02215nas a2200241   4500008004100000020004100041245009600082210006900178250001500247260000800262300001100270490000700281520139600288653002701684653003701711653002701748653001101775653002601786100001501812700001901827700001301846856011401859       2005                        eng d  a0007-1102 (Print)0007-1102 (Linking)00aComputerized adaptive testing: a mixture item selection approach for constrained situations0 aComputerized adaptive testing a mixture item selection approach   a2005/11/19  cNov  a239-570 v583 aIn computerized adaptive testing (CAT), traditionally the most discriminating items are selected to provide the maximum information so as to attain the highest efficiency in trait (theta) estimation. The maximum information (MI) approach typically results in unbalanced item exposure and hence high item-overlap rates across examinees. Recently, Yi and Chang (2003) proposed the multiple stratification (MS) method to remedy the shortcomings of MI. In MS, items are first sorted according to content, then difficulty and finally discrimination parameters. As discriminating items are used strategically, MS offers a better utilization of the entire item pool. However, for testing with imposed non-statistical constraints, this new stratification approach may not maintain its high efficiency. Through a series of simulation studies, this research explored the possible benefits of a mixture item selection approach (MS-MI), integrating the MS and MI approaches, in testing with non-statistical constraints. In all simulation conditions, MS consistently outperformed the other two competing approaches in item pool utilization, while the MS-MI and the MI approaches yielded higher measurement efficiency and offered better conformity to the constraints. Furthermore, the MS-MI approach was shown to perform better than MI on all evaluation criteria when control of item exposure was imposed.10a*Computer-Aided Design10a*Educational Measurement/methods10a*Models, Psychological10aHumans10aPsychometrics/methods1 aLeung, C K1 aChang, Hua-Hua1 aHau, K T  uhttp://iacat.org/content/computerized-adaptive-testing-mixture-item-selection-approach-constrained-situations01559nas a2200229   4500008004100000245006400041210006300105300001100168490000600179520083800185653002701023653001501050653001501065653004201080653001101122653002601133653002601159653003101185100001501216700001601231856008201247       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://iacat.org/content/computerization-and-adaptive-administration-neo-pi-r