Model misspecification in multidimensional IRT: Consequences of ignoring the guessing parameter
Abstract
This study examined the effect of ignoring the guessing parameter in the context of multidimensional Item Response Theory on item and ability parameter estimation, using Monte Carlo simulation. Data generated from a two-dimensional 3PL model (N = 2000; 40 items) were estimated under each condition using both the correctly specified model (2D-3PL) and the misspecified model (2D-2PL) via Bayesian methods (Stan/MCMC). The inter-dimensional correlation was manipulated at ρ = .10, .50, and .80. Parameter recovery was evaluated using bias, root-mean-square error (RMSE), and estimated-true correlations. Results indicated that the 2D-2PL model systematically underestimated the discrimination parameter across all conditions (16–21% negative bias), and that this distortion intensified as the correlation increased. The difficulty parameter also showed downward bias (bias ≈ −0.24 to −0.27), leading items to appear easier than they actually were. In terms of error magnitude, the 2PL/3PL RMSE ratio for discrimination ranged from 2.32 to 2.47 and increased rather than decreased under high-correlation conditions. Ability estimates, by contrast, were found to be relatively robust to model misspecification, with the 2PL–3PL RMSE difference remaining at approximately 0.5%–1.0%. The results show that 3PL should be preferred for multidimensional multiple-choice tests with N ≥ 2000, and that using 2PL for computational convenience may lead to substantial measurement errors, particularly in applications such as item calibration, item selection, and test equating.
Keywords
References
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Details
Primary Language
English
Subjects
Measurement Theories and Applications in Education and Psychology
Journal Section
Research Article
Authors
Samet Eker
*
0000-0001-8266-2953
Türkiye
Melike Kara Şahin
0009-0008-3703-3101
Türkiye
Hülya Kelecioğlu
0000-0002-0741-9934
Türkiye
Early Pub Date
October 28, 2026
Publication Date
-
Submission Date
April 8, 2026
Acceptance Date
October 11, 2026
Published in Issue
Year 2026 Number: Advanced Online Publication