Abstract:
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The field of data science has served to rapidly expand the knowledge base and decision-making ability through the combination of seemly disparate and diverse sources of information and content, which include survey and administrative data, social, financial and economic micro-data, and content from mobile devices, the internet and social media. Other attributes of data science include data visualization; social media analytics; predictive, mathematical and simulation modeling; use of Bayesian methods, machine learning; geospatial analytics and Big Data technologies. Through these synergies, greater accuracy and efficiencies can realized in the implementation of optimization analytics, and in the development of enhanced data resources that can provide the informatics to guide decisions and target interventions. This discussion focuses on the capacity of data science to inform the design of surveys, their operations and associated strategies to reduce survey errors and enhance data quality. Potential contributions to sample frame development, sample design specifications, oversampling strategies and analytic file creation are also addressed.
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