The primary purpose of this research is to examine the impact of estimation methods, actual latent trait distributions, and item pool characteristics on the performance of a simulated computerized adaptive testing (CAT) system. In this study, three estimation procedures are compared for accuracy of estimation: maximum likelihood estimation (MLE), expected a priori (EAP), and Warm's weighted likelihood estimation (WLE). Some research has shown that MLE and EAP perform equally well under certain conditions in polytomous CAT systems, such that they match the actual latent trait distribution. However, little research has compared these methods when prior estimates of. distributions are extremely poor. In general, it appears that MLE, EAP, and WLE procedures perform equally well when using an optimal item pool. However, the use of EAP procedures may be advantageous under nonoptimal testing conditions when the item pool is not appropriately matched to the examinees.
Incorrect handling of item parameter drift during the equating process can result in equating error. If the item parameter drift is due to construct-irrelevant factors, then inclusion of these items in the estimation of the equating constants can be expected to result in equating error. On the other hand, if the item parameter drift is related to the construct being measured, then removal of those items from the estimation of the equating constants can be expected to result in equating error. The effect of incorrect handling of item parameter drift on equating has been examined via simulation studies and other methods by a number of authors with mixed results. In this article, this effect is assessed by estimating the expected equating error under the assumptions that the three-parameter item response theory model is used for calibration—equating.
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