@article {507,
	title = {Constraint-weighted a-stratification for computerized adaptive testing with nonstatistical constraints: Balancing measurement efficiency and exposure control},
	journal = {Educational and Psychological Measurement},
	volume = {69},
	year = {2009},
	pages = {35-49},
	author = {Cheng, Y and Chang, Hua-Hua and Douglas, J. and Guo, F.}
}
@article {2182,
	title = {Constraint-Weighted a-Stratification for Computerized Adaptive Testing With Nonstatistical Constraints},
	journal = {Educational and Psychological Measurement},
	volume = {69},
	number = {1},
	year = {2009},
	pages = {35-49},
	abstract = {<p>a-stratification is a method that utilizes items with small discrimination (a) parameters early in an exam and those with higher a values when more is learned about the ability parameter. It can achieve much better item usage than the maximum information criterion (MIC). To make a-stratification more practical and more widely applicable, a method for weighting the item selection process in a-stratification as a means of satisfying multiple test constraints is proposed. This method is studied in simulation against an analogous method without stratification as well as a-stratification using descending-rather than ascending-a procedures. In addition, a variation of a-stratification that allows for unbalanced usage of a parameters is included in the study to examine the trade-off between efficiency and exposure control. Finally, MIC and randomized item selection are included as baseline measures. Results indicate that the weighting mechanism successfully addresses the constraints, that stratification helps to a great extent balancing exposure rates, and that the ascending-a design improves measurement precision.</p>
},
	doi = {10.1177/0013164408322030},
	url = {http://epm.sagepub.com/content/69/1/35.abstract},
	author = {Ying Cheng, and Chang, Hua-Hua and Douglas, Jeffrey and Fanmin Guo,}
}
@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}
}
@conference {884,
	title = {Constraints-weighted information method for item selection of severely constrained computerized adaptive testing},
	booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education},
	year = {2006},
	address = {San Francisco},
	author = {Cheng, Y and Chang, Hua-Hua and Wang, X. B.}
}
@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.}
}
@conference {1058,
	title = {Combining computer adaptive testing technology with cognitively diagnostic assessment},
	booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education},
	year = {2004},
	note = {{PDF file, 782 KB}},
	address = {San Diego CA},
	author = {McGlohen, MK and Chang, Hua-Hua and Wills, J. T.}
}
@article {251,
	title = {Computerized adaptive testing: A comparison of three content balancing methods},
	journal = {The Journal of Technology, Learning and Assessment},
	volume = {2},
	number = {5},
	year = {2003},
	pages = {1-15},
	abstract = {Content balancing is often a practical consideration in the design of computerized adaptive testing (CAT). This study compared three content balancing methods, namely, the constrained CAT (CCAT), the modified constrained CAT (MCCAT), and the modified multinomial model (MMM), under various conditions of test length and target maximum exposure rate. Results of a series of simulation studies indicate that there is no systematic effect of content balancing method in measurement efficiency and pool utilization. However, among the three methods, the MMM appears to consistently over-expose fewer items.},
	author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.}
}
@conference {1003,
	title = {Computerized adaptive testing: A comparison of three content balancing methods},
	booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education},
	year = {2003},
	note = {{PDF file, 227 KB}},
	address = {Chicago IL},
	author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T. and Wen. Z.}
}
@conference {1002,
	title = {Comparing three item selection approaches for computerized adaptive testing with content balancing requirement},
	booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education},
	year = {2002},
	note = {{PDF file, 226 KB}},
	address = {New Orleans LA},
	author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.}
}
@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}
}
@article {502,
	title = {A comparison of item selection rules at the early stages of computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {24},
	year = {2000},
	pages = {241-255},
	author = {Chen, S.Y. and Ankenmann, R. D. and Chang, Hua-Hua}
}
@article {70,
	title = {A comparison of item selection rules at the early stages of computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {24},
	number = {3},
	year = {2000},
	pages = {241-255},
	abstract = {The effects of 5 item selection rules--Fisher information (FI), Fisher interval information (FII), Fisher information with a posterior distribution (FIP), Kullback-Leibler information (KL), and Kullback-Leibler information with a posterior distribution (KLP)--were compared with respect to the efficiency and precision of trait (θ) estimation at the early stages of computerized adaptive testing (CAT). FII, FIP, KL, and KLP performed marginally better than FI at the early stages of CAT for θ=-3 and -2. For tests longer than 10 items, there appeared to be no precision advantage for any of the selection rules. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {Adaptive Testing, Computer Assisted Testing, Item Analysis (Test), Statistical Estimation computerized adaptive testing},
	author = {Chen, S-Y. and Ankenmann, R. D. and Chang, Hua-Hua}
}
@conference {998,
	title = {Content balancing in stratified computerized adaptive testing designs},
	booktitle = {Paper presented at the annual meeting of the American Educational Research Association},
	year = {2000},
	note = {{PDF file, 427 KB}},
	address = {New Orleans, LA},
	author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.}
}
@conference {1195,
	title = {A comparison of two methods of controlling item exposure in computerized adaptive testing},
	booktitle = {Paper presented at the meeting of the American Educational Research Association. San Diego CA.},
	year = {1998},
	author = {Tang, L. and Jiang, H. and Chang, Hua-Hua}
}
