02711nas a2200217   4500008004100000020004100041245010800082210006900190250001500259300001100274490000600285520189600291653003802187653002902225653005702254653001102311653001302322653002402335100001302359856012102372       2008                        eng d  a1529-7713 (Print)1529-7713 (Linking)00aBinary items and beyond: a simulation of computer adaptive testing using the Rasch partial credit model0 aBinary items and beyond a simulation of computer adaptive testin  a2008/01/09  a81-1040 v93 aPast 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' 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'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.10a*Data Interpretation, Statistical10a*User-Computer Interface10aEducational Measurement/*statistics & numerical data10aHumans10aIllinois10aModels, Statistical1 aLange, R  uhttp://www.iacat.org/content/binary-items-and-beyond-simulation-computer-adaptive-testing-using-rasch-partial-credit03104nas a2200445   4500008004100000020002200041245007100063210006900134250001500203300001200218490000700230520183100237653003802068653001902106653002102125653002002146653001402166653001102180653003002191653001102221653000902232653002502241653004602266653001802312653002602330100001302356700001402369700001702383700001302400700001502413700001502428700001702443700001402460700001802474700002302492700001602515700001602531700001502547856009602562       2007                        eng d  a0962-9343 (Print)00aIRT health outcomes data analysis project: an overview and summary0 aIRT health outcomes data analysis project an overview and summar  a2007/03/14  a121-1320 v163 aBACKGROUND: 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.10a*Data Interpretation, Statistical10a*Health Status10a*Quality of Life10a*Questionnaires10a*Software10aFemale10aHIV Infections/psychology10aHumans10aMale10aNeoplasms/psychology10aOutcome Assessment (Health Care)/*methods10aPsychometrics10aStress, Psychological1 aCook, KF1 aTeal, C R1 aBjorner, J B1 aCella, D1 aChang, C-H1 aCrane, P K1 aGibbons, L E1 aHays, R D1 aMcHorney, C A1 aOcepek-Welikson, K1 aRaczek, A E1 aTeresi, J A1 aReeve, B B  uhttp://www.iacat.org/content/irt-health-outcomes-data-analysis-project-overview-and-summary