A Robust Method for Detecting Item Misfit in Large-Scale Assessments

Autor/inn/en	von Davier, Matthias; Bezirhan, Ummugul
Titel	A Robust Method for Detecting Item Misfit in Large-Scale Assessments
Quelle	In: Educational and Psychological Measurement, 83 (2023) 4, S.740-765 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (von Davier, Matthias)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221105819
Schlagwörter	Robustness (Statistics); Test Items; Item Analysis; Goodness of Fit; Testing Problems; Statistical Distributions; Educational Assessment; Item Response Theory + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Test content; Testaufgabe; Itemanalyse; Wahrscheinlichkeitsverteilung; Education; assessment; Bewertungssystem; Item-Response-Theorie
Abstract	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. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01