Side Effect Reduction of Prior and Processed Information on Survey Design (Parts 1, 2 and 3)

• Activity Number: 53 - Applications of Data Linkage and Machine Learning Techniques
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Survey Research Methods Section
• Author(s): Abdellatif Demnati*
• Companies: Independent Researcher
• Keywords: Multiple sources of information; Optimal resources allocation; Responsive design; Two-phase sampling; Unit misclassification; Wisdom design

Abstract

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. We are concerned with reducing the side effect of prior information and processed information on the precision of the estimates during the data collection period. Nowadays, computer-assisted survey methods provide an instant variety of observations on the survey process and on the target random process. These paradata, data, and quality measures enable the survey producer to make decisions regarding the need for methodology-process revision during data collection period. We think of the error-prone and target information as a random process that has a joint distribution with some probability function. Then at each phase of 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 data collection period.