General Test Overlap Control: Improved Algorithm for CAT and CCT

Shu Ying Chen, Pui Wa Lei, Jyun Hong Chen, Tzu Chen Liu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This article proposed a new online test overlap control algorithm that is an improvement of Chen's algorithm in controlling general test overlap rate for item pooling among a group of examinees. Chen's algorithm is not very efficient in that not only item pooling between current examinee and prior examinees is controlled for but also item pooling between previous examinees, which would have been controlled for when they were current examinees. The proposed improvement increases efficiency by only considering item pooling between current and previous examinees, and its improved performance over Chen is demonstrated in a simulated computerized adaptive testing (CAT) environment. Moreover, the proposed algorithm is adapted for computerized classification testing (CCT) using the sequential probability ratio test procedure and is evaluated against some existing exposure control procedures. The proposed algorithm appears to work best in controlling general test overlap rate among the exposure control procedures examined without sacrificing much classification precision, though longer tests might be required for more stringent control of item pooling among larger groups. Given the capability of the proposed algorithm in controlling item pooling among a group of examinees of any size and its ease of implementation, it appears to be a good test overlap control method.

Original languageEnglish (US)
Pages (from-to)229-244
Number of pages16
JournalApplied Psychological Measurement
Issue number3
StatePublished - May 2014

All Science Journal Classification (ASJC) codes

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)


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