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Harnessing the power of data to support community‐based research
Author(s) -
Keller Sallie,
Shipp Stephanie,
Korkmaz Gizem,
Molfino Emily,
Goldstein Joshua,
Lancaster Vicki,
Pires Bianica,
Higdon David,
Chen Daniel,
Schroeder Aaron
Publication year - 2018
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1426
Subject(s) - repurposing , local government , citizen journalism , computer science , data science , population , government (linguistics) , statistic , process (computing) , public relations , political science , public administration , sociology , statistics , world wide web , engineering , linguistics , philosophy , demography , mathematics , operating system , waste management
Volumes of data are generated at every moment as we go through the paces of our daily lives. Many of these data flows are routinely captured through administrative records, social media, and surveys. Historically, agencies at different levels of government have been responsible for curating and reporting statistics about our social, economic, and health conditions associated with these data flows. Recently, the U.S. government has proposed the use of data derived from administrative records at the federal level to support social policy and program evaluation. Why not consider parallel activities at state and local levels? Harnessing local data sources and integrating them with state and federal sources will provide timelier and more geographically specific analyses to support local insights and policy development. Leveraging community‐based participatory research, researchers and civic leaders can work together to identify the questions and execute rigorous, yet flexible, processes for building local sustainable community learning cultures based on data‐driven discovery. In the process of conducting research with local civic leaders, we have observed that issues can be classified into 3 categories: locating and describing a population within a community; estimating a statistic and a measure of variability; and evaluating a program, policy, or procedure. Through a series of case studies, this paper demonstrates the unexpected value in liberating and repurposing local data. This article is categorized under: Applications of Computational Statistics > Organizations and Publications