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)

This paper discusses the use of latent class structure as a modelling framework for tests in which much of the data conforms to a relatively small number of systematic patterns. Application of this framework to the analysis of tests has been limited because available parameter estimation algorithms can only handle a relatively small number of…

The conventional method of measuring ability--based on items with assumed true parameter values obtained from a pretest--is compared to a Bayesian method that deals with the uncertainties of such items. Data from a 1987 American College Testing Program mathematics test indicate that maximum likelihood/Bayesian techniques underestimate uncertainty.…

When the three-parameter logistic model and item response theory are used to analyze Graduate Management Admission Test (GMAT) data, there are problems with the assumption of unidimensionality. Linear factor analytic models, exploratory factor analysis programs, and the comparison of item parameter estimates for heterogeneous and homogeneous…

The partial credit model of G. N. Masters (1982), a one-parameter unidimensional polychotomous Rasch model, was used to reduce the error of measurement, particularly for students near the cut score, and to permit measurement to reflect the actual ability of a student more accurately by reducing the degree of misfit for students near the cut…