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== Item selection ==
In a CCT, items are selected for administration throughout the test, unlike the traditional method of administering a fixed set of items to all examinees. While this is usually done by individual item, it can also be done in groups of items known as [[testlet]]s (Leucht & Nungester, 1996;<ref>{{cite journal|last1=Luecht|first1=R. M.|last2=Nungester|first2=R. J.|year=1998|title=Some practical examples of computer-adaptive sequential testing|journal=Journal of Educational Measurement|volume=35|pages=229-249|doi=10.1111/j.1745-3984.1998.tb00537.x}}</ref> Vos & Glas, 2000<ref>{{cite book|last1=Vos|first1=H.J.|last2=Glas|first2=C.A.W.|year=2000|chapter=Testlet-based adaptive mastery testing|editor1-last=van der Linden|editor1-first=W.J.|editor2-last=Glas|editor2-first=C.A.W.|title=Computerized Adaptive Testing: Theory and Practice|url=https://archive.org/details/computerizedadap0000unse_b0n1|doi=10.1007/0-306-47531-6_15}}</ref>).
Methods of item selection fall into two categories: cutscore-based and estimate-based. Cutscore-based methods (also known as sequential selection) maximize the [[quantities of information|information]] provided by the item at the cutscore, or cutscores if there are more than one, regardless of the ability of the examinee. Estimate-based methods (also known as adaptive selection) maximize information at the current estimate of examinee ability, regardless of the ___location of the cutscore. Both work efficiently, but the efficiency depends in part on the termination criterion employed. Because the [[sequential probability ratio test]] only evaluates probabilities near the cutscore, cutscore-based item selection is more appropriate. Because the [[confidence interval]] termination criterion is centered around the examinees ability estimate, estimate-based item selection is more appropriate. This is because the test will make a classification when the confidence interval is small enough to be completely above or below the cutscore (see below). The confidence interval will be smaller when the standard error of measurement is smaller, and the standard error of measurement will be smaller when there is more information at the theta level of the examinee.
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* {{Cite journal |last=Vos |first=H.J. |year=1998 |title=Optimal sequential rules for computer-based instruction |journal=Journal of Educational Computing Research |volume=19 |pages=133–154}}
* {{Cite journal |last=Vos |first=H.J. |year=1999 |title=Applications of Bayesian decision theory to sequential mastery testing |journal=Journal of Educational and Behavioral Statistics |volume=24 |pages=271–292}}
* {{Cite book |last=Wald |first=A. |year=1947 |title=Sequential analysis |url=https://archive.org/details/in.ernet.dli.2015.90255 |___location=New York |publisher=Wiley}}
* {{Cite journal |last=Weiss |first=D.J. |last2=Kingsbury |first2=G.G. |year=1984 |title=Application of computerized adaptive testing to educational problems |journal=Journal of Educational Measurement |volume=21 |pages=361–375}}
* {{Cite conference |last=Weissman |first=A. |year=2004 |title=Mutual information item selection in multiple-category classification CAT |conference=Annual Meeting of the National Council for Measurement in Education |___location=San Diego, CA}}
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