Identifying Effortful Individuals with Mixture Modeling Response Accuracy and Response Time Simultaneously to Improve Item Parameter Estimation

Autor/inn/en	Liu, Yue; Cheng, Ying; Liu, Hongyun
Titel	Identifying Effortful Individuals with Mixture Modeling Response Accuracy and Response Time Simultaneously to Improve Item Parameter Estimation
Quelle	In: Educational and Psychological Measurement, 80 (2020) 4, S.775-807 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Liu, Yue)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164419895068
Schlagwörter	Item Response Theory; Test Wiseness; Response Style (Tests); Reaction Time; Accuracy; Test Items; Measurement Techniques + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Antwortverhalten; Reaktionsvermögen; Test content; Testaufgabe; Messtechnik
Abstract	The responses of non-effortful test-takers may have serious consequences as non-effortful responses can impair model calibration and latent trait inferences. This article introduces a mixture model, using both response accuracy and response time information, to help differentiating non-effortful and effortful individuals, and to improve item parameter estimation based on the effortful group. Two mixture approaches are compared with the traditional response time mixture model (TMM) method and the normative threshold 10 (NT10) method with response behavior effort criteria in four simulation scenarios with regard to item parameter recovery and classification accuracy. The results demonstrate that the mixture methods and the TMM method can reduce the bias of item parameter estimates caused by non-effortful individuals, with the mixture methods showing more advantages when the non-effort severity is high or the response times are not lognormally distributed. An illustrative example is also provided. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01