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ERIC Number: ED464110
Record Type: Non-Journal
Publication Date: 2002-Apr-7
Pages: 26
Abstractor: N/A
Reference Count: N/A
ISBN: N/A
ISSN: N/A
Practical Considerations about Expected A Posteriori Estimation in Adaptive Testing: Adaptive A Priori, Adaptive Correction for Bias, and Adaptive Integration Interval.
Raiche, Gilles; Blais, Jean-Guy
In a computerized adaptive test (CAT), it would be desirable to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Decreasing the number of items is accompanied, however, by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. G. Raiche (2000) has suggested that it is possible to reduce the bias, and even the standard error of the estimate, by applying to each provisional estimation one of a combination of these strategies: (1) the adaptive correction for bias proposed by R. Bock and R. Mislevy (1982); (2) adaptive a priori estimate; and (3) adaptive integration interval. A simulation study was conducted to explore these approaches. One thousand administrations of a CAT were simulated for varying proficiency levels. Expected a priori estimation of a unidimensional Rasch model with 40 quadrature points was used for provisional and final estimates. Simulation results demonstrate that bias can be reduced more effectively by using an adaptive adjustment of the estimation procedure than by considering only the final correction for bias proposed by Bock and Mislevey. Findings suggest the use of these adaptive estimate strategies in adaptive testing, especially the adaptive a priori combined with the adaptive integration interval. (Contains 2 figures, 12 tables, and 13 references.) (SLD)
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A