03314nas a2200433   4500008004100000020004600041245007700087210006900164250001500233260001100248300001200259490000700271520203200278653002702310653003002337653002102367653001002388653000902398653001502407653003602422653002102458653004402479653002402523653001102547653001102558653001302569653000902582653001602591653003002607653003002637653003102667100001502698700001302713700001502726700001402741700001502755700001402770856009602784       2008                        eng d  a1528-1159 (Electronic)0362-2436 (Linking)00aComputerized adaptive testing in back pain: Validation of the CAT-5D-QOL0 aComputerized adaptive testing in back pain Validation of the CAT  a2008/05/23  cMay 20  a1384-900 v333 aSTUDY DESIGN: We have conducted an outcome instrument validation study. OBJECTIVE: Our objective was to develop a computerized adaptive test (CAT) to measure 5 domains of health-related quality of life (HRQL) and assess its feasibility, reliability, validity, and efficiency. SUMMARY OF BACKGROUND DATA: Kopec and colleagues have recently developed item response theory based item banks for 5 domains of HRQL relevant to back pain and suitable for CAT applications. The domains are Daily Activities (DAILY), Walking (WALK), Handling Objects (HAND), Pain or Discomfort (PAIN), and Feelings (FEEL). METHODS: An adaptive algorithm was implemented in a web-based questionnaire administration system. The questionnaire included CAT-5D-QOL (5 scales), Modified Oswestry Disability Index (MODI), Roland-Morris Disability Questionnaire (RMDQ), SF-36 Health Survey, and standard clinical and demographic information. Participants were outpatients treated for mechanical back pain at a referral center in Vancouver, Canada. RESULTS: A total of 215 patients completed the questionnaire and 84 completed a retest. On average, patients answered 5.2 items per CAT-5D-QOL scale. Reliability ranged from 0.83 (FEEL) to 0.92 (PAIN) and was 0.92 for the MODI, RMDQ, and Physical Component Summary (PCS-36). The ceiling effect was 0.5% for PAIN compared with 2% for MODI and 5% for RMQ. The CAT-5D-QOL scales correlated as anticipated with other measures of HRQL and discriminated well according to the level of satisfaction with current symptoms, duration of the last episode, sciatica, and disability compensation. The average relative discrimination index was 0.87 for PAIN, 0.67 for DAILY and 0.62 for WALK, compared with 0.89 for MODI, 0.80 for RMDQ, and 0.59 for PCS-36. CONCLUSION: The CAT-5D-QOL is feasible, reliable, valid, and efficient in patients with back pain. This methodology can be recommended for use in back pain research and should improve outcome assessment, facilitate comparisons across studies, and reduce patient burden.10a*Disability Evaluation10a*Health Status Indicators10a*Quality of Life10aAdult10aAged10aAlgorithms10aBack Pain/*diagnosis/psychology10aBritish Columbia10aDiagnosis, Computer-Assisted/*standards10aFeasibility Studies10aFemale10aHumans10aInternet10aMale10aMiddle Aged10aPredictive Value of Tests10aQuestionnaires/*standards10aReproducibility of Results1 aKopec, J A1 aBadii, M1 aMcKenna, M1 aLima, V D1 aSayre, E C1 aDvorak, M  uhttp://mail.iacat.org/content/computerized-adaptive-testing-back-pain-validation-cat-5d-qol02419nas a2200325   4500008004100000020002200041245008700063210006900150250001500219300001100234490000700245520140100252653001901653653003001672653001901702653003801721653002101759653002001780653001401800653001501814653003301829653001101862653002401873653001801897100001701915700001501932700001501947700001501962856011601977       2007                        eng d  a0962-9343 (Print)00aDeveloping tailored instruments: item banking and computerized adaptive assessment0 aDeveloping tailored instruments item banking and computerized ad  a2007/05/29  a95-1080 v163 aItem 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.10a*Health Status10a*Health Status Indicators10a*Mental Health10a*Outcome Assessment (Health Care)10a*Quality of Life10a*Questionnaires10a*Software10aAlgorithms10aFactor Analysis, Statistical10aHumans10aModels, Statistical10aPsychometrics1 aBjorner, J B1 aChang, C-H1 aThissen, D1 aReeve, B B  uhttp://mail.iacat.org/content/developing-tailored-instruments-item-banking-and-computerized-adaptive-assessment01964nas a2200265   4500008004100000020002200041245008200063210006900145260002600214300001000240490000700250520112900257653001501386653003401401653001401435653001701449653002401466653001501490653002201505653001501527100002301542700001301565700001801578856010201596       2006                        eng d  