Simultaneous Optimization of Decisions Using a Linear Utility Function

The purpose of this article is to simultaneously optimize decision rules for combinations of elementary decisions. With this approach, rules are found that make more efficient use of the data than could be achieved by optimizing these decisions separately. The framework for the approach is derived f...

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Veröffentlicht in:Journal of Educational Statistics. - American Educational Research Association and American Statistical Association, 1976. - 15(1990), 4, Seite 309-340
1. Verfasser: Vos, Hans J. (VerfasserIn)
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 1990
Zugriff auf das übergeordnete Werk:Journal of Educational Statistics
Schlagworte:Decision theory Culture-fair selection Linear utility Economics Education Information science Mathematics Behavioral sciences
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520 |a The purpose of this article is to simultaneously optimize decision rules for combinations of elementary decisions. With this approach, rules are found that make more efficient use of the data than could be achieved by optimizing these decisions separately. The framework for the approach is derived from Bayesian decision theory. To illustrate the approach, two elementary decisions (selection and mastery decisions) are combined into a simple decision network. A linear utility structure is assumed. Decision rules are derived both for quota-free and quota-restricted selection-mastery decisions in case of several subpopulations. An empirical example of instructional decision making in an individual study system concludes the article. 
540 |a Copyright 1990 The American Educational Research Association and the American Statistical Association 
650 4 |a Decision theory 
650 4 |a Culture-fair selection 
650 4 |a Linear utility 
650 4 |a Economics  |x Microeconomics  |x Economic utility  |x Utility functions 
650 4 |a Economics  |x Microeconomics  |x Economic utility  |x Expected utility 
650 4 |a Education  |x Formal education  |x Pedagogy  |x Educational methods  |x Educational testing  |x Educational tests  |x Achievement tests  |x Mastery tests 
650 4 |a Information science  |x Information analysis  |x Data analysis  |x Regression analysis  |x Linear regression 
650 4 |a Mathematics  |x Pure mathematics  |x Algebra  |x Coefficients 
650 4 |a Behavioral sciences  |x Psychology  |x Cognitive psychology  |x Decision theory 
650 4 |a Education  |x Formal education  |x Pedagogy  |x Educational methods  |x Educational testing  |x Test scores 
650 4 |a Mathematics  |x Applied mathematics  |x Statistics  |x Applied statistics  |x Descriptive statistics  |x Statistical distributions  |x Distribution functions  |x Probability distributions 
650 4 |a Mathematics  |x Applied mathematics  |x Statistics  |x Applied statistics  |x Descriptive statistics  |x Statistical distributions  |x Distribution functions  |x Probability distributions  |x Gaussian distributions 
650 4 |a Mathematics  |x Mathematical values  |x Mathematical variables  |x Mathematical independent variables 
655 4 |a research-article 
773 0 8 |i Enthalten in  |t Journal of Educational Statistics  |d American Educational Research Association and American Statistical Association, 1976  |g 15(1990), 4, Seite 309-340  |w (DE-627)482303859  |w (DE-600)2181666-9  |x 03629791  |7 nnns 
773 1 8 |g volume:15  |g year:1990  |g number:4  |g pages:309-340 
856 4 0 |u https://www.jstor.org/stable/1165091  |3 Volltext 
856 4 0 |u https://doi.org/10.2307/1165091  |3 Volltext 
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952 |d 15  |j 1990  |e 4  |h 309-340