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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| Format: | Electronic eBook |
| Language: | English |
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2002.
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| Summary: | 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 one EM cycle (OEM) method, the marginal maximum likelihood estimate with multiple EM cycles (MEM) method, and Stocking's Method B. The three methods were evaluated using simulations of data from computerized adaptive tests (CAT). The MEM method produced the smallest average total error in recovering the 240 pretest item characteristic curves. Stocking's Method B yielded the second smallest average total error in parameter estimation. In terms of scale maintenance, the MEM method and Stocking's Method B performed well in keeping with the scale of the pretest items on the same scale as that of the true parameters. With the OEM method, the scale of the pretest item parameter estimates deviated from that of the true parameters. (Contains 1 figure, 4 tables, and 14 references.) (Author/SLD) |
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| Item Description: | ERIC Document Number: ED462418. Availability: ACT Research Report Series, P.O. Box 168, Iowa City, IA 52243-0168. Tel: 319-337-1028; Web site: http://www.act.org. |
| Physical Description: | 23 pages. |