Literaturnachweis - Detailanzeige
| Autor/in | Xiao, Beiling |
|---|---|
| Titel | Dichotomous Search Strategies for Computerized Adaptive Testing. |
| Quelle | (1990), (21 Seiten) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; Monographie |
| Schlagwörter | Ability Identification; Adaptive Testing; Computer Assisted Testing; Equations (Mathematics); Estimation (Mathematics); Mathematical Models; Maximum Likelihood Statistics; Monte Carlo Methods; Robustness (Statistics); Scoring; Search Strategies; Simulation; Test Construction; Test Items; Testing Problems |
| Abstract | Dichotomous search strategies (DSSs) for computerized adaptive testing are similar to golden section search strategies (GSSSs). Each middle point of successive search regions is a testing point. After each item is administered, the subject's obtained score is compared with the expected score at successive testing points. If the subject's obtained score does not exceed a confidence interval of an expected score at a testing point, the subject's current ability estimate is assumed to be equal to that of the testing point. Otherwise, the upper or lower half of the search region is discarded and the process is continued until the test-taker's current ability estimate is determined and the next item is selected. Monte Carlo studies with 3,300 subjects in 33 ability levels using one-parameter and three-parameter models to compare the efficiency and accuracy of DSSs, GSSSs, and maximum likelihood estimate strategies (MLESs) indicated that all three measured well in the one-parameter situation, but that DSSs and GSSSs were more accurate and efficient than were MLESs for the three-parameter model. Both DSSs and GSSSs were more robust against guessing and simpler to operate than were MLESs. Twelve graphs illustrate the study. (Author/SLD) |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
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