Optimal stratification of item pools in α-stratified computerized adaptive testing

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A method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in α-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous item pool from the computerized adaptive testing (CAT) version of the Graduate Record Exams (GRE) Quantitative Test. The results indicate that the new method performs well in practical situations. It improves item exposure control, reduces the mean squared error in the O estimates, and increases test reliability. Index terms: computerized adaptive testing, α-stratified adaptive testing, item pool stratification, 0-1 linear programming.
Original languageUndefined
Pages (from-to)262-274
Number of pages13
JournalApplied psychological measurement
Issue number4
Publication statusPublished - 2003


  • METIS-215786
  • IR-60155

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