Sheridan Grant, Marina Meilă, Elena Erosheva, Carole Lee
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引用次数: 1
Abstract
We propose a new metric for evaluating the informativeness of a set of ratings from a single rater on a given scale. Such evaluations are of interest when raters rate numerous comparable items on the same scale, as occurs in hiring, college admissions, and peer review. Our exposition takes the context of peer review, which involves univariate and multivariate cardinal ratings. We draw on this context to motivate an information-theoretic measure of the refinement of a set of ratings – entropic refinement – as well as two secondary measures. A mathematical analysis of the three measures reveals that only the first, which captures the information content of the ratings, possesses properties appropriate to a refinement metric. Finally, we analyse refinement in real-world grant-review data, finding evidence that overall merit scores are more refined than criterion scores.
期刊介绍:
The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including:
• mathematical psychology
• statistics
• psychometrics
• decision making
• psychophysics
• classification
• relevant areas of mathematics, computing and computer software
These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.