Latent trait test models for responses to dichotomously scored items are considered from the point of view of parameter estimation using a Bayesian statistical approach and the EM estimation algorithm. An example using the Rasch model is presented. (Author/JKS)

A maximin model for test design based on item response theory is proposed. Only the relative shape of target test information function is specified. It serves as a constraint subject to which a linear programing algorithm maximizes the test information. The model is illustrated, and alternative models are discussed. (TJH)

The practicality of using the EM algorithm for maximum likelihood estimation of item parameters in the marginal distribution is presented. The EM procedure is shown to apply to general item-response models. (Author/JKS)

Two algorithms for marginal maximum likelihood estimation for the Rasch model are provided. The more efficient of the two algorithms is extended to estimation for the linear logistic model. Numerical examples of both procedures are presented. (Author/JKS)

A general approach for analyzing rating data with latent class models is described, paralleling rating models in the framework of latent trait theory. A general rating model and a two-parameter model with location and dispersion parameters are derived and illustrated. (Author/SLD)

Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. (Author/LMO)

The Bock and Aitkin Marginal Maximum Likelihood/EM (MML/EM) approach to item parameter estimation is an alternative to the classical joint maximum likelihood procedure of item response theory. This paper provides the essential mathematical details of a MML/EM solution and shows its use in obtaining consistent item parameter estimates. (TJH)

A simple probabilistic model for unfolding data collected by a direct response design in which responses were scored dichotomously was applied to the measurement of attitudes toward capital punishment. Responses conformed to the unfolding mechanism. Scale values of the statements were statistically equivalent to those of Thurstone's methods. (SLD)

Maximum likelihood procedures are presented for a general model to unify the various models and techniques that have been proposed for item analysis. Unconditional maximum likelihood estimation, proposed by Wright and Haberman, and conditional maximum likelihood estimation, proposed by Rasch and Andersen, are shown as important special cases. (JAZ)

This article describes a Bayesian framework for estimation in item response models, with two-stage distributions on both item and examinee populations. Strategies for point and interval estimation are discussed, and a general procedure based on the EM algorithm is presented. (Author/LMO)

A latent class model for handling the items in Birenbaum and Tatsuoka's study is described. A method to derive the optimal scoring rule when multiple choice test items are used is illustrated. Remedial training begins after a determination is made as to which of several erroneous algorithms is being used. (Author/DWH)

Some examinees' test-taking behavior may be so idiosyncratic that their test scores are not comparable to those of more typical examinees. A new theoretical approach to appropriateness measurement is proposed that specifies a likelihood ratio test and an efficient computer algorithm for computing the test statistic. (TJH)

New theory and practice in testing is replacing the standard test by the test item bank and classical test theory by item response theory. Eight papers and a commentary are presented in this special issue concerning test item banking. (SLD)

A newly introduced approach, rule space, can represent large numbers of erroneous rules of arithmetic operations quantitatively and can predict the likelihood of each erroneous rule. The new model challenges the credibility of the traditional right-or-wrong scoring procedure. (Author/PN)

A probabilistic item response theory (IRT) model is developed for pair-comparison design in which the unfolding principle governing the choice process uses a discriminant process analogous to Thurstone's Law of Comparative Judgment. A simulation study demonstrates the feasibility of estimation, and two examples illustrate the implications for…