Statistical aspects of optimal treatment assignment

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The issues of treatment assignment is ordinarily dealt with within the framework of testing aptitude treatment interaction (ATI) hypothesis. ATI research mostly uses linear regression techniques, and an ATI exists when the aptitude treatment (AT) regression lines cross each other within the relevant interval of the aptitude variable. Consistent with this approach is the use of the points of interaction of AT regression lines as treatment-assignment rule. The replacement of such rules by monotone, nonrandomized (Bayes) rules is proposed. Both continuous and dichotomous criteria for treatment success are considered. An example of the latter is evaluated using a mastery test. Solutions are given based on linear, normal ogive, and threshold utility functions. Some modifications of these functions are discussed which are believed to be more realistic in the context of individualized instruction, but for which no optimal monotone assignment rules are available yet.
Original languageUndefined
Place of PublicationEnschede, the Netherlands
PublisherUniversity of Twente
Publication statusPublished - Jun 1980

Publication series

NameTwente Educational Memorandum
PublisherUniversity of Twente, Faculty of Educational Science and Technology


  • Elementary Secondary Education
  • Research Design
  • Predictor Variables
  • Attitude Treatment Interaction
  • Individualized Instruction
  • Hypothesis Testing
  • IR-103614
  • Mastery Tests
  • Bayesian Statistics
  • Decision Making
  • Regression (Statistics)

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