Hip psychometrics

When data are abundant relative to the number of questions asked of them, answers can be formulated using little more that those data. But when data grow more sparse, so too does Out tendency to lean oil strong models to help LIS draw inferences. In this research we show how a strong item response model embedded within a fully Bayesian framework allows LIS to answer two important questions about the reliability and consistency of the clinical diagnosis of hip fractures from very limited data. We also show how the model automatically ad Lists diagnoses for biases among the surgeons judging the radiographs. This research illustrates how it Bayesian approach expands the range of problems on which item response models call profitably be used. Copyright (C) 2009 John Wiley & Soils. Ltd.