Accuracy of performance-test linking based on a many-facet Rasch model

Performance assessments, in which human raters assess examinee performance in practical tasks, have attracted much attention in various assessment contexts involving measurement of higher-order abilities. However, difficulty persists in that ability measurement accuracy strongly depends on rater and task characteristics such as rater severity and task difficulty. To resolve this problem, various item response theory (IRT) models incorporating rater and task parameters, including many-facet Rasch models (MFRMs), have been proposed. When applying such IRT models to datasets comprising results of multiple performance tests administered to different examinees, test linking is needed to unify the scale for model parameters estimated from individual test results. In test linking, test administrators generally need to design multiple tests such that raters and tasks partially overlap. The accuracy of linking under this design is highly reliant on the numbers of common raters and tasks. However, the numbers of common raters and tasks required to ensure high accuracy in test linking remain unclear, making it difficult to determine appropriate test designs. We therefore empirically evaluate the accuracy of IRT-based performance-test linking under common rater and task designs. Concretely, we conduct evaluations through simulation experiments that examine linking accuracy based on a MFRM while changing numbers of common raters and tasks with various factors that possibly affect linking accuracy.