@article {241,
	title = {Binary items and beyond: a simulation of computer adaptive testing using the Rasch partial credit model},
	journal = {Journal of Applied Measurement},
	volume = {9},
	number = {1},
	year = {2008},
	note = {Lange, RenseUnited StatesJournal of applied measurementJ Appl Meas. 2008;9(1):81-104.},
	pages = {81-104},
	edition = {2008/01/09},
	abstract = {Past research on Computer Adaptive Testing (CAT) has focused almost exclusively on the use of binary items and minimizing the number of items to be administrated. To address this situation, extensive computer simulations were performed using partial credit items with two, three, four, and five response categories. Other variables manipulated include the number of available items, the number of respondents used to calibrate the items, and various manipulations of respondents{\textquoteright} true locations. Three item selection strategies were used, and the theoretically optimal Maximum Information method was compared to random item selection and Bayesian Maximum Falsification approaches. The Rasch partial credit model proved to be quite robust to various imperfections, and systematic distortions did occur mainly in the absence of sufficient numbers of items located near the trait or performance levels of interest. The findings further indicate that having small numbers of items is more problematic in practice than having small numbers of respondents to calibrate these items. Most importantly, increasing the number of response categories consistently improved CAT{\textquoteright}s efficiency as well as the general quality of the results. In fact, increasing the number of response categories proved to have a greater positive impact than did the choice of item selection method, as the Maximum Information approach performed only slightly better than the Maximum Falsification approach. Accordingly, issues related to the efficiency of item selection methods are far less important than is commonly suggested in the literature. However, being based on computer simulations only, the preceding presumes that actual respondents behave according to the Rasch model. CAT research could thus benefit from empirical studies aimed at determining whether, and if so, how, selection strategies impact performance.},
	keywords = {*Data Interpretation, Statistical, *User-Computer Interface, Educational Measurement/*statistics \& numerical data, Humans, Illinois, Models, Statistical},
	isbn = {1529-7713 (Print)1529-7713 (Linking)},
	author = {Lange, R.}
}
@article {86,
	title = {IRT health outcomes data analysis project: an overview and summary},
	journal = {Quality of Life Research},
	volume = {16},
	number = {Suppl. 1},
	year = {2007},
	note = {Cook, Karon FTeal, Cayla RBjorner, Jakob BCella, DavidChang, Chih-HungCrane, Paul KGibbons, Laura EHays, Ron DMcHorney, Colleen AOcepek-Welikson, KatjaRaczek, Anastasia ETeresi, Jeanne AReeve, Bryce B1U01AR52171-01/AR/United States NIAMSR01 (CA60068)/CA/United States NCIY1-PC-3028-01/PC/United States NCIResearch 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 1:121-32. Epub 2007 Mar 10.},
	pages = {121-132},
	edition = {2007/03/14},
	abstract = {BACKGROUND: In June 2004, the National Cancer Institute and the Drug Information Association co-sponsored the conference, "Improving the Measurement of Health Outcomes through the Applications of Item Response Theory (IRT) Modeling: Exploration of Item Banks and Computer-Adaptive Assessment." A component of the conference was presentation of a psychometric and content analysis of a secondary dataset. OBJECTIVES: A thorough psychometric and content analysis was conducted of two primary domains within a cancer health-related quality of life (HRQOL) dataset. RESEARCH DESIGN: HRQOL scales were evaluated using factor analysis for categorical data, IRT modeling, and differential item functioning analyses. In addition, computerized adaptive administration of HRQOL item banks was simulated, and various IRT models were applied and compared. SUBJECTS: The original data were collected as part of the NCI-funded Quality of Life Evaluation in Oncology (Q-Score) Project. A total of 1,714 patients with cancer or HIV/AIDS were recruited from 5 clinical sites. MEASURES: Items from 4 HRQOL instruments were evaluated: Cancer Rehabilitation Evaluation System-Short Form, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, Functional Assessment of Cancer Therapy and Medical Outcomes Study Short-Form Health Survey. RESULTS AND CONCLUSIONS: Four lessons learned from the project are discussed: the importance of good developmental item banks, the ambiguity of model fit results, the limits of our knowledge regarding the practical implications of model misfit, and the importance in the measurement of HRQOL of construct definition. With respect to these lessons, areas for future research are suggested. The feasibility of developing item banks for broad definitions of health is discussed.},
	keywords = {*Data Interpretation, Statistical, *Health Status, *Quality of Life, *Questionnaires, *Software, Female, HIV Infections/psychology, Humans, Male, Neoplasms/psychology, Outcome Assessment (Health Care)/*methods, Psychometrics, Stress, Psychological},
	isbn = {0962-9343 (Print)},
	author = {Cook, K. F. and Teal, C. R. and Bjorner, J. B. and Cella, D. and Chang, C-H. and Crane, P. K. and Gibbons, L. E. and Hays, R. D. and McHorney, C. A. and Ocepek-Welikson, K. and Raczek, A. E. and Teresi, J. A. and Reeve, B. B.}
}
