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Qualitative data analysis software (QDAS) programs are well-established research tools, but little is known about how researchers use them. This article reports the results of a content analysis of 763 empirical articles, published in the Scopus database between 1994 and 2013, which explored how researchers use the ATLAS.ti™ and NVivo™ QDAS programs.* The analysis specifically investigated who is using these tools (in terms of subject discipline and author country of origin), and how they are being used to support research (in terms of type of data, type of study, and phase of the research process that QDAS were used to support). The study found that the number of articles reporting QDAS is increasing each year, and that the majority of studies using ATLAS.ti™ and NVivo™ were published in health sciences journals by authors from the United Kingdom, United States, Netherlands, Canada, and Australia. Researchers used QDAS to support a variety of research designs and most commonly used the programs to support analyses of data gathered through interviews, focus groups, documents, field notes, and open-ended survey questions. Although QDAS can support multiple phases of the research process, the study found the vast majority of researchers are using it for data management and analysis, with fewer using it for data collection/creation or to visually display their methods and findings. This article concludes with some discussion of the extent to which QDAS users appear to have leveraged the potential of these programs to support new approaches to research.

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The use of computer-assisted qualitative data analysis software (CAQDAS) has not been without controversy, with a pervasive sense of skepticism and resistance towards its adoption by many scholars. Language-based researchers in particular, such as conversation and discourse analysts, have been slow to embrace such tools for their work. In this paper we illustrate how we have used ATLAS.ti to support our own conversation and discourse analysis work, in order to demonstrate how such a tool can be leveraged to complete nine analytic tasks. Tasks and features we describe include, among others, transcribing and synchronizing transcripts with media files, engaging in unmotivated looking through creating quotations, and conducting a close, line by line analysis through writing memos. We illustrate how ATLAS.ti has allowed us to document our analytic decisions in a transparent, reflexive, rigorous and systematic way. We note, too, limitations of the software such as a lack of real-time collaboration support and challenges inherent to the analysis of video and online interactional data. Rather than taking control away from the researcher, we argue that ATLAS.ti enables the analyst to solve a range of methodological challenges, such as working with large data-sets and supporting deeper levels of analysis than is possible by hand.

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