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A Comparative Study of On‐line Pretest Item—Calibration/Scaling Methods in Computerized Adaptive Testing
Author(s) -
Ban JaeChun,
Hanson Bradley A.,
Wang Tianyou,
Yi Qing,
Harris Deborah J.
Publication year - 2001
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.1745-3984.2001.tb01123.x
Subject(s) - statistics , computerized adaptive testing , sample size determination , calibration , scaling , computation , mathematics , sample (material) , computer science , algorithm , psychometrics , chromatography , chemistry , geometry
The purpose of this study was to compare and evaluate five on‐line pretest item‐calibration/scaling methods in computerized adaptive testing (CAT): marginal maximum likelihood estimate with one EM cycle (OEM), marginal maximum likelihood estimate with multiple EM cycles (MEM), Stocking's Method A, Stocking's Method B, and BILOG/Prior. The five methods were evaluated in terms of item‐parameter recovery, using three different sample sizes (300, 1000 and 3000). The MEM method appeared to be the best choice among these, because it produced the smallest parameter‐estimation errors for all sample size conditions. MEM and OEM are mathematically similar, although the OEM method produced larger errors. MEM also was preferable to OEM, unless the amount of time involved in iterative computation is a concern. Stocking's Method B also worked very well, but it required anchor items that either would increase test lengths or require larger sample sizes depending on test administration design. Until more appropriate ways of handling sparse data are devised, the BILOG/Prior method may not be a reasonable choice for small sample sizes. Stocking's Method A had the largest weighted total error, as well as a theoretical weakness (i.e., treating estimated ability as true ability); thus, there appeared to be little reason to use it.