Detecting Cheating Methods on Unproctored Internet Tests

doi:10.7334/psicothema2020.86

Detecting Cheating Methods on Unproctored Internet Tests

2020-11-01

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DOI：10.7334/psicothema2020.86

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Background: Unproctored Internet Tests (UIT) are vulnerable to cheating attempts by candidates to obtain higher scores. To prevent this, subsequent procedures such as a verification test ( VT) is carried out. This study compares five statistics used to detect cheating in Computerized Adaptive Tests (CATs): Guo and Drasgow's Z-test, the Adaptive Measure of Change (AMC), Likelihood Ratio Test (LRT), Score Test, and Modified Signed Likelihood Ratio Test (MSLRT). Method: We simulated data from honest and cheating candidates to the UIT and the VT. Honest candidates responded to the UIT and the VT with their real ability level, while cheating candidates responded only to the VT, and different levels of cheating were simulated. We applied hypothesis tests, and obtained type I error and power rates. Results: Although we found differences in type I error rates between some of the procedures, all procedures reported quite accurate results with the exception of the Score Test. The power rates obtained point to MSLRT's superiority in detecting cheating. Conclusions: We consider the MSLRT to be the best test, as it has the highest power rate and a suitable type I error rate.

Keyword:

Secure online testing

online assessment

verification tests

Detecting Cheating Methods on Unproctored Internet Tests

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