ERIC Number: EJ1381818
Record Type: Journal
Publication Date: 2023-Aug
Pages: 26
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0013-1644
EISSN: EISSN-1552-3888
Available Date: N/A
A Robust Method for Detecting Item Misfit in Large-Scale Assessments
von Davier, Matthias; Bezirhan, Ummugul
Educational and Psychological Measurement, v83 n4 p740-765 Aug 2023
Viable methods for the identification of item misfit or Differential Item Functioning (DIF) are central to scale construction and sound measurement. Many approaches rely on the derivation of a limiting distribution under the assumption that a certain model fits the data perfectly. Typical DIF assumptions such as the monotonicity and population independence of item functions are present even in classical test theory but are more explicitly stated when using item response theory or other latent variable models for the assessment of item fit. The work presented here provides a robust approach for DIF detection that does not assume perfect model data fit, but rather uses Tukey's concept of contaminated distributions. The approach uses robust outlier detection to flag items for which adequate model data fit cannot be established.
Descriptors: Robustness (Statistics), Test Items, Item Analysis, Goodness of Fit, Testing Problems, Statistical Distributions, Educational Assessment, Item Response Theory
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A