ERIC Number: ED389757
Record Type: RIE
Publication Date: 1994-Nov
Reference Count: N/A
A Compensatory Approach to Optimal Selection with Mastery Scores. Research Report 94-2.
van der Linden, Wim J.; Vos, Hans J.
This paper presents some Bayesian theories of simultaneous optimization of decision rules for test-based decisions. Simultaneous decision making arises when an institution has to make a series of selection, placement, or mastery decisions with respect to subjects from a population. An obvious example is the use of individualized instruction in education. Compared with separate optimization, a simultaneous approach has two advantages. First, test scores used in previous decisions can be used as "prior" data in later decisions, and the efficiency of the decisions can be increased. Second, more realistic utility structures can be obtained defining utility functions for earlier decisions on later criteria. An important distinction 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. Conditions for monotonicity of optimal weak and strong rules are presented. Also, it is shown that under mild conditions on the test score distributions and utility functions, weak rules are always compensatory by nature. To illustrate this approach, a common decision problem in education and psychology, consisting of a selection decision for treatment followed by a mastery decision, is analyzed. (Contains 1 figure, 2 tables, and 23 references.) (Author)
Descriptors: Bayesian Statistics, Decision Making, Foreign Countries, Scores, Scoring, Selection, Test Items
Bibliotheek, Faculty of Educational Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.
Publication Type: Reports - Evaluative
Education Level: N/A
Authoring Institution: Twente Univ., Enschede (Netherlands). Faculty of Educational Science and Technology.
Identifiers: Compensatory Models; Decision Rules; Mastery Model; Simultaneous Processing; Utility Functions