Digital Qualitative Research in a Big Data World

Thoughts on Digital Qualitative Research – Part 2

Part 2 of the series examines the emerging tensions and questions for qualitative research in a Big Data world.

© Bethany Drouin – bsdrouin/pixabay

Boyd and Crawford (2012) define the shift to Big Data as a socio-technical phenomenon. The term Big Data describes the huge amount of data aggregated through interaction online and the ability to cross-reference these large data sets for new insights. Discussing the troubling concept of data, Markham (2018) and Rogers (2013) argue that datafication as ideological focus and the methodological turn towards digital research techniques quantifying social processes pose a temptation as well as a challenge for qualitative research at the same time.

The technological ability to collect data does replace the question if qualitative oriented scholars should collect such large amounts of data (Tiidenberg, 2018), from an ethical as well as from a practical point of view (Markham, 2012). Further, these large data sets do not fully represent social phenomena (Markham, 2018), but Big Data changes the way knowledge is created and research is done by changing the instruments, the focus and the process of research (boyd & Crawford, 2012). Markham (2018, p. 520) sees the turn to the quantification in data analysis, the subsumption of qualitative inquiry as an add-on for data-driven science and the pursuit of generalizability as an “ongoing risk, which the interpretative movement has long sought to combat.”  

Further, researches must be aware of the origin of these large data sets, which are often owned by large global companies like Google or Facebook (Quinton & Reynolds, 2018; Snee et al., 2016), hence boyd and Crawford (2012, p. 674) define the new digital divide between “Big Data rich” and “Big Data poor”. As access to big data is often restricted by high fees or protected by large companies. Small universities, individual researchers and research groups will face difficulties to access full data sets. As a consequence, these scholars will be forced to use smaller data sets and open data, which are easier accessible, but do not provide as much potential for generalizability as big data sets. Therefore, the need for qualitative social researchers to develop a framework and methods to justify and establish empirical research in the digital based on small data is all the more important

So how can qualitative researchers establish their importance in digital research with small data sets and qualitative approaches to digital data? How can they assess the data in digital research environments and what assumptions do they have to question and keep in mind?

Part 3, the last part of this series, will address the challenges researches face when conducting digital research.

For further ideas, input, critical questions or discussions to these thoughts about qualitative digital research, just leave me a comment and I’ll be happy to discuss with you!


  • Boyd, d., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15, 662–679.
  • Markham, A. N. (2012). The Internet as Research Context. In B. Dicks (Ed.), Digital qualitative research methods (pp. 375–403). Los Angeles: SAGE.
  • Markham, A. N. (2018). Troubling the Concept of Data in Qualitative Digital Research. In U. Flick (Ed.), The Sage handbook of qualitative data collection (pp. 511–523). London, Thousand Oaks, California: SAGE Publications Ltd.
  • Quinton, S., & Reynolds, N. (2018). Understanding research in the digital age. Sage Publications Ltd.
  • Rogers, R. (2013). Digital methods. Cambridge, Mass., London: The MIT Press.
  • Snee, H., Hine, C., Morey, Y., Roberts, S., & Watson, H. (Eds.). (2016). Digital Methods for Social Science. London: Palgrave Macmillan UK.
  • Tiidenberg, K. (2018). Ethics in Digital Research. In U. Flick (Ed.), The Sage handbook of qualitative data collection (pp. 466–479). London, Thousand Oaks, California: SAGE Publications Ltd.

The full text on digital qualitative methods was part of the course “Qualitative research II” at the University of Innsbruck.

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