Structure‐Regularized Composite Scoring for Calibrated MIRT Item Banks

Structure‐Regularized Composite Scoring (SRCS) was developed as a person‐scoring extension for calibrated multidimensional item response theory (MIRT) item banks. Given a Q‐matrix, reference distribution, and operational mastery cuts, SRCS combines the calibrated MIRT measurement score with a soft structural score while leaving the item response functions fixed. SRCS was compared with MIRT post hoc classification, MIRT with re‐estimated DINA profile scoring (MIRT‐reDINA), and Sequential ICC‐Induced Diagnostic Scoring (SIDS) across 6 simulation conditions and an empirical illustration using the ECPE grammar data. The first three procedures retained identical conventional MIRT proficiency estimates but differed in their profile‐scoring distributions; SRCS alone modified continuous person scoring. In simulation, SRCS incurred small losses in proficiency recovery, classification accuracy, probability calibration, and specificity, while increasing sensitivity and slightly reducing posterior‐weighted measurement‐structural discrepancy. The soft structural representation was consistently closer to the calibrated MIRT response surface than the hard representation. Empirically, SRCS closely preserved MIRT scores and operational profiles and moderately concentrated profile probabilities, but it did not reduce measurement‐structural discrepancy. Overall, SRCS provides a controlled scoring‐stage mechanism for incorporating attribute‐based structure while preserving a calibrated MIRT measurement system.