Multidimensional Adaptive Testing with a Minimum Error-Variance Criterion
Adaptive testing under a multidimensional logistic response model is addressed. An algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood estimator of a linear combination of abilities of interest. The criterion results in a closed-form expression that is easy to ev...
| Veröffentlicht in: | Journal of Educational and Behavioral Statistics. - SAGE Publishing, 1976. - 24(1999), 4, Seite 398-412 |
|---|---|
| 1. Verfasser: | |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
1999
|
| Zugriff auf das übergeordnete Werk: | Journal of Educational and Behavioral Statistics |
| Schlagworte: | Adaptive Testing Item Response Theory Maximum-Likelihood Estimation Multidimensionality Mathematics Education Applied sciences Behavioral sciences Business |
| Zusammenfassung: | Adaptive testing under a multidimensional logistic response model is addressed. An algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood estimator of a linear combination of abilities of interest. The criterion results in a closed-form expression that is easy to evaluate. In addition, it is shown how the algorithm can be modified if the interest is in a test with a "simple ability structure". The statistical properties of the adaptive ML estimator are demonstrated for a two-dimensional item pool with several linear combinations of the abilities. |
|---|---|
| ISSN: | 19351054 |