00732nas a2200193 4500008004500000245009100045210006900136300001000205490000700215653003500222653003400257653002900291653002600320100001900346700002700365700002400392700002100416856010100437 2023 Engldsh 00aHow Do Trait Change Patterns Affect the Performance of Adaptive Measurement of Change?0 aHow Do Trait Change Patterns Affect the Performance of Adaptive  a32-580 v1010aadaptive measurement of change10acomputerized adaptive testing10alongitudinal measurement10atrait change patterns1 aTai, Ming, Him1 aCooperman, Allison, W.1 aDeWeese, Joseph, N.1 aWeiss, David, J. uhttp://www.iacat.org/how-do-trait-change-patterns-affect-performance-adaptive-measurement-change03946nas a2200181 4500008004100000245009300041210006900134260005500203520329800258653001203556653002403568653002603592653002503618100001403643700002103657700001503678856007103693 2017 eng d00aDIF-CAT: Doubly Adaptive CAT Using Subgroup Information to Improve Measurement Precision0 aDIFCAT Doubly Adaptive CAT Using Subgroup Information to Improve aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Differential item functioning (DIF) is usually regarded as a test fairness issue in high-stakes tests. In low-stakes tests, it is more of an accuracy problem. However, in low-stakes tests, the same method, deleting items that demonstrate significant DIF, is still employed to treat DIF items. When political concerns are not important, such as in low-stakes tests and instruments that are not used to make decisions about people, deleting items might not be optimal. Computerized adaptive testing (CAT) is more and more frequently used in low-stakes tests. The DIF-CAT method evaluated in this research is designed to cope with DIF in a CAT environment. Using this method, item parameters are separately estimated for the focal group and the reference group in a DIF study, then CATs are administered based on different sets of item parameters for the focal and reference groups.

To evaluate the performance of the DIF-CAT procedure, it was compared in a simulation study to (1) deleting all the DIF items in a CAT bank and (2) ignoring DIF. A 300-item flat item bank and a 300-item peaked item bank were simulated using the three-parameter logistic IRT model with D = 1,7. 40% of the items in each bank showed DIF. The DIF size was b and/or a = 0.5 while original b ranged from -3 to 3 and a ranged from 0.3 to 2.1. Three types of DIF were considered: (1) uniform DIF caused by differences in b, non-uniform DIF caused by differences in a, and non-uniform DIF caused by differences in both a and b. 500 normally distributed simulees in each of reference and focal groups were used in item parameter re-calibration. In the Delete DIF method, only DIF-free items were calibrated. In the Ignore DIF method, all the items were calibrated using all simulees without differentiating the groups. In the DIF-CAT method, the DIF-free items were used as anchor items to estimate the item parameters for the focal and reference groups and the item parameters from recalibration were used. All simulees used the same item parameters in the Delete method and the Ignore method. CATs for simulees within the two groups used group-specific item parameters in the DIF-CAT method. In the CAT stage, 100 simulees were generated for each of the reference and focal groups, at each of six discrete q levels ranging from -2.5 to 2.5. CAT test length was fixed at 40 items. Bias, average absolute difference, RMSE, standard error of θ estimates, and person fit, were used to compare the performance of the DIF methods. DIF item usage was also recorded for the Ignore method and the DIF-CAT method.

Generally, the DIF-CAT method outperformed both the Delete method and the Ignore method in dealing with DIF items in CAT. The Delete method, which is the most frequently used method for handling DIF, performed the worst of the three methods in a CAT environment, as reflected in multiple indices of measurement precision. Even the Ignore method, which simply left DIF items in the item bank, provided θ estimates of higher precision than the Delete method. This poor performance of the Delete method was probably due to reduction in size of the item bank available for each CAT.

Session Video

10aDIF-CAT10aDoubly Adaptive CAT10aMeasurement Precision10asubgroup information1 aWang, Joy1 aWeiss, David, J.1 aWang, Chun uhttps://drive.google.com/open?id=1Gu4FR06qM5EZNp_Ns0Kt3HzBqWAv3LPy02365nas a2200277 4500008004100000245007100041210006900112260005500181520152900236653000801765653001501773653002801788100001501816700002101831700001501852700001401867700001601881700001501897700001601912700001601928700001601944700001801960700001901978700001901997856007102016 2017 eng d00aNew Challenges (With Solutions) and Innovative Applications of CAT0 aNew Challenges With Solutions and Innovative Applications of CAT aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Over the past several decades, computerized adaptive testing (CAT) has profoundly changed the administration of large-scale aptitude tests, state-wide achievement tests, professional licensure exams, and health outcome measures. While many challenges of CAT have been successfully addressed due to the continual efforts of researchers in the field, there are still many remaining, longstanding challenges that have yet to be resolved. This symposium will begin with three presentations, each of which provides a sound solution to one of the unresolved challenges. They are (1) item calibration when responses are “missing not at random” from CAT administration; (2) online calibration of new items when person traits have non-ignorable measurement error; (3) establishing consistency and asymptotic normality of latent trait estimation when allowing item response revision in CAT. In addition, this symposium also features innovative applications of CAT. In particular, there is emerging interest in using cognitive diagnostic CAT to monitor and detect learning progress (4th presentation). Last but not least, the 5th presentation illustrates the power of multidimensional polytomous CAT that permits rapid identification of hospitalized patients’ rehabilitative care needs in health outcomes measurement. We believe this symposium covers a wide range of interesting and important topics in CAT.

Session Video

10aCAT10achallenges10ainnovative applications1 aWang, Chun1 aWeiss, David, J.1 aZhang, Xue1 aTao, Jian1 aHe, Yinhong1 aChen, Ping1 aWang, Shiyu1 aZhang, Susu1 aLin, Haiyan1 aGao, Xiaohong1 aChang, Hua-Hua1 aShang, Zhuoran uhttps://drive.google.com/open?id=1Wvgxw7in_QCq_F7kzID6zCZuVXWcFDPa