Linking errors in item response models quantify the dependence on the chosen items in means, standard deviations, or other distribution parameters. The jackknife approach is frequently employed in the computation of the linking error. However, this jackknife linking error could be computationally tedious if many items were involved. In this article, we provide an analytical approximation of the jackknife linking error. The newly proposed approach turns out to be computationally much less demanding. Moreover, the new linking error approach performed satisfactorily for datasets with at least 20 items.
Original languageEnglish
Issue number1
Pages (from-to)49-59
Publication statusPublished - 05.01.2023

    Research areas

  • Methodological research and machine learning - item response model, linking error, jackknife

ID: 7053685