The current study evaluates three stopping rules for computerized adaptive testing (CAT): the predicted standard error reduction (PSER), the fixed-length, and the minimum SE using Andrich’s rating scale model with a survey to identify at-risk students. PSER attempts to reduce the number of items administered and increase measurement precision of the trait. Several variables are manipulated, such as trait distribution and item pool size, in order to evaluate how these conditions interact and potentially help improve the correct classification of students. The findings indicate that the PSER stopping rule may be preferred when wanting to correctly diagnose or classify students at-risk and at the same time alleviate test burden for those taking screening measures based on the rating scale model with smaller item pools.
Leroux, A. J., & Dodd, B. G. (2014). A comparison of stopping rules for computerized adaptive screening measures using the rating scale model. Journal of Applied Measurement, 15(3), 213–226.