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Data Sparseness and On‐Line Pretest Item Calibration‐Scaling Methods in CAT
Author(s) -
Ban JaeChun,
Hanson Bradley A.,
Yi Qing,
Harris Deborah J.
Publication year - 2002
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2002.tb01174.x
Subject(s) - statistics , maximum likelihood , calibration , mathematics , scaling , maximization , restricted maximum likelihood , expectation–maximization algorithm , line (geometry) , computer science , mathematical optimization , geometry
The purpose of this study was to compare and evaluate three on‐line pretest item calibration‐scaling methods (the marginal maximum likelihood estimate with one expectation maximization [EM] cycle [OEM] method, the marginal maximum likelihood estimate with multiple EM cycles [MEM] method, and Stocking's Method B) in terms of itern parameter recovery when the item responses to the pretest items in the pool are sparse. Simulations of computerized adaptive tests were used to evaluate the results yielded by the three methods. The MEM method produced the smallest average total error in parameter estimation, and the OEM method yielded the largest total error.