@article {2109,
	title = {To Weight Or Not To Weight? Balancing Influence Of Initial Items In Adaptive Testing},
	journal = {Psychometrica},
	volume = {73},
	year = {2008},
	pages = {441-450},
	abstract = {<p>It has been widely reported that in computerized adaptive testing some examinees may get much lower scores than they would normally if an alternative paper-and-pencil version were given. The main purpose of this investigation is to quantitatively reveal the cause for the underestimation phenomenon. The logistic models, including the 1PL, 2PL, and 3PL models, are used to demonstrate our assertions. Our analytical derivation shows that, under the maximum information item selection strategy, if an examinee failed a few items at the beginning of the test, easy but more discriminating items are likely to be administered. Such items are ineffective to move the estimate close to the true theta, unless the test is sufficiently long or a variable-length test is used. Our results also indicate that a certain weighting mechanism is necessary to make the algorithm rely less on the items administered at the beginning of the test.</p>
},
	doi = {10.1007/S11336-007-9047-7},
	author = {Chang, H.-H. and Ying, Z.}
}
@conference {874,
	title = {Test-score comparability, ability estimation, and item-exposure control in computerized adaptive testing},
	booktitle = {Paper presented at the Annual meeting of the National Council on Measurement in Education},
	year = {2003},
	address = {Chicago IL},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@conference {873,
	title = {To weight or not to weight {\textendash} balancing influence of initial and later items in CAT},
	booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education},
	year = {2002},
	note = {{PDF file, 252 KB}},
	address = {New Orleans LA},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@article {56,
	title = {a-Stratified multistage computerized adaptive testing with b blocking},
	journal = {Applied Psychological Measurement},
	volume = {25},
	number = {4},
	year = {2001},
	pages = {333-341},
	abstract = {Chang \& Ying{\textquoteright}s (1999) computerized adaptive testing item-selection procedure stratifies the item bank according to a parameter values and requires b parameter values to be evenly distributed across all strata. Thus, a and b parameter values must be incorporated into how strata are formed. A refinement is proposed, based on Weiss{\textquoteright} (1973) stratification of items according to b values. Simulation studies using a retired item bank of a Graduate Record Examination test indicate that the new approach improved control of item exposure rates and reduced mean squared errors. },
	isbn = {0146-6216},
	author = {Chang, Hua-Hua and Qian, J. and Ying, Z.}
}
@conference {930,
	title = {Deriving a stopping rule for sequential adaptive tests},
	booktitle = {Paper presented at the annual meeting of the American Educational Research Association},
	year = {2001},
	note = {{PDF file, 111 KB}},
	address = {Seattle WA},
	author = {Grabovsky, I. and Chang, Hua-Hua and Ying, Z.}
}
@article {482,
	title = {On maximizing item information and matching difficulty with ability},
	journal = {Psychometrika},
	volume = {66},
	year = {2001},
	pages = {69-77},
	author = {Bickel, P. and Buyske, S. and Chang, Hua-Hua and Ying, Z.}
}
@article {495,
	title = {a-stratified multistage computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {23},
	year = {1999},
	pages = {211{\textendash}222},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@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.}
}
@article {497,
	title = {a-stratified multistage computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {23},
	year = {1999},
	pages = {211-222},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@conference {872,
	title = {Multi-stage CAT with stratified design},
	booktitle = {Paper presented at the annual meeting of the Psychometric Society. Gatlinburg TN.},
	year = {1997},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@article {496,
	title = {Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests},
	journal = {The Annals of Statistics.},
	year = {1997},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@conference {871,
	title = {Building a statistical foundation for computerized adaptive testing},
	booktitle = {Paper presented at the annual meeting of the Psychometric Society},
	year = {1996},
	address = {Banff, Alberta, Canada},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@article {58,
	title = {A global information approach to computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {20},
	number = {3},
	year = {1996},
	pages = {213-229},
	abstract = {based on Fisher information (or item information). At each stage, an item is selected to maximize the Fisher information at the currently estimated trait level (\&thetas;). However, this application of Fisher information could be much less efficient than assumed if the estimators are not close to the true \&thetas;, especially at early stages of an adaptive test when the test length (number of items) is too short to provide an accurate estimate for true \&thetas;. It is argued here that selection procedures based on global information should be used, at least at early stages of a test when \&thetas; estimates are not likely to be close to the true \&thetas;. For this purpose, an item selection procedure based on average global information is proposed. Results from pilot simulation studies comparing the usual maximum item information item selection with the proposed global information approach are reported, indicating that the new method leads to improvement in terms of bias and mean squared error reduction under many circumstances. Index terms: computerized adaptive testing, Fisher information, global information, information surface, item information, item response theory, Kullback-Leibler information, local information, test information.},
	isbn = {0146-6216},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@article {2032,
	title = {A Global Information Approach to Computerized Adaptive Testing},
	journal = {Applied Psychological Measurement},
	volume = {20},
	year = {1996},
	pages = {213-229},
	author = {Chang, H.-H. and Ying, Z.}
}
@booklet {1346,
	title = {Recursive maximum likelihood estimation, sequential design, and computerized adaptive testing},
	year = {1996},
	address = {Princeton NJ: Educational Testing Service},
	author = {Chang, Hua-Hua and Ying, Z.}
}
@conference {1286,
	title = {Recursive maximum likelihood estimation, sequential designs, and computerized adaptive testing},
	booktitle = {Paper presented at the Eleventh Workshop on Item Response Theory},
	year = {1995},
	address = {University of Twente, the Netherlands},
	author = {Ying, Z. and Chang, Hua-Hua}
}
