Big Qual: Defining and Debating Qualitative Inquiry for Large Data Sets

Abstract

Big qualitative data (Big Qual), or research involving large qualitative data sets, has introduced many newly evolving conventions that have begun to change the fundamental nature of some qualitative research. In this methodological essay, we first distinguish big data from big qual. We define big qual as data sets containing either primary or secondary qualitative data from at least 100 participants analyzed by teams of researchers, often funded by a government agency or private foundation, conducted either as a stand-alone project or in conjunction with a large quantitative study. We then present a broad debate about the extent to which big qual may be transforming some forms of qualitative inquiry. We present three questions, which examine the extent to which large qualitative data sets offer both constraints and opportunities for innovation related to funded research, sampling strategies, team-based analysis, and computer-assisted qualitative data analysis software (CAQDAS). The debate is framed by four related trends to which we attribute the rise of big qual: the rise of big quantitative data, the growing legitimacy of qualitative and mixed methods work in the research community, technological advances in CAQDAS, and the willingness of government and private foundations to fund large qualitative projects.