages of the distance to a target information function and simultaneously considering an entire range of ability points used to condition…

Estimating item parameters from a two-parameter normal ogive model is considered using Gibbs sampling to simulate draws from the joint posterior distribution of ability and item parameters. The method gives marginal posterior density estimates for any parameter of interest, as illustrated using data from a 33-item mathematics placement…

A two-level model for factor analysis is defined, and formulas for a scoring algorithm for this model are derived. A simple noniterative method based on decomposition of total sums of the squares and cross-products is discussed and illustrated with simulated data and data from the Second International Mathematics Study. (SLD)