Precision of Warm’s Weighted Likelihood Estimates for a Polytomous Model in Computerized Adaptive Testing

Abstract

This monte carlo study evaluated the relative accuracy of Warm’s (1989) weighted likelihood estimate (WLE) compared to the maximum likelihood estimate (MLE), expected a posteriori (EAP) estimate, and maximum a posteriori (MAP) estimate. The generalized partial-credit model was used under a variety of computerized adaptive testing (CAT) conditions. The results indicated that WLE was more accurate than MLE with a fixed-length CAT, consistent with previous findings. WLE and MLE had smaller bias and larger standard errors than EAP and MAP. EAP was more accurate than MAP in a variety of CAT conditions. Although root mean squared errors were different among the four estimation methods, no statistically significant mean differences were found. EAP and MAP had advantages over WLE and MLE in terms of test efficiency. These results suggest that the test termination rule has more impact on the accuracy of θ estimation methods than does the item bank size.