Drowning in Data: Industry and Academic Approaches to Mixed Methods in “Holistic” Big Data Studies


In the past five years, as “big data” research increasingly has been adopted and adapted in the social sciences, the question of multimodal analysis pays a larger role in approaches and perspectives of research methodology. The buzzword "big data" has provoked critiques by a number of social scientists (eg., boyd & Crawford 2011; Bruns & Burgess 2012; Burrell 2012; Baym 2013; Lazer, et al. 2014; Tufekci 2014) on the theories, methodologies, and analysis of large data sources, and yet a growing number of scholars are experimenting with new ways to think about applying traditional and established methods to a newer domain and scale of data. Past panels (e.g., ICA 2013’s “Downsizing Data: Analyzing Social Digital Traces” and ICA 2014’s “Data-Driven Data Research Using Data and Databases: A Practical Critique of Methods and Approaches in ‘Big Data’ Studies”) have examined the practice of large-scale data analysis in social media research. This panel extends those discussions to look at the complications of mixed-methods research in big data studies, specifically in cases when “holistic,” population-level data is available.