a1076-9986 (Print)00aAssembling a computerized adaptive testing item pool as a set of linear tests0 aAssembling a computerized adaptive testing item pool as a set of  bSage Publications: US  a81-990 v313 aTest-item writing efforts typically results in item pools with an undesirable correlational structure between the content attributes of the items and their statistical information. If such pools are used in computerized adaptive testing (CAT), the algorithm may be forced to select items with less than optimal information, that violate the content constraints, and/or have unfavorable exposure rates. Although at first sight somewhat counterintuitive, it is shown that if the CAT pool is assembled as a set of linear test forms, undesirable correlations can be broken down effectively. It is proposed to assemble such pools using a mixed integer programming model with constraints that guarantee that each test meets all content specifications and an objective function that requires them to have maximal information at a well-chosen set of ability values. An empirical example with a previous master pool from the Law School Admission Test (LSAT) yielded a CAT with nearly uniform bias and mean-squared error functions for the ability estimator and item-exposure rates that satisfied the target for all items in the pool. 10aAlgorithms10acomputerized adaptive testing10aitem pool10alinear tests10amathematical models10astatistics10aTest Construction10aTest Items1 avan der Linden, WJ1 aAriel, A1 aVeldkamp, B P  uhttp://mail.iacat.org/content/assembling-computerized-adaptive-testing-item-pool-set-linear-tests03120nas a2200277   4500008004100000020002200041245010900063210006900172250001500241260000800256300001200264490000700276520221700283653002902500653002002529653002502549653002102574653001502595653002802610653001102638653002502649100001702674700001502691700001202706856012402718       2006                        eng d  a0214-9915 (Print)00aMaximum information stratification method for controlling item exposure in computerized adaptive testing0 aMaximum information stratification method for controlling item e  a2007/02/14  cFeb  a156-1590 v183 aThe proposal for increasing the security in Computerized Adaptive Tests that has received most attention in recent years is the a-stratified method (AS - Chang and Ying, 1999): at the beginning of the test only items with low discrimination parameters (a) can be administered, with the values of the a parameters increasing as the test goes on. With this method, distribution of the exposure rates of the items is less skewed, while efficiency is maintained in trait-level estimation. The pseudo-guessing parameter (c), present in the three-parameter logistic model, is considered irrelevant, and is not used in the AS method. The Maximum Information Stratified (MIS) model incorporates the c parameter in the stratification of the bank and in the item-selection rule, improving accuracy by comparison with the AS, for item banks with a and b parameters correlated and uncorrelated. For both kinds of banks, the blocking b methods (Chang, Qian and Ying, 2001) improve the security of the item bank.Método de estratificación por máxima información para el control de la exposición en tests adaptativos informatizados. La propuesta para aumentar la seguridad en los tests adaptativos informatizados que ha recibido más atención en los últimos años ha sido el método a-estratificado (AE - Chang y Ying, 1999): en los momentos iniciales del test sólo pueden administrarse ítems con bajos parámetros de discriminación (a), incrementándose los valores del parámetro a admisibles según avanza el test. Con este método la distribución de las tasas de exposición de los ítems es más equilibrada, manteniendo una adecuada precisión en la medida. El parámetro de pseudoadivinación (c), presente en el modelo logístico de tres parámetros, se supone irrelevante y no se incorpora en el AE. El método de Estratificación por Máxima Información (EMI) incorpora el parámetro c a la estratificación del banco y a la regla de selección de ítems, mejorando la precisión en comparación con AE, tanto para bancos donde los parámetros a y b correlacionan como para bancos donde no. Para ambos tipos de bancos, los métodos de bloqueo de b (Chang, Qian y Ying, 2001) mejoran la seguridad del banco.10a*Artificial Intelligence10a*Microcomputers10a*Psychological Tests10a*Software Design10aAlgorithms10aChi-Square Distribution10aHumans10aLikelihood Functions1 aBarrada, J R1 aMazuela, P1 aOlea, J  