@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}
}
