Stan is a flexible probabilistic programming language providing full Bayesian inference through Hamiltonian Monte Carlo algorithms. The benefits of Hamiltonian Monte Carlo include improved efficiency and faster inference, when compared to other MCMC software implementations. Users can interface with Stan through a variety of computing…

Optimal test-design methods are applied to rule-based item generation. Three different cases of automated test design are presented: (a) test assembly from a pool of pregenerated, calibrated items; (b) test generation on the fly from a pool of calibrated item families; and (c) test generation on the fly directly from calibrated features defining…

A multiobjective programming method is proposed for determining samples of examinees needed for estimating the parameters of a group of items. This approach maximizes the information functions of each of three parameters. A numerical verification of the procedure is presented. (SLD)

A set of linear conditions on the item response functions is derived that guarantees identical observed-score distributions on two test forms. The conditions can be added as constraints to a linear programming model for test assembly that assembles a new test form to have an observed-score distribution optimally equated to the distribution of the…

Derives a set of linear conditions of item-response functions that guarantees identical observed-score distributions on two test forms. The conditions can be added as constraints to a linear programming model for test assembly. An example illustrates the use of the model for an item pool from the Law School Admissions Test (LSAT). (SLD)

Item response theory (IRT) has become one of the most popular scoring frameworks for measurement data. IRT models are used frequently in computerized adaptive testing, cognitively diagnostic assessment, and test equating. This article reviews two of the most popular software packages for IRT model estimation, BILOG-MG (Zimowski, Muraki, Mislevy, &…

An optimization model is presented that allows test assemblers to control the shape of the observed-score distribution on a test for a population with a known ability distribution. An obvious application is for item response theory-based test assembly in programs where observed scores are reported and operational test forms are required to produce…