@article {29,
	title = {Developing tailored instruments: item banking and computerized adaptive assessment},
	journal = {Quality of Life Research},
	volume = {16},
	number = {Suppl 1},
	year = {2007},
	note = {Bjorner, Jakob BueChang, Chih-HungThissen, DavidReeve, Bryce B1R43NS047763-01/NS/United States NINDSAG015815/AG/United States NIAResearch Support, N.I.H., ExtramuralNetherlandsQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitationQual Life Res. 2007;16 Suppl 1:95-108. Epub 2007 Feb 15.},
	pages = {95-108},
	edition = {2007/05/29},
	abstract = {Item banks and Computerized Adaptive Testing (CAT) have the potential to greatly improve the assessment of health outcomes. This review describes the unique features of item banks and CAT and discusses how to develop item banks. In CAT, a computer selects the items from an item bank that are most relevant for and informative about the particular respondent; thus optimizing test relevance and precision. Item response theory (IRT) provides the foundation for selecting the items that are most informative for the particular respondent and for scoring responses on a common metric. The development of an item bank is a multi-stage process that requires a clear definition of the construct to be measured, good items, a careful psychometric analysis of the items, and a clear specification of the final CAT. The psychometric analysis needs to evaluate the assumptions of the IRT model such as unidimensionality and local independence; that the items function the same way in different subgroups of the population; and that there is an adequate fit between the data and the chosen item response models. Also, interpretation guidelines need to be established to help the clinical application of the assessment. Although medical research can draw upon expertise from educational testing in the development of item banks and CAT, the medical field also encounters unique opportunities and challenges.},
	keywords = {*Health Status, *Health Status Indicators, *Mental Health, *Outcome Assessment (Health Care), *Quality of Life, *Questionnaires, *Software, Algorithms, Factor Analysis, Statistical, Humans, Models, Statistical, Psychometrics},
	isbn = {0962-9343 (Print)},
	author = {Bjorner, J. B. and Chang, C-H. and Thissen, D. and Reeve, B. B.}
}
@article {305,
	title = {Assessing tobacco beliefs among youth using item response theory models},
	journal = {Drug and Alcohol Dependence},
	volume = {68},
	number = {Suppl 1},
	year = {2002},
	note = {0376-8716Journal Article},
	month = {Nov},
	pages = {S21-S39},
	abstract = {Successful intervention research programs to prevent adolescent smoking require well-chosen, psychometrically sound instruments for assessing smoking prevalence and attitudes. Twelve thousand eight hundred and ten adolescents were surveyed about their smoking beliefs as part of the Teenage Attitudes and Practices Survey project, a prospective cohort study of predictors of smoking initiation among US adolescents. Item response theory (IRT) methods are used to frame a discussion of questions that a researcher might ask when selecting an optimal item set. IRT methods are especially useful for choosing items during instrument development, trait scoring, evaluating item functioning across groups, and creating optimal item subsets for use in specialized applications such as computerized adaptive testing. Data analytic steps for IRT modeling are reviewed for evaluating item quality and differential item functioning across subgroups of gender, age, and smoking status. Implications and challenges in the use of these methods for tobacco onset research and for assessing the developmental trajectories of smoking among youth are discussed.},
	keywords = {*Attitude to Health, *Culture, *Health Behavior, *Questionnaires, Adolescent, Adult, Child, Female, Humans, Male, Models, Statistical, Smoking/*epidemiology},
	author = {Panter, A. T. and Reeve, B. B.}
}
