@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 {57,
	title = {Optimal stratification of item pools in α-stratified computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {4},
	year = {2003},
	pages = {262-274},
	abstract = {A method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in α-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous item pool from the computerized adaptive testing (CAT) version of the Graduate Record Exams (GRE) Quantitative Test. The results indicate that the new method performs well in practical situations. It improves item exposure control, reduces the mean squared error in the θ estimates, and increases test reliability. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {Adaptive Testing, Computer Assisted Testing, Item Content (Test), Item Response Theory, Mathematical Modeling, Test Construction computerized adaptive testing},
	author = {Chang, Hua-Hua and van der Linden, W. J.}
}
@article {308,
	title = {A comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model},
	journal = {Applied Psychological Measurement},
	volume = {26},
	number = {2},
	year = {2002},
	pages = {147-163},
	abstract = {The use of more performance items in large-scale testing has led to an increase in the research investigating the use of polytomously scored items in computer adaptive testing (CAT). Because this research has to be complemented with information pertaining to exposure control, the present research investigated the impact of using five different exposure control algorithms in two sized item pools calibrated using the generalized partial credit model. The results of the simulation study indicated that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap, increase pool utilization, and only minorly degrade measurement precision. Use of the more restrictive exposure control algorithms, such as the Sympson-Hetter and conditional Sympson-Hetter, controlled exposure to a greater extent but at the cost of measurement precision. Because convergence of the exposure control parameters was problematic for some of the more restrictive exposure control algorithms, use of the more simplistic exposure control mechanisms, particularly when the test length to item pool size ratio is large, is recommended. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {(Statistical), Adaptive Testing, Algorithms computerized adaptive testing, Computer Assisted Testing, Item Analysis, Item Response Theory, Mathematical Modeling},
	author = {Pastor, D. A. and Dodd, B. G. and Chang, Hua-Hua}
}
