Monte Carlo Test Assembly for Item Pool Analysis and Extension

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 fe...

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Bibliographic Details
Published inApplied psychological measurement Vol. 29; no. 4; pp. 239 - 261
Main Authors Belov, Dmitry I., Armstrong, Ronald D.
Format Journal Article
LanguageEnglish
Published Thousand Oaks, CA Sage Publications 01.07.2005
Subjects
Computer Assisted Testing
Data Analysis
Evaluation Methods
Item Banks
Item Response Theory
Monte Carlo Methods
Psychological Studies
Test Items
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Summary: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.
ISSN:0146-6216
1552-3497
DOI:10.1177/0146621605275413