Tools for Analyzing Qualitative Data: The History and Relevance of Qualitative Data Analysis Software

Abstract

IntroductionThis chapter focuses on tools for supporting the analysis of qualitative data, particularly on software designed for that purpose. The choice of the word "tools" rather than simply "software" in the title of this chapter re fl ects the role of technology in the context of complex intellectual work. "Tools" is a broad term, which could encompass the broad array of theoretical constructs that fall under the mantle of qualitative research, analytical practices used in conjunction with various theoretical approaches, as well as the wide range of supportive technologies that are increasingly used for this speci fi c kind of knowledge work; these dimensions of the term are interrelated, but not uni fi ed. In addition, a variety of technological tools can be used to achieve the same analytic goal, while very different theoretical approaches often involve the same analytical tasks. This complexity leads us to an important point: the most common question from novices regarding the use of software in qualitative research is "which program should I use?" when they would be better served by asking "what analytical tasks will AbstractThe most common question from novices regarding the use of software in qualitative research is "which program should I use?" when they would be better served by asking "what analytical tasks will I be engaged in, and what are the different ways I can leverage technology to do them well?" In this chapter, we fi rst provide an overview of tasks involved in analyzing qualitative data, with a focus on increasingly complex projects, before we turn to the software meant to support these tasks. One genre of software, known as Qualitative Data Analysis Software (QDAS or QDA software), is speci fi cally designed to support qualitative research, as opposed to tools primarily used for the collection of data (such as audio or video recorders), or presentation of fi ndings (such as presentation or modeling software). We brie fl y review the historical development of QDA software-including associated methodological questions and issues-before identifying the increasingly diverse array of expected features and functions in most of the current software programs. We then summarize the "user experience" literature and subsequently discuss the boundaries between cadres of qualitative researchers who do use software, and those who do not. Finally, we address potential directions as these programs are being in fl uenced by Web 2.0 developments.

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“…Accordingly, this research employed Nvivo software, which is considered to be one of the most useful software tools for qualitative data analysis. Using Nvivo software enables the researcher to code the collected data as well as to link and create relationships between codes [26]. In addition, it helps to reshape, reorganize, and compare the coding nodes easily.…”

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“…MacMillan's critique of QSR NVivo, Qualrus and MAXQDA included technical difficulties, a steep learning curve, and researcher preference for doing the analysis by hand. MacMillan's conclusions, however, reminded us of Gilbert et al's (2014) observation that:…”

“…The history of this troubled relationship between qualitative researchers and the CAQDAS community has been reported in depth by Davidson and di Gregorio (2011), Gilbert, Jackson, and di Gregorio (2014), and Paulus, Lester, and Britt (2013). Concerns that the software can take control of the analysis or that its primary use is for the quantification of data have resulted in persistent misconceptions regarding CAQDAS.…”

“…Gilbert et al (2014) noted that some of the common difficulties users experience in transitioning from paper-based analysis to software include: (1) missing the ability to work directly on paper, resulting in a feeling of 'distance' from data; (2) engaging in nonproductive coding or analysis practices; (3) experiencing difficulties in managing analytic distance; (4) not recognizing when the software is not doing what you think it is doing; and (5) being tempted to turn qualitative data into quantitative data. In contrast, Gilbert et al (2014) and others have highlighted the benefits of using CAQDAS, including the ability to: (1) work efficiently with a large data-set, (2) work productively and transparently in teams, (3) manage data systematically, and (4) maintain portable and durable data. Further, some researchers have pointed to the ability of software to provide greater transparency around methods as being a central rationale for its use (Odena, 2013;.…”

“…Further, there has been a long-standing critique that CAQDAS only supports traditional forms of coding (Coffey, Holbrook, & Atkinson, 1996) while others argue that in actuality the software affords a variety of forms of analysis, including conversation analysis and discourse analysis (CA/DA) (Lee & Fielding, 1996;Paulus, Lester, & Dempster, 2014;Silver & Lewins, 2014). Gilbert et al (2014) noted that some of the common difficulties users experience in transitioning from paper-based analysis to software include: (1) missing the ability to work directly on paper, resulting in a feeling of 'distance' from data; (2) engaging in nonproductive coding or analysis practices; (3) experiencing difficulties in managing analytic distance; (4) not recognizing when the software is not doing what you think it is doing; and (5) being tempted to turn qualitative data into quantitative data. In contrast, Gilbert et al (2014) and others have highlighted the benefits of using CAQDAS, including the ability to: (1) work efficiently with a large data-set, (2) work productively and transparently in teams, (3) manage data systematically, and (4) maintain portable and durable data.…”