A Response Time Process Model for Not-Reached and Omitted Items

Autor/inn/en	Lu, Jing; Wang, Chun
Titel	A Response Time Process Model for Not-Reached and Omitted Items
Quelle	In: Journal of Educational Measurement, 57 (2020) 4, S.584-620 (37 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12270
Schlagwörter	Item Response Theory; Test Items; Standardized Tests; Responses; Testing Problems; Achievement Tests; International Assessment; Foreign Countries; Secondary School Students; Mathematics Tests; Bayesian Statistics; Computation; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Test content; Testaufgabe; Standadised tests; Standardisierter Test; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Sekundarschüler
Abstract	Item nonresponses are prevalent in standardized testing. They happen either when students fail to reach the end of a test due to a time limit or quitting, or when students choose to omit some items strategically. Oftentimes, item nonresponses are nonrandom, and hence, the missing data mechanism needs to be properly modeled. In this paper, we proposed to use an innovative item response time model as a cohesive missing data model to account for the two most common item nonresponses: not-reached items and omitted items. In particular, the new model builds on a behavior process interpretation: a person chooses to skip an item if the required effort exceeds the implicit time the person allocates to the item (Lee & Ying, 2015; Wolf, Smith, & Birnbaum, 1995), whereas a person fails to reach the end of the test due to lack of time. This assumption was verified by analyzing the 2015 PISA computer-based mathematics data. Simulation studies were conducted to further evaluate the performance of the proposed Bayesian estimation algorithm for the new model and to compare the new model with a recently proposed "speed-accuracy + omission" model (Ulitzsch, von Davier, & Pohl, 2019). Results revealed that all model parameters could recover properly, and inadequately accounting for missing data caused biased item and person parameter estimates. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01