This EA focuses on enhancing data collection methods and operations, building on 2020 Census innovations, and further improving data quality. It will research the best contact methods and data collection strategies, with an emphasis on improving data collection for specific population groups, particularly historically undercounted populations. Research conducted through this EA also aims to enhance our engagement and outreach to communities and historically undercounted populations. Several research projects focus on increasing automation in our data collection operations to reduce respondent burden and improve efficiency.
Note that this Enhancement Area comprises sub-EAs, each of which has designated research projects. These sub-EAs are: Self-Response, In-Field and In-Office Enumeration, Response Data Quality and Questionnaire Content, and the Post-Enumeration Survey (PES).
This EA focuses on group quarters (GQs), which are places where people live or stay in a group living arrangement, such as college or university student housing or a nursing home. While GQ residents represent a small proportion of the population, they are a unique population that must be handled with specialized methods and operations. This EA will build a good inventory of GQ facilities to ensure an accurate enumeration of the residents. It will also research ways to improve data collection methods for GQs to improve data quality and reduce the burden on GQ facilities and residents. Through these efforts, this EA also seeks to build partnerships with the GQ community and their advocates.
This EA is focused on integrating data collection and data processing, improving data quality in near real time, and pivoting away from the historical linear approach for processing census data toward a more transaction-based framework. By shifting to processing data in near real time we can minimize the need for a large-scale post-collection processing operation. Additionally, by integrating analytics and near real time monitoring of data, we can identify and address quality issues and make data-centric decisions to resolve anomalies as they surface. This research will streamline data processing activities and enhance progress reporting throughout the collection process.
This EA is focused on transforming and improving the support infrastructure for the 2030 Census. It focuses on topics such as reducing the physical footprint for infrastructure; re-envisioning the office structure and the use of automation for field operations and other respondent services; researching ways to streamline and automate recruiting, hiring, and payroll services to increase efficiency and facilitate large scale staffing for peak operations; and researching ways to improve external engagement and leverage partnership networks.
This EA focuses on gathering and assessing data throughout the decade to optimize the once-a-decade count. Building on the 2020 Census success of conducting the majority of address frame development across the decade, this EA will research ways to apply a similar strategy to person data collection, decreasing the amount of work necessary at the end of the decade. This research will identify and procure new administrative records data to increase coverage of all populations. This EA will also establish the ability to continuously develop and assess population estimates from administrative data throughout the decade, to identify the coverage and quality gaps.
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