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Title: Bayesian decision theoretic approach to experimental design with application to usability experiments
Author: Valks, Pamela.
Awarding Body: University of Sunderland
Current Institution: University of Sunderland
Date of Award: 2004
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This thesis looks at the practicality of applying a Bayesian Decision Theoretic approach to the design of HCI usability experiments. It looks at the particular issues involved in following the Bayesian experimental design framework of developing a stochastic model, eliciting priors and utility functions and choosing the option with the maximum expected utility. HeI usability testing may involve user and analyst experimentation and various courses of action may be employed using either one or both types of experiment. The thesis shows that HeI usability experiments can be represented diagrammatically by a decision tree so that courses of action and consequences can be shown in sequential order and consequently that decision theory can be applied to experimental design. A structure of three decisions is proposed for the user experiment where the design of the experiment is a decision within the larger decision of whether to launch or rewrite. A structure of a single decision is proposed for the analyst experiment. The thesis shows that stochastic models can be developed which give solutions using realistic priors and utility functions. For the user experiment the problem of a joint prior distribution for two dependent binomial parameters is overcome by developing a method using copula functions. For the analyst experiment a two factor capturerecapture model for the identification of potential HeI problems is developed. Two ways of representing the utility function, either in terms of monetary rewards only or as a bivariate utility function, are investigated. The thesis shows that for realistic utility functions both ways require numerical methods to calculate the expected utilities, but a bivariate utility function has computational and elicitation advantages. Hel usability experiments pose many questions including the following. Should a user experiment be performed is it better to launch or rewrite without performing an experiment? If a user experiment is performed what is the optimal number of subjects? After a user experiment is it better to launch or rewrite? What is the optimal number of analysts to take part in an experiment? How many problems are remaining in the system after an analyst experiment? This thesis shows how models currently described in the HCI literature can be generalised using a Bayesian Decision Theoretic approach and used to give answers to these questions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available