@article {2151, title = {A Monte Carlo Approach for Adaptive Testing With Content Constraints}, journal = {Applied Psychological Measurement}, volume = {32}, number = {6}, year = {2008}, pages = {431-446}, abstract = {

This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the item pool, and (c) more robust ability estimates. Computer simulations with Law School Admission Test items demonstrated that the new algorithm (a) produces similar ability estimates as shadow CAT but with half the maximum item exposure rate and 100\% pool utilization and (b) produces more robust estimates when a high- (or low-) ability examinee performs poorly (or well) at the beginning of the test.

}, doi = {10.1177/0146621607309081}, url = {http://apm.sagepub.com/content/32/6/431.abstract}, author = {Belov, Dmitry I. and Armstrong, Ronald D. and Weissman, Alexander} } @article {2150, title = {A Monte Carlo Approach to the Design, Assembly, and Evaluation of Multistage Adaptive Tests}, journal = {Applied Psychological Measurement}, volume = {32}, number = {2}, year = {2008}, pages = {119-137}, abstract = {

This article presents an application of Monte Carlo methods for developing and assembling multistage adaptive tests (MSTs). A major advantage of the Monte Carlo assembly over other approaches (e.g., integer programming or enumerative heuristics) is that it provides a uniform sampling from all MSTs (or MST paths) available from a given item pool. The uniform sampling allows a statistically valid analysis for MST design and evaluation. Given an item pool, MST model, and content constraints for test assembly, three problems are addressed in this study. They are (a) the construction of item response theory (IRT) targets for each MST path, (b) the assembly of an MST such that each path satisfies content constraints and IRT constraints, and (c) an analysis of the pool and constraints to increase the number of nonoverlapping MSTs that can be assembled from the pool. The primary intent is to produce reliable measurements and enhance pool utilization.

}, doi = {10.1177/0146621606297308}, url = {http://apm.sagepub.com/content/32/2/119.abstract}, author = {Belov, Dmitry I. and Armstrong, Ronald D.} } @article {2142, title = {Monte Carlo Test Assembly for Item Pool Analysis and Extension}, journal = {Applied Psychological Measurement}, volume = {29}, number = {4}, year = {2005}, pages = {239-261}, abstract = {

A new test assembly algorithm based on a Monte Carlo random search is presented in this article. A major advantage of the Monte Carlo test assembly over other approaches (integer programming or enumerative heuristics) is that it performs a uniform sampling from the item pool, which provides every feasible item combination (test) with an equal chance of being built during an assembly. This allows the authors to address the following issues of pool analysis and extension: compare the strengths and weaknesses of different pools, identify the most restrictive constraint(s) for test assembly, and identify properties of the items that should be added to a pool to achieve greater usability of the pool. Computer experiments with operational pools are given.

}, doi = {10.1177/0146621605275413}, url = {http://apm.sagepub.com/content/29/4/239.abstract}, author = {Belov, Dmitry I. and Armstrong, Ronald D.} }