Estimating Variance Components from Sparse Data Matrices in Large-Scale Educational Assessments

Autor/in	DeMars, Christine
Titel	Estimating Variance Components from Sparse Data Matrices in Large-Scale Educational Assessments
Quelle	In: Applied Measurement in Education, 28 (2015) 1, S.1-13 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2014.973562
Schlagwörter	Educational Assessment; Measurement; Data; Generalizability Theory; Scores; Computation; Test Bias; Reliability; Error of Measurement + Suchen Sie Ihr Suchwort? Education; assessment; Bewertungssystem; Messverfahren; Daten; Testkritik; Reliabilität; Messfehler
Abstract	In generalizability theory studies in large-scale testing contexts, sometimes a facet is very sparsely crossed with the object of measurement. For example, when assessments are scored by human raters, it may not be practical to have every rater score all students. Sometimes the scoring is systematically designed such that the raters are consistently grouped throughout the scoring, so that the data can be analyzed as raters nested within teams. Other times, rater pairs are randomly assigned for each student, such that each rater is paired with many other raters at different times. One possibility for this scenario is to treat the data as if raters were nested within students. Because the raters are not truly independent across all students, the resulting variance components could be somewhat biased. This study illustrates how the bias will tend to be small in large-scale studies. (As Provided).
Anmerkungen	Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01