Publication at Faculty of Mathematics and Physics, Faculty of Education |
2019
Abstract
Many methods for detection of differential item functioning (DIF) are derived from comparison of item characteristic curves. Most of these approaches are limited in detection of DIF caused either by difference in difficulty or discrimination parameters with the exception of 3-4 parametric logistic IRT (Birnbaum, 1968; Barton & Lord, 1981) and non-IRT models (Drabinová & Martinková, 2017).
We propose a novel approach using kernel smoothing estimation based on nearest neighbors. We argue that newly proposed approach has a great application potential, as it also considers the differences between groups in probability of guessing correct answer or in probability of inattention when answering.