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Conditional reliability of admissions interview ratings: extreme ratings are the most informative
Author(s) -
Brent Stansfield R,
Kreiter Clarence D
Publication year - 2007
Publication title -
medical education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 138
eISSN - 1365-2923
pISSN - 0308-0110
DOI - 10.1111/j.1365-2929.2006.02634.x
Subject(s) - reliability (semiconductor) , weighting , psychology , predictive validity , scale (ratio) , rating scale , clinical psychology , validity , set (abstract data type) , psychometrics , statistics , developmental psychology , computer science , medicine , mathematics , power (physics) , physics , quantum mechanics , radiology , programming language
Context Admissions interviews are unreliable and have poor predictive validity, yet are the sole measures of non‐cognitive skills used by most medical school admissions departments. The low reliability may be due in part to variation in conditional reliability across the rating scale. Objectives To describe an empirically derived estimate of conditional reliability and use it to improve the predictive validity of interview ratings. Methods A set of medical school interview ratings was compared to a Monte Carlo simulated set to estimate conditional reliability controlling for range restriction, response scale bias and other artefacts. This estimate was used as a weighting function to improve the predictive validity of a second set of interview ratings for predicting non‐cognitive measures (USMLE Step II residuals from Step I scores). Results Compared with the simulated set, both observed sets showed more reliability at low and high rating levels than at moderate levels. Raw interview scores did not predict USMLE Step II scores after controlling for Step I performance (additional r 2 = 0.001, not significant). Weighting interview ratings by estimated conditional reliability improved predictive validity (additional r 2 = 0.121, P < 0.01). Conclusions Conditional reliability is important for understanding the psychometric properties of subjective rating scales. Weighting these measures during the admissions process would improve admissions decisions.