Evaluating Human Scoring Using Generalizability Theory

Autor/inn/en	Bimpeh, Yaw; Pointer, William; Smith, Ben Alexander; Harrison, Liz
Titel	Evaluating Human Scoring Using Generalizability Theory
Quelle	In: Applied Measurement in Education, 33 (2020) 3, S.198-209 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2020.1750403
Schlagwörter	Scoring; Generalizability Theory; Interrater Reliability; Foreign Countries; Test Items; High Stakes Tests; United Kingdom + Suchen Sie Ihr Suchwort? Bewertung; Interrater-Reliabilität; Ausland; Test content; Testaufgabe; Großbritannien
Abstract	Many high-stakes examinations in the United Kingdom (UK) use both constructed-response items and selected-response items. We need to evaluate the inter-rater reliability for constructed-response items that are scored by humans. While there are a variety of methods for evaluating rater consistency across ratings in the psychometric literature, we apply generalizability theory (G theory) to data from routine monitoring of ratings to derive an estimate for inter-rater reliability. UK examinations use a combination of double or multiple rating for routine monitoring, creating a more complex design that consists of cross-pairing of raters and overlapping of raters for different groups of candidates or items. This sampling design is neither fully crossed nor is it nested. Each double- or multiple-scored item takes a different set of candidates, and the number of sampled candidates per item varies. Therefore, the standard G theory method, and its various forms for estimating inter-rater reliability, cannot be directly applied to the operational data. We propose a method that takes double or multiple rating data as given and analyzes the datasets at the item level in order to obtain more accurate and stable variance component estimates. We adapt the variance component in observed scores for an unbalanced one-facet crossed design with some missing observations. These estimates can be used to make inferences about the reliability of the entire scoring process. We illustrate the proposed method by applying it to real scoring data. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01