Year: 2026
Type: article
Abstract: In the field of educational and psychological measurement, computerized adaptive testing (CAT) is flexible and convenient, but its reliance on repeatedly administered, pre-calibrated items makes it vulnerable to item exposure and pre-knowledge. We propose a method called CHeater Identification using Interim Person fit Statistic (CHIPS) and a slight modification of it, called Modified CHIPS (M-CHIPS), both designed to identify and limit cheaters during test administration. The methodological novelty lies in redefining a likelihood-based person-fit statistic for response times so that it becomes computable at each adaptive step. CHIPS replaces parameters that traditionally require full-test MCMC estimation with interim maximum-likelihood estimators of speed and expected log-response times, yielding a statistic (IPS) with an analytically tractable asymptotic $${{\upchi}}^{2}$$ distribution. This allows the IPS to be embedded as a constraint within the Shadow Test Approach, producing a dynamic item-selection algorithm that switches between databases based on real-time evidence of item pre-knowledge. M-CHIPS further introduces an early-stage speed-based intervention to improve detectability under extreme cheating scenarios. A simulation study evaluates estimation accuracy, error rates, and computational performance under varying pre-knowledge levels, ability–speed correlations, and test-length settings. Results show that the proposed methods substantially improve ability estimation for cheaters without affecting non-cheaters, demonstrating the statistical and algorithmic effectiveness of incorporating interim fit statistics into adaptive testing.
Source: Computational Statistics
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
Funder: Università di Bologna
OpenAlex ID: W7138293381