Optimal number of strata for the stratified methods in computerized adaptive testing
Test security can be a major problem in computerized adaptive testing, as examinees can share information about the items they receive. Of the different item selection rules proposed to alleviate this risk, stratified methods are among those that have received most attention. In these methods, only...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Recursos: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/665573 |
| Acesso em linha: | http://hdl.handle.net/10486/665573 https://dx.doi.org/10.1017/sjp.2014.50 |
| Access Level: | acceso abierto |
| Palavra-chave: | Computerized adaptive testing Ítem exposure control Test security Stratified methods Psicología |
| Resumo: | Test security can be a major problem in computerized adaptive testing, as examinees can share information about the items they receive. Of the different item selection rules proposed to alleviate this risk, stratified methods are among those that have received most attention. In these methods, only low discriminative items can be presented at the beginning of the test and the mean information of the items increases as the test goes on. To do so, the item bank must be divided into several strata according to the information of the items. To date, there is no clear guidance about the optimal number of strata into which the item bank should be split. In this study, we will simulate conditions with different numbers of strata, from 1 (no stratification) to a number of strata equal to test length (maximum level of stratification) while manipulating the maximum exposure rate that no item should surpass (rmax) in its whole domain. In this way, we can plot the relation between test security and accuracy, making it possible to determine the number of strata that leads to better security while holding constant measurement accuracy. Our data indicates that the best option is to stratify into as many strata as possible. |
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