Modeling person guessing as a random effect: a Bayesian approach of the two-parameter logistic model
Year: 2026
Type: article
Abstract: 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 ( N = 100–1,000) and test lengths (6–40 items), with 50 replications per condition. Item response data were simulated under the 2PLE model with heterogeneous guessing. 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 n reflects differences between individuals in guessing tendency and directly influences the lower asymptote of an item response function. 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.
Source: Frontiers in Psychology
Authors: Georgios Sideridis, Mohammed Alghamdi
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funder: King Saud University
OpenAlex ID: W7129075991