Sanz Susana, Luzardo Mario, García Carmen, Abad Francisco J
Universidad Autónoma de Madrid.
Psicothema. 2020 Nov;32(4):549-558. doi: 10.7334/psicothema2020.86.
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).
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.
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.
We consider the MSLRT to be the best test, as it has the highest power rate and a suitable type I error rate.
非监考的网络考试（UIT）容易受到考生为获得更高分数而进行的作弊企图的影响。为防止这种情况，随后会进行诸如验证测试（VT）等程序。本研究比较了用于检测计算机自适应测试（CAT）中作弊行为的五种统计方法：郭和德拉斯戈的Z检验、变化自适应度量（AMC）、似然比检验（LRT）、得分检验和修正符号似然比检验（MSLRT）。
我们模拟了诚实考生和作弊考生在UIT和VT中的数据。诚实考生以其真实能力水平回答UIT和VT，而作弊考生只回答VT，并模拟了不同程度的作弊情况。我们应用了假设检验，并获得了I型错误率和检验功效。
虽然我们发现某些程序之间的I型错误率存在差异，但除得分检验外，所有程序报告的结果都相当准确。获得的检验功效表明MSLRT在检测作弊方面具有优越性。
我们认为MSLRT是最佳检验方法，因为它具有最高的检验功效和合适的I型错误率。
