ERIC Number: EJ723301
Record Type: Journal
Publication Date: 2005
Reference Count: 22
Computerized Adaptive Testing with the Partial Credit Model: Estimation Procedures, Population Distributions, and Item Pool Characteristics
Gorin, Joanna; Dodd, Barbara; Fitzpatrick, Steven; Shieh, Yann
Applied Psychological Measurement, v29 n6 p433-456 2005
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.
Descriptors: Adaptive Testing, Computer Assisted Testing, Computation, Test Items, Models, Computer Simulation, Evaluation Methods, Item Banks, Population Distribution
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
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