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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Analysis

Background:

  • Unproctored Internet Tests (UIT) are susceptible to cheating.
  • Verification Tests (VT) are used to mitigate cheating.
  • Accurate detection of cheating is crucial for test validity.

Purpose of the Study:

  • To compare the effectiveness of five statistical methods for detecting cheating in Computerized Adaptive Tests (CATs).
  • To evaluate the performance of Guo and Drasgow's Z-test, Adaptive Measure of Change (AMC), Likelihood Ratio Test (LRT), Score Test, and Modified Signed Likelihood Ratio Test (MSLRT).

Main Methods:

  • Simulated data for honest and cheating candidates in UIT and VT scenarios.
  • Applied hypothesis testing to analyze simulated data.
  • Calculated type I error rates and power rates for each statistical procedure.

Main Results:

  • All tested procedures, except the Score Test, provided accurate results regarding type I error rates.
  • The Modified Signed Likelihood Ratio Test (MSLRT) demonstrated superior power rates in identifying cheating.
  • Differences in type I error rates were observed among some procedures.

Conclusions:

  • The Modified Signed Likelihood Ratio Test (MSLRT) is recommended as the optimal method for detecting cheating.
  • MSLRT offers the highest power rate for identifying dishonest responses.
  • MSLRT maintains a suitable type I error rate, balancing detection accuracy and false positives.