Online Program

Survey Process Control with Significance Editing: Foundations, Perspectives, and Plans for Development
Joseph Kosler, USDA NASS Research and Development 
*Joseph Kosler, USDA NASS Research and Development 

Keywords: significance edit, selective edit, process control, automated imputation

There has been a change in business model at the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) precipitated in part by the global economic crisis of 2008. NASS has created a research prototype for significance editing which is being developed into a full-scale production system. The NASS change in business model included a sweeping change of information technology systems used for processing establishment reports. These circumstances provided a foundation for exploring Fellegi-Holt methodology and designing a fully automated system for significance editing. Incorporation of the significance editing system is contingent upon performance to be assessed through a series of post-production tests begun September 2012 with the quarterly Hogs and Pigs Survey. If successful, the new system for significance editing, called SignEdit, would combine with existing NASS production systems to form an automated process support for participating surveys. Cost savings attributable to the SignEdit System could not be estimated at this time. The system is expected to improve consistency of editing and imputation across establishment reports. Also, the system is expected to alert agricultural statisticians of any imputed value that might have an impact of practical significance on a state-level indication. The focus on manual review for high-impact reports and automation for low-impact reports is expected to maintain the quality and caliber of NASS estimates while providing a substantial reduction in the time needed for processing. The recent changes to NASS production systems enable process control for surveys in the sense that it has become possible to automate the capture of process data (i.e. paradata) as well as the reaction to process data (e.g. adaptive design) for the purposes of controlling the grade (i.e. caliber), quality (e.g. compliance with publication standards), and financial cost of survey estimates. The term survey process control refers to the combined use of methods from survey production, survey management, industrial quality control, and statistical science to enact process control for sample surveys.