uhttp://mail.iacat.org/content/maximum-information-stratification-method-controlling-item-exposure-computerized-adaptive02153nas a2200229   4500008004100000020002200041245008700063210006900150260004100219300001200260490000700272520135500279653002101634653001501655653002401670653002601694653002501720653002101745653003101766100002301797856010301820       2005                        eng d  a0022-0655 (Print)00aA comparison of item-selection methods for adaptive tests with content constraints0 acomparison of itemselection methods for adaptive tests with cont  bBlackwell Publishing: United Kingdom  a283-3020 v423 aIn test assembly, a fundamental difference exists between algorithms that select a test sequentially or simultaneously. Sequential assembly allows us to optimize an objective function at the examinee's ability estimate, such as the test information function in computerized adaptive testing. But it leads to the non-trivial problem of how to realize a set of content constraints on the test—a problem more naturally solved by a simultaneous item-selection method. Three main item-selection methods in adaptive testing offer solutions to this dilemma. The spiraling method moves item selection across categories of items in the pool proportionally to the numbers needed from them. Item selection by the weighted-deviations method (WDM) and the shadow test approach (STA) is based on projections of the future consequences of selecting an item. These two methods differ in that the former calculates a projection of a weighted sum of the attributes of the eventual test and the latter a projection of the test itself. The pros and cons of these methods are analyzed. An empirical comparison between the WDM and STA was conducted for an adaptive version of the Law School Admission Test (LSAT), which showed equally good item-exposure rates but violations of some of the constraints and larger bias and inaccuracy of the ability estimator for the WDM.10aAdaptive Testing10aAlgorithms10acontent constraints10aitem selection method10ashadow test approach10aspiraling method10aweighted deviations method1 avan der Linden, WJ  uhttp://mail.iacat.org/content/comparison-item-selection-methods-adaptive-tests-content-constraints02021nas a2200193   4500008004100000245009300041210006900134300001200203490000700215520136400222653001501586653002401601653001101625653002201636100001801658700001801676700001901694856011401713       2005                        eng d00aInfeasibility in automated test assembly models: A comparison study of different methods0 aInfeasibility in automated test assembly models A comparison stu  a223-2430 v423 aSeveral techniques exist to automatically put together a test meeting a number of specifications. In an item bank, the items are stored with their characteristics. A test is constructed by selecting a set of items that fulfills the specifications set by the test assembler. Test assembly problems are often formulated in terms of a model consisting of restrictions and an objective to be maximized or minimized. A problem arises when it is impossible to construct a test from the item pool that meets all specifications, that is, when the model is not feasible. Several methods exist to handle these infeasibility problems. In this article, test assembly models resulting from two practical testing programs were reconstructed to be infeasible. These models were analyzed using methods that forced a solution (Goal Programming, Multiple-Goal Programming, Greedy Heuristic), that analyzed the causes (Relaxed and Ordered Deletion Algorithm (RODA), Integer Randomized Deletion Algorithm (IRDA), Set Covering (SC), and Item Sampling), or that analyzed the causes and used this information to force a solution (Irreducible Infeasible Set-Solver). Specialized methods such as the IRDA and the Irreducible Infeasible Set-Solver performed best. Recommendations about the use of different methods are given. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAlgorithms10aItem Content (Test)10aModels10aTest Construction1 aHuitzing, H A1 aVeldkamp, B P1 aVerschoor, A J  uhttp://mail.iacat.org/content/infeasibility-automated-test-assembly-models-comparison-study-different-methods01905nas a2200217   4500008004100000245010000041210006900141260000800210300001100218490000700229520117300236653002201409653001501431653003701446653003101483653001101514653001901525100001201544700001501556856011601571       2004                        eng d00aA computerized adaptive knowledge test as an assessment tool in general practice: a pilot study0 acomputerized adaptive knowledge test as an assessment tool in ge  