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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8725052 | PMC |
| http://dx.doi.org/10.1177/0013164421994893 | DOI Listing |
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Article Synopsis
- Test tampering in educational assessments is a significant concern, prompting the need for data forensic analyses to identify potential cheating.* -
- The article introduces a new erasure detection index that utilizes item response theory to assess discrepancies between actual and expected erasures based on a test taker's ability.* -
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