Christina Silver

Computer assisted qualitative data analysis

The article summarizes some of the approaches, tactics, and tools to describe ways that researchers can qualitatively analyze qualitative data. A short background to the development of Computer-Assisted Qualitative Data AnalysisSoftware packages leads to the parameters of the software category described. Elements of project planning and different aspects of managing qualitative data are discussed with emphasis given to developing functionality and the various types of data which can now be integrated and incorporated. Descriptions of the ways that specific and generally available types of tools within software can help aims to promote the idea that the researcher can individually choose the tools which will support a chosen analytic direction.

From guided-instruction to facilitation of learning: the development of Five-level QDA as a CAQDAS pedagogy that explicates the practices of expert users

This paper introduces Five-level QDA (Qualitative Data Analysis) as a pedagogy for the teaching and learning of CAQDAS (Computer Assisted Qualitative Data AnalysiS) that spans methodologies, software packages and teaching modes. Based on the authors’ personal trajectories of using, teaching and researching CAQDAS since the late-1990s, the paper illustrates the need for a CAQDAS pedagogy by describing the challenges of learners in powerfully harnessing CAQDAS packages. The principles behind Five-level QDA are outlined, which focus on the contrast between the strategies and tactics of conducting QDA with software, and the need to translate between these. The implementation of Five-level QDA as an adaptable method of instruction is illustrated through the use of Analytic Planning Worksheets in the Recurring Hourglass design.

The CAQDAS Postgraduate Learning Model: an interplay between methodological awareness, analytic adeptness and technological proficiency

This paper presents an empirically-derived CAQDAS Postgraduate Learning Model (CPLM) that illustrates the importance of integrated learning of qualitative methodology and technology for postgraduate students. Developed from analysis of a qualitative data-set of interview transcripts, survey responses and support-session observations constructed from two previous studies, CPLM brings the fields of technology acceptance/adoption and CAQDAS together. Analysis reveals that initial enthusiasm for the potential of software is often tempered by frustrations with actual, independent use. However, despite frustrations being attributed to lack of software functionality, CPLM illustrates that successful use of CAQDAS technology amongst postgraduate students is related to methodological awareness, adeptness in the techniques of analysis and technological proficiency.

QDA Miner (With WordStat and Simstat)

There are many Computer Assisted Qualitative Data AnalysiS (CAQDAS) packages. Most are powerful tools supporting the range of qualitative analytic techniques. QDA Miner (http://www.provalisresearch.com/) is one of a suite designed by Provalis Research for textual and image analysis. In its own right QDA Miner is a sophisticated package for conducting robust code-based analysis; when integrated with WordStat and SimStat, possibilities for conducting mixed analyses of mixed data are greatly extended. Provalis Research’s tools in most respects equal others’ in supporting code-based qualitative analysis but outstrip those for quantitative,mixed, and statistical analysis. This review highlights QDA Miner’s distinguishing features and summarizes functions of its concurrent use with WordStat and SimStat that make it a particularly good choice for mixed methods analysis.

It will be very helpful once I understand ATLAS.ti”: Teaching ATLAS.ti using the Five-Level QDA method

ABSTRACT Few studies have explored approaches to teaching qualitative data analysis software (QDAS). As more researchers rely on self-teaching, more research into best practices for developing QDAS expertise is warranted. In this paper, we report our experience using the Five-Level QDA® method to guide the design of an introductory ATLAS.ti workshop. By focusing on the translation process between analytic strategies and the tactics of the software, we sought to help students harness ATLAS.ti powerfully. Using a case study and reflective practice approach, we reviewed instructional materials, observational field notes, instructor reflections, student questionnaires, and interviews to describe what happened during the workshop as well as instructor and learner perceptions of the method. For the method to be successful, methodological competence prior to training and ongoing support after training are necessary, both of which may be fostered by using community-building strategies during and after instruction. Hands-on exploration of the software components during the workshop, including demonstration of both in-progress and completed projects by the instructor and peers, can bring life to the Five-Level QDA method. While the method emphasizes that powerful use of the software requires analytic strategies to drive software tactics, learners may view the two as mutually constituting. Implications for teaching QDAS as well as the development of the Five-Level QDA method are explored.

CAQDAS at a Crossroads: Choices, Controversies and Challenges

The field of Computer Assisted Qualitative Data AnalysiS (CAQDAS) is characterised by choices, controversies and challenges. This chapter first briefly outlines the history of the field and discusses choices between and within programs. It goes on to discuss persisting controversies around the appropriateness of using technology for qualitative data analysis. It then outlines challenges arising from these choices and controversies. The chapter concludes by discussing how the Five-Level QDA™ method, a CAQDAS pedagogy that transcends methodologies, software programs and teaching modes, addresses one of the key challenges: how to appropriately enable researchers with varied needs to harness CAQDAS packages powerfully.

Review: Qualitative Research Design for Software Users, by Silvana di Gregorio and Judith Davidson. Maidenhead, UK: McGraw Hill, Open University Press. 2008. 272 pp

In Qualitative Research Design for Software Users, di Gregorio and Davidson provide a useful account of factors to consider when planning for and using Computer Assisted Qualitative Data AnalysiS (CAQDAS) packages. The book addresses an important gap in the literature, raises a number of issues for further debate, and will help those using qualitative software to plan their work. Some of the assumptions underlying the text, however, are contentious, and therefore this is best seen as a guide for aspects to consider rather than as a recipe for using CAQDAS packages in research. The book comprises three parts covering (1) principles of qualitative research design, both in general terms and when using customized software to facilitate analyses; (2) exemplars of real research projects in different software packages and research settings; and (3) a discussion of implications and practical guidelines for using such software. Although the exemplars include specific discussion of four packages (ATLAS.ti, MAXqda, NVivo, and XSight), di Gregorio and Davidson’s general argument is not software specific. Indeed, they suggest that brand-specific discussions and arguments have worked against the development of richer methodological perspectives, and as such, they introduce and develop the concept of an “E-project” (software project) as a genre. The text also presents a three-part framework for representing research design within the E-project, comprising (1) creating a “software shell” in which descriptive foundations for the research are developed, (2) developing an interpretive (coding) system through disaggregation and recontextualization of data, and (3) relying on an iterative practice that sustains dialog between technology and methodology. Working in this way is seen to enhance project management, transparency, and rigor—the implication being that conducting qualitative research according to the E-project framework will improve quality. In developing their argument, di Gregorio and Davidson suggest that there are specifically “QDAS [qualitative data analysis software]-based