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Ali, Usama S.; Chang, Hua-Hua – ETS Research Report Series, 2014
Adaptive testing is advantageous in that it provides more efficient ability estimates with fewer items than linear testing does. Item-driven adaptive pretesting may also offer similar advantages, and verification of such a hypothesis about item calibration was the main objective of this study. A suitability index (SI) was introduced to adaptively…
Descriptors: Adaptive Testing, Simulation, Pretests Posttests, Test Items
Chen, Ping; Xin, Tao; Wang, Chun; Chang, Hua-Hua – Psychometrika, 2012
Item replenishing is essential for item bank maintenance in cognitive diagnostic computerized adaptive testing (CD-CAT). In regular CAT, online calibration is commonly used to calibrate the new items continuously. However, until now no reference has publicly become available about online calibration for CD-CAT. Thus, this study investigates the…
Descriptors: Computer Assisted Testing, Adaptive Testing, Diagnostic Tests, Cognitive Tests
Wang, Chun; Chang, Hua-Hua; Huebner, Alan – Journal of Educational Measurement, 2011
This paper proposes two new item selection methods for cognitive diagnostic computerized adaptive testing: the restrictive progressive method and the restrictive threshold method. They are built upon the posterior weighted Kullback-Leibler (KL) information index but include additional stochastic components either in the item selection index or in…
Descriptors: Test Items, Adaptive Testing, Computer Assisted Testing, Cognitive Tests
Deng, Hui; Ansley, Timothy; Chang, Hua-Hua – Journal of Educational Measurement, 2010
In this study we evaluated and compared three item selection procedures: the maximum Fisher information procedure (F), the a-stratified multistage computer adaptive testing (CAT) (STR), and a refined stratification procedure that allows more items to be selected from the high a strata and fewer items from the low a strata (USTR), along with…
Descriptors: Computer Assisted Testing, Adaptive Testing, Selection, Methods
Cheng, Ying; Chang, Hua-Hua; Douglas, Jeffrey; Guo, Fanmin – Educational and Psychological Measurement, 2009
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…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
Zhang, Jinming; Chang, Hua-Hua – ETS Research Report Series, 2005
This paper compares the use of multiple pools versus a single pool with respect to test security against large-scale item sharing among some examinees in a computer-based test, under the assumption that a randomized item selection method is used. It characterizes the conditions under which employing multiple pools is better than using a single…
Descriptors: Comparative Analysis, Test Items, Item Banks, Computer Assisted Testing

Pastor, Dena A.; Dodd, Barbara G.; Chang, Hua-Hua – Applied Psychological Measurement, 2002
Studied the impact of using five different exposure control algorithms in two sizes of item pool calibrated using the generalized partial credit model. Simulation results show that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap and increase pool use, while degrading…
Descriptors: Adaptive Testing, Comparative Analysis, Computer Assisted Testing, Item Banks