A Bayesian analysis of survey design parameters - ARCHIVED

In the design of surveys a number of parameters like contact propensities, participation propensities and costs per sample unit play a decisive role. In on-going surveys, these survey design parameters are usually estimated from previous experience and updated gradually with new experience. In new surveys, these parameters are estimated from expert opinion and experience with similar surveys. Although survey institutes have a fair expertise and experience, the postulation, estimation and updating of survey design parameters is rarely done in a systematic way. This paper presents a Bayesian framework to include and update prior knowledge and expert opinion about the parameters. This framework is set in the context of adaptive survey designs in which different population units may receive different treatment given quality and cost objectives. For this type of survey, the accuracy of design parameters becomes even more crucial to effective design decisions. The framework allows for a Bayesian analysis of the performance of a survey during data collection and in between waves of a survey. We demonstrate the Bayesian analysis using a realistic simulation study.