Coding is Not a Dirty Word: Theory-Driven Data Analysis Using NVivo

Coding is Not a Dirty Word: Theory-Driven Data Analysis Using NVivo

Kakali Bhattacharya (Kansas State University, USA)
Copyright: © 2015 |Pages: 30
DOI: 10.4018/978-1-4666-6493-7.ch001


Current discourses in qualitative research, especially those situated in postmodernism, represent coding and the technology that assists with coding as reductive, lacking complexity, and detached from theory. In this chapter, the author presents a counter-narrative to this dominant discourse in qualitative research. The author argues that coding is not necessarily devoid of theory, nor does the use of software for data management and analysis automatically render scholarship theoretically lightweight or barren. A lack of deep analytical insight is a consequence not of software but of epistemology. Using examples informed by interpretive and critical approaches, the author demonstrates how NVivo can provide an effective tool for data management and analysis. The author also highlights ideas for critical and deconstructive approaches in qualitative inquiry while using NVivo. By troubling the positivist discourse of coding, the author seeks to create dialogic spaces that integrate theory with technology-driven data management and analysis, while maintaining the depth and rigor of qualitative research.
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Since the National Research Council (NRC) released its report on scientific research (Feuer, Towne, & Shavelson, 2002; Shavelson & Towne, 2002) outlining what counts as evidence, especially fundable scientific evidence, qualitative researchers have been extremely concerned about how this discourse limits the practice and theorization of qualitative inquiry (Cannella & Lincoln, 2004; Popkewitz, 2004; St. Pierre, 2002). To the disappointment of many qualitative scholars, the American Educational Research Association (AERA) used the NRC report to construct a definition of scientific research (AERA, 2009). The ensuing discussion addressed questions of accountability, what good research in qualitative inquiry might look like, and how one might weigh the merits of evidence presented in qualitative inquiry (Freeman, deMarrais, Preissle, Roulston, & St. Pierre, 2007; Slavin, 2007).

Qualitative researchers argued that discourses of accountability and expectations of evidence driven by positivist paradigms were not only limiting but also dangerous and uninformed, marginalizing multiple epistemological ways in which knowledge could be constructed (Lather, 2004). On the heels of these discussions, a healthy amount of skepticism arose whenever the call for accountability was introduced, or when scholars claimed that using qualitative data management software allowed them to preserve some accountability, conduct systematic reviews, and document a trail of their data management and analysis (MacLure, 2007).

Qualitative researchers presented multiple panels at various conferences, such as the International Congress of Qualitative Inquiry and AERA, to invite the authors of the NRC Report to engage in dialogue and find common ground across differences. However, while these discussions were productive, the report remained the same; thus, the fundability of research continued to privilege experimental or other quantitative studies. This prompted a move in qualitative research to emphasize theory-driven discussions and to infiltrate several discursive spaces, such as top tier qualitative research journals, national and international conferences with strong qualitative presence to demonstrate the importance of theory to any other aspect of data management and analysis (St. Pierre, 2013; Pierre & Jackson, In Press).

In these discussions, criticism was aimed at various aspects of data management and analysis, particularly coding and those who use software to manage and analyze data. Theory-based data analysis continued to claim the academic high ground (St. Pierre, 2013; 2011), and coding became a dirty word. People chuckled at conferences at those who still utilized coding, and those who used software were viewed by those who did not as theoretically light.

In this chapter, I problematize this narrow and limited conceptualization of coding and the role of software, and address the accusations of a theoretical void aimed at those who use coding and software. Specifically, I argue that software itself cannot create a theoretical void, because software cannot conduct data analysis without a human operator who drives the thinking, writing, and in-depth inquiry. Similarly, I deconstruct the notion that coding in and of itself is positivist, limiting, and critique such essentialist understandings of coding.

Using a broad theoretical approach, I demonstrate how NVivo can allow for powerful data management and analysis. Although a more extended discussion is beyond the scope of this chapter, I address briefly the ways in which theory-driven data analysis can be integrated with NVivo in conducting critical and deconstructive work. Moreover, I highlight the depth of inquiry and analysis that can be achieved using software with efficient processes such as clustering, connecting, interrogating data, and visualizing results while using various data sources such as YouTube videos, Twitter feeds, Facebook conversations, and Web-based articles. Please note that this chapter is not about advocating for the use of NVivo as the only tool for data management and analysis. There are many tools in the market that can assist with qualitative data management and analysis. However, this chapter demonstrates how using NVivo contributed towards a theory-driven research experience for me.

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