Modeling multiple item context effects with generalized linear mixed models.

Autor/inn/en	Rose, Norman; Nagy, Gabriel; Nagengast, Benjamin; Frey, Andreas; Becker, Michael
Titel	Modeling multiple item context effects with generalized linear mixed models.
Quelle	In: Frontiers in psychology, 10 (2019) Art. 248, 13 S. PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1664-1078
Schlagwörter	Test; Kontext; Datenanalyse; Effekt; Item; Modell; Deutschland
Abstract	Item context effects refer to the impact of features of a test on an examinee's item responses. These effects cannot be explained by the abilities measured by the test. Investigations typically focus on only a single type of item context effects, such as item position effects, or mode effects, thereby ignoring the fact that different item context effects might operate simultaneously. In this study, two different types of context effects were modeled simultaneously drawing on data from an item calibration study of a multidimensional computerized test (N = 1,632) assessing student competencies in mathematics, science, and reading. We present a generalized linear mixed model (GLMM) parameterization of the multidimensional Rasch model including item position effects (distinguishing between within-block position effects and block position effects), domain order effects, and the interactions between them. Results show that both types of context effects played a role, and that the moderating effect of domain orders was very strong. The findings have direct consequences for planning and applying mixed domain assessment designs. (DIPF/Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2021/4