Modeling person guessing as a random effect: a Bayesian approach of the two-parameter logistic model.

Introduction: Guessing behavior has been an enduring problem that undermines the validity and interpretability of scores from MC items. The present study implements a Bayesian random-effects extension of the 2PLE model which suggests that guessing is a latent individual trait rather than a single item parameter. Methods: We implemented a Monte Carlo simulation in a fully crossed design of sample sizes ( Results: In all conditions the estimates of discrimination were larger with the 2PLE than with the 3PL. Gains were especially marked for item difficulty and lower-asymptote estimation that had noticeable distortion under the incorrect 3PL model. Bayesian predictive fit indices (i.e., Leave-One-Out Information Criterion, LOOIC; Widely Applicable Information Criterion, WAIC) consistently supported the 2PLE model under all sample sizes and test lengths. In the proposed framework, the person-level random effect δ Discussion: Through reallocating guessing variance from items to persons the 2PLE random-effects model can better capture diversified response patterns, and obtain a better psychometric performance. Findings are consistent with the conceptualization of guessing as a substantive trait-based process and underscore the utility and necessity of using person-specific guessing models to optimize inferences from test scores.