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ERIC Number: ED462418
Record Type: Non-Journal
Publication Date: 2002-Jan
Pages: 23
Abstractor: N/A
Reference Count: N/A
ISBN: N/A
ISSN: N/A
Data Sparseness and Online Pretest Item Calibration/Scaling Methods in CAT. ACT Research Report Series.
Ban, Jae-Chun; Hanson, Bradley A.; Yi, Qing; Harris, Deborah J.
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)
ACT Research Report Series, P.O. Box 168, Iowa City, IA 52243-0168. Tel: 319-337-1028; Web site: http://www.act.org.
Publication Type: Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: American Coll. Testing Program, Iowa City, IA.