Reducing Bias in CAT Trait Estimation: A Comparison of Approaches

Publisher Website
Google Scholar
Abstract
The use of a beta prior in trait estimation was extended to the maximum expected a posteriori (MAP) method of Bayesian estimation. This new method, called essentially unbiased MAP (EU-MAP), was compared with MAP (using a standard normal prior), essentially unbiased expected a posteriori, weighted likelihood, and maximum likelihood estimation methods. Comparisons were made based on the effects that the shape of prior distributions, different item bank characteristics, and practical constraints had on bias, standard error, and root-mean-square error (RMSE). Overall, EU-MAP performed best. This new method significantly reduced bias in fixed-length tests (though with a slight increase in RMSE) and performed reasonably well when a fixed posterior variance termination rule was used. Practical constraints had little effect on the bias of this method.