cMar  a178-830 v263 aAdvantageous to assessment in many fields, CAT (computerized adaptive testing) use in general practice has been scarce. In adapting CAT to general practice, the basic assumptions of item response theory and the case specificity must be taken into account. In this context, this study first evaluated the feasibility of converting written extended matching tests into CAT. Second, it questioned the content validity of CAT. A stratified sample of students was invited to participate in the pilot study. The items used in this test, together with their parameters, originated from the written test. The detailed test paths of the students were retained and analysed thoroughly. Using the predefined pass-fail standard, one student failed the test. There was a positive correlation between the number of items and the candidate's ability level. The majority of students were presented with questions in seven of the 10 existing domains. Although proved to be a feasible test format, CAT cannot substitute for the existing high-stakes large-scale written test. It may provide a reliable instrument for identifying candidates who are at risk of failing in the written test.10a*Computer Systems10aAlgorithms10aEducational Measurement/*methods10aFamily Practice/*education10aHumans10aPilot Projects1 aRoex, A1 aDegryse, J  uhttp://mail.iacat.org/content/computerized-adaptive-knowledge-test-assessment-tool-general-practice-pilot-study02594nas a2200469   4500008004100000245007200041210006900113300001000182490000600192520108600198653002501284653001001309653001501319653002101334653002201355653005901377653007001436653003301506653001101539653001101550653001301561653000901574653002701583653002201610653005501632653001901687653001501706653006601721653001801787653003701805653004101842653003001883653001301913100001501926700001301941700001801954700001501972700001401987700001402001700001302015856009602028       2004                        eng d00aComputerized adaptive measurement of depression: A simulation study0 aComputerized adaptive measurement of depression A simulation stu  a13-230 v43 aBackground: Efficient, accurate instruments for measuring depression are increasingly importantin clinical practice. We developed a computerized adaptive version of the Beck DepressionInventory (BDI). We examined its efficiency and its usefulness in identifying Major DepressiveEpisodes (MDE) and in measuring depression severity.Methods: Subjects were 744 participants in research studies in which each subject completed boththe BDI and the SCID. In addition, 285 patients completed the Hamilton Depression Rating Scale.Results: The adaptive BDI had an AUC as an indicator of a SCID diagnosis of MDE of 88%,equivalent to the full BDI. The adaptive BDI asked fewer questions than the full BDI (5.6 versus 21items). The adaptive latent depression score correlated r = .92 with the BDI total score and thelatent depression score correlated more highly with the Hamilton (r = .74) than the BDI total scoredid (r = .70).Conclusions: Adaptive testing for depression may provide greatly increased efficiency withoutloss of accuracy in identifying MDE or in measuring depression severity.10a*Computer Simulation10aAdult10aAlgorithms10aArea Under Curve10aComparative Study10aDepressive Disorder/*diagnosis/epidemiology/psychology10aDiagnosis, Computer-Assisted/*methods/statistics & numerical data10aFactor Analysis, Statistical10aFemale10aHumans10aInternet10aMale10aMass Screening/methods10aPatient Selection10aPersonality Inventory/*statistics & numerical data10aPilot Projects10aPrevalence10aPsychiatric Status Rating Scales/*statistics & numerical data10aPsychometrics10aResearch Support, Non-U.S. Gov't10aResearch Support, U.S. Gov't, P.H.S.10aSeverity of Illness Index10aSoftware1 aGardner, W1 aShear, K1 aKelleher, K J1 aPajer, K A1 aMammen, O1 aBuysse, D1 aFrank, E  uhttp://mail.iacat.org/content/computerized-adaptive-measurement-depression-simulation-study01762nas a2200313   4500008004100000245007700041210006900118300001200187490000700199520083400206653002101040653001501061653001701076653001601093653003001109653001701139653001501156653002501171653002001196653001501216653002501231653001801256653000901274100001801283700001201301700001601313700001601329856010301345       2003                        eng d00aComputerized adaptive rating scales for measuring managerial performance0 aComputerized adaptive rating scales for measuring managerial per  a237-2460 v113 aComputerized adaptive rating scales (CARS) had been developed to measure contextual or citizenship performance. This rating format used a paired-comparison protocol, presenting pairs of behavioral statements scaled according to effectiveness levels, and an iterative item response theory algorithm to obtain estimates of ratees' citizenship performance (W. C. Borman et al, 2001). In the present research, we developed CARS to measure the entire managerial performance domain, including task and citizenship performance, thus addressing a major limitation of the earlier CARS. The paper describes this development effort, including an adjustment to the algorithm that reduces substantially the number of item pairs required to obtain almost as much precision in the performance estimates. