Anselmi, Robusto, and Cristante (2023) propose a novel approach to improving Computerized Adaptive Testing (CAT) by integrating unidimensional test batteries. This method aims to enhance both the accuracy and efficiency of ability estimation by dynamically updating prior estimates with each test response. Background Computerized Adaptive Testing has been a widely …
The study by Xiao and Bulut (2020) evaluates how different methods for handling missing data perform when estimating ability parameters from sparse datasets. Using two Monte Carlo simulations, the research highlights the strengths and limitations of four approaches, providing valuable insights for researchers and practitioners in educational and psychological measurement. …
