Challenges for Digital Qualitative Research

Thoughts on Digital Qualitative Research – Part 3

Although the digital space offers many opportunities in terms of data collection, analysis and new data itself, part 3 of the series ” Thoughts on Digital Qualitative Research”  addresses the challenges going hand in hand with digital research. Although each method and each research project may face other or additional questions and difficulties, the challenges presented here are valid for all scholars who aim to conduct digital qualitative inquiry.

The Nature and Quality of Data

The nature and quality of data are crucial for coherent and transparent interpretation. In early research, digital data often required grounding in the offline world to be seen as valid (Rogers, 2013). Although today not all data require grounding outside the digital sphere (Rogers, 2013) as they often describe natively digital phenomena, the methodological context is essential to understand the nature and meaning of the data analysed (Dicks, 2012).  Quinton and Reynolds (2018) argue that researches have to define their stance but can freely classify their approaches in terms of methodology.

Data for qualitative research can occur in very different forms, e.g. text, images, videos, graphics, databases, sound or coding structures (Fielding & Lee, 2008), as newly emerged platforms, networks and digital spaces fulfil the technological requirements needed. The challenge for qualitative researches is how to interpret these different forms of data and what method to choose to grasp the context and connection between various elements.

Further, technology itself can be a threat to the quality of data. Technological failures can affect the process of data collection or the nature of data itself (Hewson, 2014), as the digital is characterized by its ever-changing landscape and functions (Quinton & Reynolds, 2018).
The dynamic nature of the digital research context also has an impact on the longevity of data (Quinton & Reynolds, 2018). The results displayed in research have to be seen as momentarily snapshots in terms of time, place and context (Markham & Gammelby, 2018).

Additionally, researchers have to be aware of data as fragments and partial representation of social processes. Referring to Latour, Markham (2018) suggests to examine data as a construct of an object or reality, but never as a complete representation of the concept. Not every aspect of a concept is digitalisable. Vadén (2004) emphasizes that the concept needs to be abstractable, e.g. there is no digital version of a hug because there is no abstract version of a hug. The question for researchers is then: What kind of information do we need to understand the full phenomenon?

Ethics in Digital Research

Ethics in digital research pose a great challenge for researchers as there are often no official guidelines or no consent about what is ethical (Quinton & Reynolds, 2018; Tiidenberg, 2018). Additionally, control instances in companies, universities and ethical commissions are not fully aware of the implication and consequences of the ever-changing and complex field of digital data (boyd & Crawford, 2012). Fielding and Lee (2008) define data protection and ethical conventions as significant hurdles for qualitative inquiry. All data somehow involve a human subject at some part (Tiidenberg, 2018). Therefore, the protection and anonymity of these subjects are essential when working with digital data. How can researches design their research ethically in the digital space?

The often blurry distinction of public and private online data entails more difficulties than in the offline domain (boyd & Crawford, 2012; Quinton & Reynolds, 2018; Tiidenberg, 2018). Is the data the researcher wants to collect public, semi-public or private? Who owns the data? The most obvious way to secure ethically correct data collection, whether in the public or private domain, would be to gain informed consent of all participant, but the fluid boundaries and anonymity in the digital often make it hard to do so (Hewson, 2014; Tiidenberg, 2018). The research design and decisions should therefore always be guided by doing no harm to the research, as Tiidenberg (2018) and Quinton and Reynolds (2018) emphasize.

The Availability and Display of Data

As discussed in part 2 of this series, large data sets are often in the hands of big companies and the access for researchers can be restricted. This limited access creates a new digital divide because status, prestige and money influence the amount and quality of data scientists can collect (boyd & Crawford, 2012). Nevertheless, the digital does not only pose restrictions for data collection. Some advantages for qualitative research is the huge amount of data, which can be accessed much more cost- and time-saving, in a broad geographical and social context, and in hard-to-reach populations (Hewson, 2014). However, the responsibilities of researchers do not end with the data access and collection. Rogers (2013) shows an example for non-responsible display of data with the AOL scandal. Although researchers anonymized the data published, the combination of the large-scale data sets made it possible for journalists to track down individuals, only using the displayed, publicly available data (Barbaro & Zeller, 2006).

The digital environment offers more challenges for the research like a shift toward technological expertise (boyd & Crawford, 2012; Quinton & Reynolds, 2018), the development of technological tools by social science itself (Fielding & Lee, 2008) and many more.

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!

References

• Barbaro, M., & Zeller, T. (2006, August 9). A Face Is Exposed for AOL Searcher No. 4417749. The New York Times, https://www.nytimes.com/2006/08/09/technology/09aol.html.
• Boyd, d., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15, 662–679. https://doi.org/10.1080/1369118X.2012.678878
• Dicks, B. (Ed.). (2012). Digital qualitative research methods. Los Angeles: SAGE.
• Fielding, N., & Lee, R. M. (2008). Qualitative e-Social Science/Cyber-Research. In N. Fielding, R. M. Lee, & G. Blank (Eds.), The SAGE handbook of online research methods (pp. 491–506). Los Angeles, Calif.: SAGE.
• Hewson, C. (2014). Qualitative Approaches in Internet-Mediated Research. In P. Leavy (Ed.), Oxford library of psychology. The Oxford handbook of qualitative research (pp. 423–454). Oxford, New York: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199811755.013.020
• 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. https://doi.org/10.4135/9781526416070.n33
• Markham, A. N., & Gammelby, A. K. (2018). Moving Through Digital Flows: An Epistemological and Practical Approach. In U. Flick (Ed.), The Sage handbook of qualitative data collection (pp. 451–465). London, Thousand Oaks, California: SAGE Publications Ltd. https://doi.org/10.4135/9781526416070.n29
• 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.
• 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. https://doi.org/10.4135/9781526416070.n30
• Vadén, T. (2004). Digital Nominalism. Notes on the Ethics of Information Society in View of the Ontology of the Digital. Ethics and Information Technology, 6, 223–231. https://doi.org/10.1007/s10676-005-0350-7

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