Presents a variation of a "greedy" algorithm that can be used in test-assembly problems. The algorithm, the normalized weighted absolute-deviation heuristic, selects items to have a locally optimal fit to a moving set of average criterion values. Demonstrates application of the model. (SLD)

Reviews optimal test-assembly literature and introduces the contributions to this special issue. Discusses four approaches to computerized test assembly: (1) heuristic-based test assembly; (2) 0-1 linear programming; (3) network-flow programming; and (4) an optimal design approach. Contains a bibliography of 90 sources on test assembly.…

Although cognitive diagnostic models (CDMs) can be useful in the analysis and interpretation of existing tests, little has been developed to specify how one might construct a good test using aspects of the CDMs. This article discusses the derivation of a general CDM index based on Kullback-Leibler information that will serve as a measure of how…

A method of automatically selecting items for inclusion in a test with constraints on item content and statistical properties was applied to real data. Tests constructed manually from the same data and constraints were compared to tests constructed automatically. Results show areas in which automated assembly can improve test construction. (SLD)

Used the multicomponent latent trait model (MTLM) (S. Embretson, 1980, 1984) for 3 different synonym tasks completed by 212 and 257 Belgian secondary school students. Developed a heuristic evaluation procedure that tested features of the model and provided an explanation for why the MTLM did not fit the data. (SLD)

When the network-flow algorithm (NFA) and the average growth approximation algorithm (AGAA) were used for automated test assembly with American College Test and Armed Services Vocational Aptitude Battery item banks, results indicate that reasonable error in item parameters is not harmful for test assembly using NFA or AGAA. (SLD)

Two methods are proposed for the construction of weakly parallel tests based on a prespecified information function. A method is then described for selecting weakly parallel tests that are optimal with respect to the Maximin criterion. Numerical examples demonstrate the practicality of the tests. (SLD)

A model for solving very large item selection problems is presented. The model builds on binary programming applied to test construction. A heuristic for selecting items that satisfy the constraints in the model is also presented, and various problems are solved using the model and heuristic. (SLD)

Three heuristic methods of assessing the dimensionality of binary item pools were evaluated in a Monte Carlo investigation. The indices were based on (1) the local independence of unidimensional tests; (2) patterns of second-factor loadings derived from simplex theory; and (3) the shape of the curve of successive eigenvalues. (SLD)

To determine which produced the most accurate item parameter estimates, four methods of item response theory were evaluated: (1) heuristic estimates; (2) the ANCILLES program; (3) the LOGIST program; and (4) the ASCAL program. LOGIST and ASCAL produced estimates of superior and essentially equivalent accuracy. (SLD)

A generalized variance criterion is proposed to measure efficiency in item-response-theory (IRT) models. Heuristic arguments are given to formulate the efficiency of a design in terms of an asymptotic generalized variance criterion. Efficiencies of designs for one-, two-, and three-parameter models are compared. (SLD)