Compensatory Rules for Optimal Classification with Mastery Scores
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.
KeywordsBayesian Decision Theory Classification Decisions Mastery Testing Simultaneous Optimization Compensatory Rules
Unable to display preview. Download preview PDF.
- Lindgren, B.W. (1976). Statistical Theory, Macmillan, New York.Google Scholar
- Lord, F.M. & Novick, M.R. (1968). Statistical Theories of Mental Test Scores, Addison-Wesley, Reading, Mass.Google Scholar
- Luce, R.D. & Raiffa, H. (1957). Games and Decisions, Wiley, New York.Google Scholar
- Tatsuoka, M.M. (1971). Multivariate Analysis: Techniques for Educational and Psychological Research, Wiley, New York.Google Scholar