a-Stratified CAT design with content blocking.
Content balancing is often required in the development and implementation of computerized adaptive tests (CATs). In the current study, we propose a modified a-stratified method, the a-stratified method with content blocking. As a further refinement of a-stratified CAT designs, the new method incorporates content specifications into item pool stratification. Simulation studies were conducted to compare the new method with three previous item selection methods: the a-stratified method; the a-stratified with b-blocking method; and the maximum Fisher information method with Sympson-Hetter exposure control. The results indicated that the refined a-stratified design performed well in reducing item overexposure rates, balancing item usage within the pool, and maintaining measurement precision, in a situation where all four procedures were forced to balance content.
Yi
Qing
Q
ACT, Inc., Iowa City. IA 52243, USA. qing.yi@act.org
Chang
Hua-Hua
HH
D003627:Data Interpretation, Statistical; D004521:Educational Measurement / Q000706:statistics & numerical data*; D004522:Educational Status; D006801:Humans; D008432:Mathematical Computing; D008433:Mathematics