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Abstract:
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This paper proposes a framework and guiding principles for conducting quantitative Community-Based Research with secondary datasets, an approach I term Empathetic Data Science Practice (EDSP). The wide availability of secondary data and increased accessibility of data science has depersonalized secondary data analysis and distanced statisticians from the people who are impacted by the results of the analysis. Especially when addressing questions of relevance to communities, this distance decreases the effectiveness of research based on secondary datasets. EDSP, in which statisticians collaborate with people who will be most impacted by their analyses and engage thoughtfully and respectfully with data, is a solution to this widening divide. The principles of EDSP are based on theories of quantitative analysis and Community-Based Research as well as case studies of successful collaborations between data scientists and communities in analysis of secondary data to inform policy goals and organizing strategies. I provide nuanced discussion on the benefits and limitations of EDSP, emphasizing how it brings together Community-Based Research and secondary data analysis in a mutually beneficial way.
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