A Mixture IRTree Model for Extreme Response Style: Accounting for Response Process Uncertainty

Autor/inn/en	Kim, Nana; Bolt, Daniel M.
Titel	A Mixture IRTree Model for Extreme Response Style: Accounting for Response Process Uncertainty
Quelle	In: Educational and Psychological Measurement, 81 (2021) 1, S.131-154 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kim, Nana) ORCID (Bolt, Daniel M.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164420913915
Schlagwörter	Item Response Theory; Response Style (Tests); Models; Test Items; Rating Scales; Generalization; Bayesian Statistics; Achievement Tests; Foreign Countries; International Assessment; Mathematics Tests; Elementary Secondary Education; Science Tests; Mathematics Achievement; Science Achievement; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Antwortverhalten; Analogiemodell; Test content; Testaufgabe; Rating-Skala; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz
Abstract	This paper presents a mixture item response tree (IRTree) model for extreme response style. Unlike traditional applications of single IRTree models, a mixture approach provides a way of representing the mixture of respondents following different underlying response processes (between individuals), as well as the uncertainty present at the individual level (within an individual). Simulation analyses reveal the potential of the mixture approach in identifying subgroups of respondents exhibiting response behavior reflective of different underlying response processes. Application to real data from the Students Like Learning Mathematics (SLM) scale of Trends in International Mathematics and Science Study (TIMSS) 2015 demonstrates the superior comparative fit of the mixture representation, as well as the consequences of applying the mixture on the estimation of content and response style traits. We argue that methodology applied to investigate response styles should attend to the inherent uncertainty of response style influence due to the likely influence of both response styles and the content trait on the selection of extreme response categories. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01