Reference
Anselmi, P., Robusto, E., & Cristante, F. (2023). Enhancing Computerized Adaptive Testing with Batteries of Unidimensional Tests. Applied Psychological Measurement, 47(3), 167-182. https://doi.org/10.1177/01466216231165301
Review
The article authored by Anselmi, Robusto, and Cristante (2023) introduces a pioneering procedure for Computerized Adaptive Testing (CAT) with unidimensional test batteries. The goal is to optimize the process by constantly updating the estimation of a given ability with every new response and, concurrently, the current estimations of all other abilities within the test battery. Their innovative approach integrates data from these abilities into an empirical prior, which subsequently undergoes regular updates.
In a bid to validate their approach, the researchers employed two simulation studies contrasting the performance of their suggested procedure against a standard CAT technique for unidimensional test batteries. Results indicated a notable uptick in accuracy for fixed-length CATs using the proposed procedure. Simultaneously, there was an observed shortening in test length for variable-length CATs. Notably, the enhancements in both accuracy and efficiency escalated proportionally with the correlation among the abilities evaluated by the test batteries.
While the study provides a promising avenue for the enhancement of CAT, the outcomes' dependence on the correlation between abilities measured by the test batteries may hint at limitations in its applicability. The reliance on simulation studies also indicates a need for real-world validations. Nonetheless, Anselmi et al.'s innovative approach offers a commendable step forward in refining CAT procedures, potentially yielding significant efficiencies in real-world applications, contingent upon further validation.