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aAlgorithms10aAssociations10aCitizenship10aComputer Assisted Testing10aConstruction10aContextual10aItem Response Theory10aJob Performance10aManagement10aManagement Personnel10aRating Scales10aTest1 aSchneider, RJ1 aGoff, M1 aAnderson, S1 aBorman, W C  uhttp://mail.iacat.org/content/computerized-adaptive-rating-scales-measuring-managerial-performance02014nas a2200205   4500008004100000245008200041210006900123300001200192490000700204520132300211653002101534653001501555653003001570653001101600653000901611653004701620100001901667700001301686856010901699       2002                        eng d00aHypergeometric family and item overlap rates in computerized adaptive testing0 aHypergeometric family and item overlap rates in computerized ada  a387-3980 v673 aA computerized adaptive test (CAT) is usually administered to small groups of examinees at frequent time intervals. It is often the case that examinees who take the test earlier share information with examinees who will take the test later, thus increasing the risk that many items may become known. Item overlap rate for a group of examinees refers to the number of overlapping items encountered by these examinees divided by the test length. For a specific item pool, different item selection algorithms may yield different item overlap rates. An important issue in designing a good CAT item selection algorithm is to keep item overlap rate below a preset level. In doing so, it is important to investigate what the lowest rate could be for all possible item selection algorithms. In this paper we rigorously prove that if every item had an equal possibility to be selected from the pool in a fixed-length CAT, the number of overlapping item among any α randomly sampled examinees follows the hypergeometric distribution family for α ≥ 1. Thus, the expected values of the number of overlapping items among any randomly sampled α examinee can be calculated precisely. These values may serve as benchmarks in controlling item overlap rates for fixed-length adaptive tests. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aAlgorithms10aComputer Assisted Testing10aTaking10aTest10aTime On Task computerized adaptive testing1 aChang, Hua-Hua1 aZhang, J  uhttp://mail.iacat.org/content/hypergeometric-family-and-item-overlap-rates-computerized-adaptive-testing01564nas a2200229   4500008004100000245006400041210006300105300001100168490000600179520083800185653002701023653001501050653001501065653004201080653001101122653002601133653002601159653003101185100001501216700001601231856008701247       2000                        eng d00aComputerization and adaptive administration of the NEO PI-R0 aComputerization and adaptive administration of the NEO PIR  a347-640 v73 aThis study asks, how well does an item response theory (IRT) based computerized adaptive NEO PI-R work? To explore this question, real-data simulations (N = 1,059) were used to evaluate a maximum information item selection computerized adaptive test (CAT) algorithm. Findings indicated satisfactory recovery of full-scale facet scores with the administration of around four items per facet scale. Thus, the NEO PI-R could be reduced in half with little loss in precision by CAT administration. However, results also indicated that the CAT algorithm was not necessary. We found that for many scales, administering the "best" four items per facet scale would have produced similar results. In the conclusion, we discuss the future of computerized personality assessment and describe the role IRT methods might play in such assessments.10a*Personality Inventory10aAlgorithms10aCalifornia10aDiagnosis, Computer-Assisted/*methods10aHumans10aModels, Psychological10aPsychometrics/methods10aReproducibility of Results1 aReise, S P1 aHenson, J M  uhttp://mail.iacat.org/content/computerization-and-adaptive-administration-neo-pi-r