Application of Bayesian Decision Theory in Detecting Test Fraud
This article suggests a new approach based on Bayesian decision theory (e.g., Cronbach & Gleser, 1965; Ferguson, 1967) for detection of test fraud. The approach leads to a simple decision rule that involves the computation of the posterior probability that an examinee committed test fraud given the data. The suggested approach was applied to a real data set that involved actual test fraud.
Authors
- Sandip Sinharay (ORCID: https://orcid.org/0000-0003-4491-8510)
- Matthew Johnson (ORCID: https://orcid.org/0000-0003-3157-4165)
Institutions
- Educational Testing Service (US)
Publication Details
- Journal
- Applied Psychological Measurement
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1177/01466216251316559
- Primary Topic
- Imbalanced Data Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00