References on qualitative computing and use of NVivoCompiled by Pat Bazeley and Kristi Jackson

Contents

History of qualitative computing...................................................................................1

The development and nature of QDA software generally............................................2

Comparative studies and other (specific) QDA programs............................................6

Methodological issues associated with software use generally (including NVivo).....10

Applications/Methods/Practice...................................................................................19

Guidelines for and experiences in using NUD*IST or NVivo......................................24

History of qualitative computing

Bazeley, P. (2002). The evolution of a project involving an integrated analysis of structured qualitative and quantitative data: From N3 to NVivo. International Journal of Social Research Methodology, 5(3), 229-243. doi: 10.1080/13645570210146285

NOTES: Traces developments in QSR software from NUD*IST 3 through to the first versions of NVivo in so far as they impacted on the capacity of the researcher to integrate mixed methods (qualitative and quantitative) analysis.

Davidson, J., & di Gregorio, S. (2011a). Qualitative research and technology: In the midst of a revolution. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (4th ed., pp. 627-643). Thousand Oaks, CA: Sage.

NOTES: This chapter maps Lincoln and Denzin’s stages of qualitative research with Davidson and di Gregorio’s stages of QDAS development. In addition to providing a partial history of the relationship between technology and qualitative research, it raises questions about the future of qualitative research in the context of Web 2.0 developments.

Richards, L., & Richards, T. (1994). From filing cabinet to computer. In A. Bryman and R. G. Burgess (Eds.), Analyzing qualitative data (pp. 146–172). London: Routledge.

NOTES: A detailed narrative about the neighbourhood study which prompted the early development of NUD*IST. The authors describe the problems of working with a rapidly growing volume of qualitative data. The problems associated with manual methods include an over-emphasis on code-and-retrieve, postponed analysis, and the distancing of data into the filing cabinet. The benefits of moving to a computer system included an ability to separate documents from indexing and building theory.

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Richards, T. (2002). An intellectual history of NUD*IST and NVivo. International Journal of Social Research Methodology, 5(3), 199-214. doi: 10.1080/13645570210146267

ABSTRACT: Since the rise of qualitative computing in the mid-1980s, the field of qualitative data analysis has changed in a number of ways, which remarkably have been ignored in the methodological literature, to the detriment of the area’s self-understanding. This paper provides for the record an account of the intellectual development of the two qualitative data analysis programs that I have designed, together with Lyn Richards. The theme behind the history is: (1) computing has enabled new, previously unavailable qualitative techniques; (2) some important pre-computer techniques and methods were not supported by computerization of the field, at least until recently; and hence (3) computerization encouraged some biases in qualitative techniques. I hope that this paper will act as a source for a revitalized and up-to-date debate on methods and techniques that recognizes computer use as an agent of change in the field and QDA software generally

The development and nature of QDA software generally

Many of the references in this section also offer a historical perspective on QDAS. They raised issues for the time when they were written, issues which have since been addressed by the developments in computing generally and QDAS in particular. Take note of the dates of publication when drawing information and inferences from them!

Bong, Sharon A. (2002). Debunking myths in qualitative data analysis. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 3(2): Article 10. 

ABSTRACT: In deciding on CAQDAS use in my research, I deliberate firstly the primacy of grounded theory as a methodology and secondly the primacy of coding as a method. In the first section of this paper, I weigh the extent to which my research draws and departs from the principles and practices of grounded theory (GT). In examining the impact of cultures and religions on women's human rights in Malaysia I have used for example hypothesis-guided criteria for sampling. This is strictly speaking not in the original sense a grounded theory approach. In the paper, I make transparent the extent to which GT has informed my work in enhancing the qualitative research and in highlighting the uses and limits of grounded theory, I pose the question to what extent have I de-mystified its paradigmatic status in CAQDAS and its homogenising effects. In the second section, I discuss the dominance of coding in qualitative data analysis and I argue that the pitfall of reifying coding as analyses can be avoided through a researcher's reflexivity and agency (self-determination) combined with a pragmatic view and the use of codes as a means and not as an end. I discuss whether CAQDAS use essentially facilitates the rigour of methodology and the transparency of method as for example manifested in one's audit trail, and whether this in turn constitute research that is more accountable, innovative and effective.

