@conference {2077,
	title = {Building Affordable CD-CAT Systems for Schools To Address Today{\textquoteright}s Challenges In Assessment},
	booktitle = {Annual Conference of the International Association for Computerized Adaptive Testing},
	year = {2011},
	keywords = {affordability, CAT, cost},
	author = {Chang, Hua-Hua}
}
@article {250,
	title = {Incorporation of Content Balancing Requirements in Stratification Designs for Computerized Adaptive Testing},
	journal = {Educational and Psychological Measurement},
	volume = {63},
	number = {2},
	year = {2003},
	pages = {257-70},
	abstract = {Studied three stratification designs for computerized adaptive testing in conjunction with three well-developed content balancing methods. Simulation study results show substantial differences in item overlap rate and pool utilization among different methods. Recommends an optimal combination of stratification design and content balancing method. (SLD)},
	keywords = {computerized adaptive testing},
	author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.}
}
@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}
}
@article {60,
	title = {Hypergeometric family and item overlap rates in computerized adaptive testing},
	journal = {Psychometrika},
	volume = {67},
	number = {3},
	year = {2002},
	pages = {387-398},
	abstract = {A computerized adaptive test (CAT) is usually administered to small groups of examinees at frequent time intervals. It is often the case that examinees who take the test earlier share information with examinees who will take the test later, thus increasing the risk that many items may become known. Item overlap rate for a group of examinees refers to the number of overlapping items encountered by these examinees divided by the test length. For a specific item pool, different item selection algorithms may yield different item overlap rates. An important issue in designing a good CAT item selection algorithm is to keep item overlap rate below a preset level. In doing so, it is important to investigate what the lowest rate could be for all possible item selection algorithms. In this paper we rigorously prove that if every item had an equal possibility to be selected from the pool in a fixed-length CAT, the number of overlapping item among any α randomly sampled examinees follows the hypergeometric distribution family for α >= 1. Thus, the expected values of the number of overlapping items among any randomly sampled α examinee can be calculated precisely. These values may serve as benchmarks in controlling item overlap rates for fixed-length adaptive tests. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Adaptive Testing, Algorithms, Computer Assisted Testing, Taking, Test, Time On Task computerized adaptive testing},
	author = {Chang, Hua-Hua and Zhang, J.}
}
@article {55,
	title = {a-stratified multistage computerized adaptive testing with b blocking},
	journal = {Applied Psychological Measurement},
	volume = {25},
	number = {4},
	year = {2001},
	pages = {333-41},
	abstract = {Proposed a refinement, based on the stratification of items developed by D. Weiss (1973), of the computerized adaptive testing item selection procedure of H. Chang and Z. Ying (1999). Simulation studies using an item bank from the Graduate Record Examination show the benefits of the new procedure. (SLD)},
	keywords = {computerized adaptive testing},
	author = {Chang, Hua-Hua and Qian, J. and Yang, Z.}
}
@article {188,
	title = {Item selection in computerized adaptive testing: Should more discriminating items be used first?},
	journal = {Journal of Educational Measurement},
	volume = {38},
	number = {3},
	year = {2001},
	pages = {249-266},
	abstract = {During computerized adaptive testing (CAT), items are selected continuously according to the test-taker{\textquoteright}s estimated ability. Test security has become a problem because high-discrimination items are more likely to be selected and become overexposed. So, there seems to be a tradeoff between high efficiency in ability estimations and balanced usage of items. This series of four studies addressed the dilemma by focusing on the notion of whether more or less discriminating items should be used first in CAT. The first study demonstrated that the common maximum information method with J. B. Sympson and R. D. Hetter (1985) control resulted in the use of more discriminating items first. The remaining studies showed that using items in the reverse order, as described in H. Chang and Z. Yings (1999) stratified method had potential advantages: (a) a more balanced item usage and (b) a relatively stable resultant item pool structure with easy and inexpensive management. This stratified method may have ability-estimation efficiency better than or close to that of other methods. It is argued that the judicious selection of items, as in the stratified method, is a more active control of item exposure. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {ability, Adaptive Testing, Computer Assisted Testing, Estimation, Statistical, Test Items computerized adaptive testing},
	author = {Hau, Kit-Tai 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}
}
@article {59,
	title = {a-stratified multistage computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {23},
	number = {3},
	year = {1999},
	note = {Sage Publications, US},
	pages = {211-222},
	abstract = {For computerized adaptive tests (CAT) based on the three-parameter logistic mode it was found that administering items with low discrimination parameter (a) values early in the test and administering those with high a values later was advantageous; the skewness of item exposure distributions was reduced while efficiency was maintain in trait level estimation. Thus, a new multistage adaptive testing approach is proposed that factors a into the item selection process. In this approach, the items in the item bank are stratified into a number of levels based on their a values. The early stages of a test use items with lower as and later stages use items with higher as. At each stage, items are selected according to an optimization criterion from the corresponding level. Simulation studies were performed to compare a-stratified CATs with CATs based on the Sympson-Hetter method for controlling item exposure. Results indicated that this new strategy led to tests that were well-balanced, with respect to item exposure, and efficient. The a-stratified CATs achieved a lower average exposure rate than CATs based on Bayesian or information-based item selection and the Sympson-Hetter method. (PsycINFO Database Record (c) 2003 APA, all rights reserved).},
	keywords = {computerized adaptive testing},
	author = {Chang, Hua-Hua and Ying, Z.}
}
