a-Stratified CAT design with content blocking

Qing Yi, Hua Hua Chang

Research output: Contribution to journalArticlepeer-review


Content balancing is often required in the development and implementation of computerized adaptive tests (CATs). In the current study, we propose a modified a-stratified method, the a-stratified method with content blocking. As a further refinement of a-stratified CAT designs, the new method incorporates content specifications into item pool stratification. Simulation studies were conducted to compare the new method with three previous item selection methods: the a-stratified method; the a-stratified with b-blocking method; and the maximum Fisher information method with Sympson-Hetter exposure control. The results indicated that the refined a-stratified design performed well in reducing item overexposure rates, balancing item usage within the pool, and maintaining measurement precision, in a situation where all four procedures were forced to balance content.

Original languageEnglish (US)
Pages (from-to)359-378
Number of pages20
JournalBritish Journal of Mathematical and Statistical Psychology
Issue number2
StatePublished - Nov 2003
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Arts and Humanities (miscellaneous)
  • Psychology(all)


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