Abstract
A model for simultaneous optimization of combinations of test-based decisions in education and psychology is proposed using Bayesian decision theory. To illustrate the approach, one classification decision with two treatments each followed by a mastery decision are combined into a decision network. An important decision is made between weak and strong decision rules. As opposed to strong rules, weak rules are allowed to be a function of prior test scores in the series. Conditions under which optimal rules take weak monotone forms are derived. Results from a well-known problem in The Netherlands of selecting optimal continuation schools on the basis of achievement test scores are presented.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Vos, H.J. (1998). Compensatory Rules for Optimal Classification with Mastery Scores. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_29
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DOI: https://doi.org/10.1007/978-3-642-72253-0_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64641-9
Online ISBN: 978-3-642-72253-0
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