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Abstract:
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It is difficult to design and conduct a survey because prior information on response rates and the like is likely generated from a different random process than the target one governing the surveys to be designed; and survey process, such as text classification, may vary from one human or machine to another. The impact of each error-prone sets of information on the properties of the estimator can be significant. We are concerned with reducing the side effect of both error-prone prior information and processed information on the quality of the estimator of the parameter of interest during survey data collection period. Nowadays, computer-assisted survey methods provide an instant variety of observations on the survey process and on the target random process governing the survey under consideration. These paradata, data, and quality measures enable the survey producer to make decisions regarding the need for methodology-process revision during survey data collection period, which involves consideration of both a model that represents how the target information relates to the error-prone information and the design that describes how the observations are obtained. We think of the error-prone and target information is a random process that has a joint distribution with some probability function. Then, at each phase of survey data collection, after receiving the information that the target random process has taken specific values, we update the joint probability distribution to revise the design specification in the course of the survey data collection period. Also, the coefficient of reliability for a survey as a whole set of processes as well as for a single process is further discussed.
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