@article {181,
	title = {Optimal and nonoptimal computer-based test designs for making pass-fail decisions},
	journal = {Applied Measurement in Education},
	volume = {19},
	number = {3},
	year = {2006},
	pages = {221-239},
	publisher = {Lawrence Erlbaum: US},
	abstract = {Now that many credentialing exams are being routinely administered by computer, new computer-based test designs, along with item response theory models, are being aggressively researched to identify specific designs that can increase the decision consistency and accuracy of pass-fail decisions. The purpose of this study was to investigate the impact of optimal and nonoptimal multistage test (MST) designs, linear parallel-form test designs (LPFT), and computer adaptive test (CAT) designs on the decision consistency and accuracy of pass-fail decisions. Realistic testing situations matching those of one of the large credentialing agencies were simulated to increase the generalizability of the findings. The conclusions were clear: (a) With the LPFTs, matching test information functions (TIFs) to the mean of the proficiency distribution produced slightly better results than matching them to the passing score; (b) all of the test designs worked better than test construction using random selection of items, subject to content constraints only; (c) CAT performed better than the other test designs; and (d) if matching a TIP to the passing score, the MST design produced a bit better results than the LPFT design. If an argument for the MST design is to be made, it can be made on the basis of slight improvements over the LPFT design and better expected item bank utilization, candidate preference, and the potential for improved diagnostic feedback, compared with the feedback that is possible with fixed linear test forms. (PsycINFO Database Record (c) 2007 APA, all rights reserved)},
	keywords = {adaptive test, credentialing exams, Decision Making, Educational Measurement, multistage tests, optimal computer-based test designs, test form},
	isbn = {0895-7347 (Print); 1532-4818 (Electronic)},
	author = {Hambleton, R. K. and Xing, D.}
}
@inbook {180,
	title = {Applications of item response theory to improve health outcomes assessment: Developing item banks, linking instruments, and computer-adaptive testing},
	booktitle = {Outcomes assessment in cancer},
	year = {2005},
	note = {Using Smart Source ParsingOutcomes assessment in cancer: Measures, methods, and applications. (pp. 445-464). New York, NY : Cambridge University Press. xiv, 662 pp},
	pages = {445-464},
	publisher = {Cambridge University Press},
	organization = {Cambridge University Press},
	address = {Cambridge, UK},
	abstract = {(From the chapter) The current chapter builds on Reise{\textquoteright}s introduction to the basic concepts, assumptions, popular models, and important features of IRT and discusses the applications of item response theory (IRT) modeling to health outcomes assessment. In particular, we highlight the critical role of IRT modeling in: developing an instrument to match a study{\textquoteright}s population; linking two or more instruments measuring similar constructs on a common metric; and creating item banks that provide the foundation for tailored short-form instruments or for computerized adaptive assessments. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Computer Assisted Testing, Health, Item Response Theory, Measurement, Test Construction, Treatment Outcomes},
	author = {Hambleton, R. K.},
	editor = {C. C. Gotay and C. Snyder}
}
@article {179,
	title = {Emergence of item response modeling in instrument development and data analysis},
	journal = {Medical Care},
	volume = {38},
	number = {Suppl. 9},
	year = {2000},
	pages = {II60-II65},
	keywords = {Computer Assisted Testing, Health, Item Response Theory, Measurement, Statistical Validity computerized adaptive testing, Test Construction, Treatment Outcomes},
	author = {Hambleton, R. K.}
}
