RIDIR: The Sub-National Data Archive System for Social and Behavioral Data

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

This project will create the Sub-National Data Archive System for Social and Behavioral Data. Research on political, social, and economic behavior increasingly depends on local, sub-national data, and on analysts' ability to combine data from multiple sources. Data may differ in geographic and time units and in measurement approaches and may have a variety of biases. Different data choices by researchers can lead to different conclusions and thus, across studies, can reduce the reliability, transparency, and replicability of empirical research. The Sub-National Data Archive System for Social and Behavioral Data will offer social scientists, policymakers, and the general public a suite of tools (1) to integrate different kinds of geographic data, (2) to account for differences in measurement across primary sources, and (3) to help assess the generalizability and robustness of research results across data sources. By making sub-national data more accessible, the project will help researchers inside and outside of academia test hypotheses, generate forecasts, evaluate the effectiveness of past and existing programs, and inform the allocation of scarce resources across competing initiatives. The overall goal is to improve understanding of how societies develop, prosper, and change, by enabling researchers to have easier access and effective use of highly-granular data from local areas. The Sub-National Data Archive System for Social and Behavioral Data addresses three persistent problems in social science research: mismatch between theoretical units of analysis and the scale of available data; the availability of multiple data sources for the same variables, which may be conflicting, overlapping or complementary; and a lack of generalizability of empirical results across alternative measurement strategies, or when using data from different sources and cases. This project will relieve these bottlenecks by producing a platform to develop highly-customizable integrated data with low technical barriers, enabling users to standardize sub-national units of analysis across countries, employ built-in methods where multiple data sources exist, and readily evaluate the robustness and generalizability of their results to alternative data and measurement choices (including shifting regional contexts). Core features of the platform will include: (1) a user-friendly web interface, where researchers can easily construct their own sub-national datasets from a large collection of archived data, (2) an open-source software package, which enables users to apply these tools to their own data, and produce a more customizable output based on individual specifications and needs, and (3) for users who wish to contribute original data to the platform, an archiving tool and data review process to ensure technical compliance and quality control. The program will build on previous data integration projects by providing a platform that covers almost every stage in the data life cycle. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.