@article {77,
	title = {The maximum priority index method for severely constrained item selection in computerized adaptive testing},
	journal = {British Journal of Mathematical and Statistical Psychology},
	volume = {62},
	number = {2},
	year = {2009},
	note = {Cheng, YingChang, Hua-HuaResearch Support, Non-U.S. Gov{\textquoteright}tEnglandThe British journal of mathematical and statistical psychologyBr J Math Stat Psychol. 2009 May;62(Pt 2):369-83. Epub 2008 Jun 2.},
	month = {May},
	pages = {369-83},
	edition = {2008/06/07},
	abstract = {This paper introduces a new heuristic approach, the maximum priority index (MPI) method, for severely constrained item selection in computerized adaptive testing. Our simulation study shows that it is able to accommodate various non-statistical constraints simultaneously, such as content balancing, exposure control, answer key balancing, and so on. Compared with the weighted deviation modelling method, it leads to fewer constraint violations and better exposure control while maintaining the same level of measurement precision.},
	keywords = {Aptitude Tests/*statistics \& numerical data, Diagnosis, Computer-Assisted/*statistics \& numerical data, Educational Measurement/*statistics \& numerical data, Humans, Mathematical Computing, Models, Statistical, Personality Tests/*statistics \& numerical data, Psychometrics/*statistics \& numerical data, Reproducibility of Results, Software},
	isbn = {0007-1102 (Print)0007-1102 (Linking)},
	author = {Cheng, Y and Chang, Hua-Hua}
}
@article {273,
	title = {Combining computer adaptive testing technology with cognitively diagnostic assessment},
	journal = {Behavioral Research Methods },
	volume = {40},
	number = {3},
	year = {2008},
	note = {McGlohen, MeghanChang, Hua-HuaUnited StatesBehavior research methodsBehav Res Methods. 2008 Aug;40(3):808-21.},
	month = {Aug},
	pages = {808-21},
	edition = {2008/08/14},
	abstract = {A major advantage of computerized adaptive testing (CAT) is that it allows the test to home in on an examinee{\textquoteright}s ability level in an interactive manner. The aim of the new area of cognitive diagnosis is to provide information about specific content areas in which an examinee needs help. The goal of this study was to combine the benefit of specific feedback from cognitively diagnostic assessment with the advantages of CAT. In this study, three approaches to combining these were investigated: (1) item selection based on the traditional ability level estimate (theta), (2) item selection based on the attribute mastery feedback provided by cognitively diagnostic assessment (alpha), and (3) item selection based on both the traditional ability level estimate (theta) and the attribute mastery feedback provided by cognitively diagnostic assessment (alpha). The results from these three approaches were compared for theta estimation accuracy, attribute mastery estimation accuracy, and item exposure control. The theta- and alpha-based condition outperformed the alpha-based condition regarding theta estimation, attribute mastery pattern estimation, and item exposure control. Both the theta-based condition and the theta- and alpha-based condition performed similarly with regard to theta estimation, attribute mastery estimation, and item exposure control, but the theta- and alpha-based condition has an additional advantage in that it uses the shadow test method, which allows the administrator to incorporate additional constraints in the item selection process, such as content balancing, item type constraints, and so forth, and also to select items on the basis of both the current theta and alpha estimates, which can be built on top of existing 3PL testing programs.},
	keywords = {*Cognition, *Computers, *Models, Statistical, *User-Computer Interface, Diagnosis, Computer-Assisted/*instrumentation, Humans},
	isbn = {1554-351X (Print)},
	author = {McGlohen, M. and Chang, Hua-Hua}
}
@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.}
}
