A compensatory approach to optimal selection with mastery scores

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Abstract

A Bayesian approach for simultaneous optimization of test-based decisions is presented using the example of a selection decision for a treatment followed by a mastery decision. A distinction is made between weak and strong rules where, as opposed to strong rules, weak rules use prior test scores as collateral data. Conditions for monotonicity of optimal weak and strong rules are presented. It is shown that under mild conditions on the test score distributions and utility functions, weak rules are always compensatory by nature.
Original languageEnglish
Pages (from-to)155-172
JournalPsychometrika
Volume61
Issue number1
DOIs
Publication statusPublished - 1996

Keywords

  • Decision theory
  • Mastery testing
  • Monotone Bayes rules
  • Selection decisions

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