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

Autor/inn/en	Chen, Ping; Xin, Tao; Wang, Chun; Chang, Hua-Hua
Titel	Online Calibration Methods for the DINA Model with Independent Attributes in CD-CAT
Quelle	In: Psychometrika, 77 (2012) 2, S.201-222 (22 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-012-9255-7
Schlagwörter	Computer Assisted Testing; Adaptive Testing; Diagnostic Tests; Cognitive Tests; Item Banks; Test Items; Maximum Likelihood Statistics; Comparative Analysis + Suchen Sie Ihr Suchwort? Diagnostic test; Diagnostischer Test; Kognitiver Fähigkeitstest; Test content; Testaufgabe
Abstract	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. (As Provided).
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Update	2017/4/10