Guessing During Testing is a Person Attribute Not an Instrument Parameter

The three-parameter logistic (3PL) model in item-response theory (IRT) has long been used to account for guessing in multiple-choice assessments through a fixed item-level parameter. However, this approach treats guessing as a property of the test item rather than the individual, potentially misrepresenting the cognitive processes underlying the examinee's behavior. This study evaluates a novel alternative, the Two-Parameter Logistic Extension (2PLE) model, which re-conceptualizes guessing as a function of a person's ability rather than as an item-specific constant. Using Monte Carlo simulation and empirical data from the PIRLS 2021 reading comprehension assessment, we compared the 3PL and 2PLE models on the recovery of latent ability, predictive fit (Leave-One-Out Information Criterion [LOOIC]), and theoretical alignment with test-taking behavior. The simulation results demonstrated that although both models performed similarly in terms of root-mean-squared error (RMSE) for ability estimates, the 2PLE model consistently achieved superior LOOIC values across conditions, particularly with longer tests and larger sample sizes. In an empirical analysis involving the reading achievement of 131 fourth-grade students from Saudi Arabia, model comparison again favored 2PLE, with a statistically significant LOOIC difference (ΔLOOIC = 0.482, z = 2.54). Importantly, person-level guessing estimates derived from the 2PLE model were significantly associated with established person-fit statistics (C*, U3), supporting their criterion validity. These findings suggest that the 2PLE model provides a more cognitively plausible and statistically robust representation of examinee behavior by embedding an ability-dependent guessing function.