Coffey, Amanda; Holbrook, Beverley, & Atkinson, Paul (1996). Qualitative data analysis: technologies and representations. Sociological Research On-line, 1(1). http://www.socresonline.org.uk/1/1/4.html

ABSTRACT: In this paper we address a number of contemporary themes concerning the analysis of qualitative data and the ethnographic representation of social realities. A contrast is drawn. On the one hand, a diversity of representational modes and devices is

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currently celebrated, in response to various critiques of conventional ethnographic representation. On the other hand, the widespread influence of computer- assisted qualitative data analysis is promoting convergence on a uniform mode of data analysis and representation (often justified with reference to grounded theory). We note the ironic contrast between these two tendencies, the heterodox and the orthodox, in contemporary qualitative research. We go on to suggest that there exist alternatives that reflect both the diversity of representational approaches, and the broader possibilities of contemporary computing. We identify the technical and intellectual possibilities of hypertext software as offering just one such synthesis.

Fielding, N. (2008). The role of computer-assisted qualitative data analysis: Impact on emergent methods in qualitative research. In S. Hesse-Biber, & P. Leavy (Eds.), Handbook of emergent methods (pp. 675-695). New York: Guilford Press.

NOTES: Fielding traces developments in the field of qualitative computing as it has developed over 20 years – its emergence, the different types that have been developed, and the uses to which researchers have put it – as a backdrop to profiling new developments and possibilities, including using software tools for mixed methods work, developments in grid and high performance computing that facilitate analysis of distributed and/or archival material, and the use of XML and HTML applications for qualitative research.

Fielding, N., & Lee, R. (1998). Computer analysis and qualitative research. Thousand Oaks, CA: Sage.

PUBLISHER NOTES: The use of computers in qualitative research has dramatically changed the way social researchers handle qualitative data and computer-assisted qualitative data analysis (CAQDAS) has become an indispensable element in the researcher’s tool kit. Authors Nigel G. Fielding and Raymond M. Lee, leading researchers in the field, provide a lucid and accessible text on the nature of this change and profile potential new approaches to qualitative data analysis. They report on findings from the first systematic field research on the impact of CAQDAS. They analyze the rapidly growing popularity and legitimacy of qualitative research methods, looking at users’ experiences of CAQDAS and the advantages and disadvantages of computer use, research resources, and the status of qualitative research. Fielding and Lee also cover the principal approaches in qualitative research and show how leading computer programs are actually used. They provide a framework for developing the craft and practice of CAQDAS and conclude by examining new techniques and the evolution of qualitative research to meet new challenges.

Hesse-Biber, S. & Croft, C. (2008). User-centered perspectives on qualitative data analysis software: Emergent technologies and future trends. In S. Hesse-Biber & P. Leavy (Eds.), Handbook of Emergent Methods (pp. 655-674). New York: Guilford Press.

NOTES: Describes the characteristics of qualitative research and analysis as a foundation for tracing the emergence of qualitative software, suggesting that the different functions provided by various qualitative programs accommodate different methods. They provide a set of questions for the user, to guide choice of program for their qualitative analysis. Finally they look to the future and directions in which software for qualitative analysis is moving including the use of XML and HTML, the digitization of

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multimedia data and multi-site teamwork (the latter options now being a reality), and the incorporation of artificial intelligence within qualitative analysis software.

