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Autor/inn/enHecht, Martin; Weirich, Sebastian; Siegle, Thilo; Frey, Andreas
TitelModeling booklet effects for nonequivalent group designs in large-scale assessment.
QuelleIn: Educational and psychological measurement, 75 (2015) 4, S. 568-584Infoseite zur Zeitschrift
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ZusatzinformationForschungsdaten, Studiendetails und Erhebungsinstrumente
Spracheenglisch
Dokumenttyponline; gedruckt; Zeitschriftenaufsatz
ISSN0013-1644; 1552-3888
DOI10.1177/0013164414554219
SchlagwörterRasch-Modell; Statistisches Modell; Bildungsforschung; Empirische Untersuchung; Längsschnittuntersuchung; Test; Schuljahr 09; Schuljahr 10; Kontext; Matrix; Statistik; Effekt; Modell; Testen; PISA (Programme for International Student Assessment)
AbstractMultiple matrix designs are commonly used in large-scale assessments to distribute test items to students. These designs comprise several booklets, each containing a subset of the complete item pool. Besides reducing the test burden of individual students, using various booklets allows aligning the difficulty of the presented items to the assumed performance level of examined subgroups. While this may improve measurement precision and students' test-taking motivation, using several booklets might influence response behavior and thus constitute a potential source of unwanted variation. To provide guidance to identify and model booklet effects, this study presents statistical models accounting for booklet effects and applies these models in a large-scale assessment setting. Three models are derived from the Rasch model employing the generalized linear mixed models framework. The models were applied to data from a national educational standards assessment study for scientific competence. A total of 1,021 items were compiled to 74 booklets distributed to a sample of 9,044 students of Grades 9 and 10. The results revealed a small but nonnegligible booklet effect. For further large-scale assessment studies, it is recommended to examine whether booklet effects occur and to adequately account for them in the subsequent analyses where necessary. (Orig.).
Erfasst vonExterner Selbsteintrag
Update2016/2
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