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.

Arts engagement supports social connectedness in adulthood: findings from the HEartS Survey

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“…In keeping with newly emerging conventions for large qualitative datasets (Brower et al, 2019), we present our findings in two formats. First, we present tables summarizing the subthemes across all data that demonstrate the variety of forms of intergenerational learning present in our data.…”

Clinical Data Mining With the Listening Guide: An Approach to Narrative Big Qual

“…The case notes are also an instance of Big Qual data. Big Qual refers to the use of large data sets involving either primary or secondary data, typically analyzed by teams of researchers as part of funded mixed methods research projects (Brower et al, 2019). Data for Big Qual projects may be newly collected from the field as in the study described by Brower and colleagues (2019), downloaded from social media sources like Facebook and Twitter, culled from qualitative responses to open-ended survey questions, or repurposed from digital archives or various kinds of administrative databases for reuse and secondary analysis (Davidson et al, 2019).…”

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“…This paper discusses an emergent qualitative method developed by our research team in the domain of big qualitative data or Big Qual (Brower et al, 2019), which combines Lyn Mikel Brown andCarol Gilligan's (1991, 1992) Listening Guide with Irwin Epstein's (2009) clinical data mining (CDM) methodology. Emergent qualitative methods can help researchers answer novel questions, bridge disciplinary boundaries, and leverage new types of data for knowledge production (Hesse-Biber & Leavy, 2013).…”