The Multidimensionality of Measurement Bias in High-Stakes Testing: Using Machine Learning to Evaluate Complex Sources of Differential Item Functioning

Autor/in	Belzak, William C. M.
Titel	The Multidimensionality of Measurement Bias in High-Stakes Testing: Using Machine Learning to Evaluate Complex Sources of Differential Item Functioning
Quelle	In: Educational Measurement: Issues and Practice, 42 (2023) 1, S.24-33 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Belzak, William C. M.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0731-1745
DOI	10.1111/emip.12486
Schlagwörter	Test Bias; High Stakes Tests; Artificial Intelligence; Test Items; Measurement + Suchen Sie Ihr Suchwort? Testkritik; Künstliche Intelligenz; Test content; Testaufgabe; Messverfahren
Abstract	Test developers and psychometricians have historically examined measurement bias and differential item functioning (DIF) across a single categorical variable (e.g., gender), independently of other variables (e.g., race, age, etc.). This is problematic when more complex forms of measurement bias may adversely affect test responses and, ultimately, bias test scores. Complex forms of measurement bias include conditional effects, interactions, and mediation of background information on test responses. I propose a multidimensional, person-specific perspective of measurement bias to explain how complex sources of bias can manifest in the assessment of human knowledge, skills, and abilities. I also describe a data-driven approach for identifying key sources of bias among many possibilities--namely, a machine learning method commonly known as regularization. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01