@article {249,
	title = {Computerized adaptive testing: a mixture item selection approach for constrained situations},
	journal = {British Journal of Mathematical and Statistical Psychology},
	volume = {58},
	number = {2},
	year = {2005},
	note = {Leung, Chi-KeungChang, Hua-HuaHau, Kit-TaiEnglandBr J Math Stat Psychol. 2005 Nov;58(Pt 2):239-57.},
	month = {Nov},
	pages = {239-57},
	edition = {2005/11/19},
	abstract = {In 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.},
	keywords = {*Computer-Aided Design, *Educational Measurement/methods, *Models, Psychological, Humans, Psychometrics/methods},
	isbn = {0007-1102 (Print)0007-1102 (Linking)},
	author = {Leung, C. K. and Chang, Hua-Hua and Hau, K. T.}
}
@article {329,
	title = {Computerization and adaptive administration of the NEO PI-R},
	journal = {Assessment},
	volume = {7},
	number = {4},
	year = {2000},
	note = {1073-1911 (Print)Journal Article},
	pages = {347-64},
	abstract = {This 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.},
	keywords = {*Personality Inventory, Algorithms, California, Diagnosis, Computer-Assisted/*methods, Humans, Models, Psychological, Psychometrics/methods, Reproducibility of Results},
	author = {Reise, S. P. and Henson, J. M.}
}
