Data Sparseness and Online Pretest Item Calibration [electronic resource] : Scaling Methods in CAT. ACT Research Report Series / Jae-Chun Ban, Bradley A. Hanson and Qing Yi.

The purpose of this study was to compare and evaluate three online pretest item calibration/scaling methods in terms of item parameter recovery when the item responses to the pretest items in the pool would be sparse. The three methods considered were the marginal maximum likelihood estimate with on...

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Bibliographic Details

Online Access:	Full Text (via ERIC)
Main Author:	Ban, Jae-Chun
Corporate Author:	American College Testing Program
Other Authors:	Hanson, Bradley A., Yi, Qing, Harris, Deborah J.
Format:	Electronic eBook
Language:	English
Published:	[Place of publication not identified] : Distributed by ERIC Clearinghouse, 2002.
Subjects:	Adaptive Testing. Computer Assisted Testing. Data Analysis. Error of Measurement. Estimation (Mathematics) Maximum Likelihood Statistics. Online Systems. Pretests Posttests. Scaling. Simulation. Test Items.