Hybrid Computerized Adaptive Testing: From Group Sequential Design to Fully Sequential Design

Shiyu Wang, Haiyan Lin, Hua Hua Chang, Jeff Douglas

Research output: Contribution to journalArticlepeer-review


Computerized adaptive testing (CAT) and multistage testing (MST) have become two of the most popular modes in large-scale computer-based sequential testing.  Though most designs of CAT and MST exhibit strength and weakness in recent large-scale implementations, there is no simple answer to the question of which design is better because different modes may fit different practical situations. This article proposes a hybrid adaptive framework to combine both CAT and MST, inspired by an analysis of the history of CAT and MST. The proposed procedure is a design which transitions from a group sequential design to a fully sequential design. This allows for the robustness of MST in early stages, but also shares the advantages of CAT in later stages with fine tuning of the ability estimator once its neighborhood has been identified. Simulation results showed that hybrid designs following our proposed principles provided comparable or even better estimation accuracy and efficiency than standard CAT and MST designs, especially for examinees at the two ends of the ability range.

Original languageEnglish (US)
Pages (from-to)45-62
Number of pages18
JournalJournal of Educational Measurement
Issue number1
StatePublished - Mar 1 2016

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Psychology
  • Psychology (miscellaneous)


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