The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level

Bibliographic Data

ID20286378
AuthorsLuyao Peng (0009-0005-1277-4251, Beijing Language and Culture University, Beijing, China, corresponding author), Sandip Sinharay (0000-0003-4491-8510, Educational Testing Service, Princeton, NJ, USA)
Year2022
Volume82
Issue1
Pages177-200
Publication date2022-02-01
Open AccessYes
TypeARTICLE
VenueEducational and Psychological Measurement (JOURNAL)
Journal identifiersISSN: 0013-1644 • E-ISSN: 1552-3888
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/0013164421994893
PMID34992311
OpenAlexW3139757804
LanguageEN
References cited24

Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of Wollack and Eckerly (2017) and Sinharay (2018) and suggests a new aggregate-level EDI by incorporating the empirical best linear unbiased predictor from the literature of linear mixed-effects models (e.g., McCulloch et al., 2008). A simulation study shows that the new EDI has larger power than the indices of Wollack and Eckerly (2017) and Sinharay (2018). In addition, the new index has satisfactory Type I error rates. A real data example is also included

Aggregate (composite) · Algorithm · Econometrics · Erasure · Index (typography) · Statistics · Type I and type II errors · Advanced Statistical Methods and Models · Computer Science · Imbalanced Data Classification Techniques · Mathematics

Tools

Open DOIOpen Access