Abstract
Profile analysis is one of the main tools for studying whether differential item functioning can be related to specific features of test items. While relevant, profile analysis in its current form has two restrictions that limit its usefulness in practice: It assumes that all test items have equal discrimination parameters, and it does not test whether conclusions about the item-feature effects generalize outside of the considered set of items. This article addresses both of these limitations, by generalizing profile analysis to work under the two-parameter logistic model and by proposing a permutation test that allows for generalizable conclusions about item-feature effects. The developed methods are evaluated in a simulation study and illustrated using Programme for International Student Assessment 2015 Science data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
