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.

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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
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article

Application of Bayesian Decision Theory in Detecting Test Fraud

Sandip Sinharay, Matthew Johnson
Applied Psychological Measurement
Imbalanced Data Classification Techniques
article

Application of Bayesian Decision Theory in Detecting Test Fraud

Sandip Sinharay, Matthew Johnson
article en

Abstract

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.

Applied Psychological Measurement
Educational Testing Service (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 99%
Imbalanced Data Classification Techniques
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