Online Calibration Methods for the DINA Model with Independent Attributes in CD-CAT

Ping Chen, Tao Xin, Chun Wang, Hua Hua Chang

Research output: Contribution to journalArticlepeer-review


Item replenishing is essential for item bank maintenance in cognitive diagnostic computerized adaptive testing (CD-CAT). In regular CAT, online calibration is commonly used to calibrate the new items continuously. However, until now no reference has publicly become available about online calibration for CD-CAT. Thus, this study investigates the possibility to extend some current strategies used in CAT to CD-CAT. Three representative online calibration methods were investigated: Method A (Stocking in Scale drift in on-line calibration. Research Rep. 88-28, 1988), marginal maximum likelihood estimate with one EM cycle (OEM) (Wainer & Mislevy In H. Wainer (ed.) Computerized adaptive testing: A primer, pp. 65-102, 1990) and marginal maximum likelihood estimate with multiple EM cycles (MEM) (Ban, Hanson, Wang, Yi, & Harris in J. Educ. Meas. 38:191-212, 2001). The objective of the current paper is to generalize these methods to the CD-CAT context under certain theoretical justifications, and the new methods are denoted as CD-Method A, CD-OEM and CD-MEM, respectively. Simulation studies are conducted to compare the performance of the three methods in terms of item-parameter recovery, and the results show that all three methods are able to recover item parameters accurately and CD-Method A performs best when the items have smaller slipping and guessing parameters. This research is a starting point of introducing online calibration in CD-CAT, and further studies are proposed for investigations such as different sample sizes, cognitive diagnostic models, and attribute-hierarchical structures.

Original languageEnglish (US)
Pages (from-to)201-222
Number of pages22
Issue number2
StatePublished - Apr 2012


  • cognitive diagnostic computerized adaptive testing
  • DINA model
  • independent attribute
  • new item
  • online calibration

ASJC Scopus subject areas

  • General Psychology
  • Applied Mathematics


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