Koenig, T. (n.d. – draft). Routinizing frame analysis through the use of CAQDAS. Department of Social Sciences, Loughborough University. http://www.restore.ac.uk/lboro/research/case_studies/hohmann/frames_and_CAQDAS.pdf

ABSTRACT: Even though frame analysis has become a popular analytical framework in media studies and social movement research, the methodological underpinnings of the empirical identification of frames lack systematization and have consequently remained underdeveloped. This paper consolidates recent advances in the empirical measurement of frames and explores, in how far computer-assisted qualitative data analysis software (CAQDAS) can extend on these methodologies. Because framing has become a fairly widely used but ill-defined concept, the paper will start with a brief delineation of framing theory as it is understood for present purposes. Next, current attempts to measure frames empirically in a systematic fashion will be discussed and a methodology, which synthesizes some of these approaches will be proposed. This methodology attempts in a first step to draw on existing knowledge on metanarratives to avoid a purely inductive identification of frames. Additionally, automatic word mapping tools such as Leximancer, Sphinx Survey Lexica are suggested as interpretative aids. In a second step, the analyst identifies a set of keywords, key phrases, and possibly audial or visual symbols that indicate frames in his data. These indicators are then used in a third step to semi-automatically identify frames in the remainder of the data. Keywords that might acquire different meanings in different contexts are inspected in their contexts by the analyst, who decide on their coding. This method avoids both the rigidities that come with fully automatic keyword clustering, which may lead to the inclusion non-interpretable keywords as well as the exclusion of so-called stop words such as prepositions and articles, which under certain circumstances might indeed be the strongest indicators for certain frames. At the same time it allows for a degree of routinization and systematization in frame analysis, whose quality has notoriously depended on the creativity of the framing researchers. Five CAQDAS – ATLAS.ti, Kwalitan, MAXqda, NVivo, and Qualrus – are examined with respect to their usability in this type of framing research.

Lee R. M., & Fielding, N. (1996, December). Qualitative data analysis: Representations of a technology: A comment on Coffey, Holbrook and Atkinson. Sociological Research Online, 1(4). http://www.socresonline.org.uk/1/4/lf.html

NOTES: A response to the article by Coffey, Holbrook and Atkinson where the authors respond to the criticism of CAQDAS orthodoxy, and the emphasis of grounded theory in CAQDAS development, both of which they refute.

Lewins, A. (2010). What is CAQDAS. Paper presented at the NCRM Research Methods Festival 2010, St. Catherine's College, Oxford. Power Point available: http://eprints.ncrm.ac.uk/1525/

NOTES: The goals of the presentation were to: Provide context about QDAS with a review of the historical debate, a description of what it has come to mean, a discussion of what it is and what it isn’t, how it developed, and a brief demonstration of key features.

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Lewins, A., & Silver, C. (2007) Using Software in Qualitative Research: A Step-by-Step Guide. London: Sage. 

NOTES: Sets a context and provides guidance for using three QDA programs: Atlas.ti, MAXqda, and NVivo (versions covered are now out of date: new updated edition due late 2013, to include a wider range of QDAS).

MacMillan, K. (2005). More than just coding: Evaluating CAQDAS in a discourse analysis of news texts. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 6(3), Article 25. http://www.qualitative-research.net/index.php/fqs/article/view/28/60

Abstract: Computer assisted qualitative data analysis software (CAQDAS) is frequently described as a tool that can be used for "qualitative research" in general, with qualitative analysis treated as a "catch-all" homogeneous category. Few studies have detailed its use within specific methods, and even fewer have appraised its value for discourse analysis (DA). While some briefly comment that CAQDAS has technical limitations for discourse analysis, in general, the topic as a whole is given scant attention. Our aim is to investigate whether this limited interest in CAQDAS as a qualitative tool amongst discourse analysts, and in DA as a research method amongst CAQDAS users, is practically based; due to an uncertainty about research methods, including DA; or because of methodological incompatibilities. In order to address these questions, this study is based not only on a review of the literature on CAQDAS and on DA, but also on our own experience as discourse analysts putting some of the main CAQDAS to the test in a media analysis of news texts.

MacMillan, K., & Koenig, T. (2004). The wow factor: Preconceptions and expectations for data analysis software in qualitative research. Social Science Computer Review, 22(2), 179-186. 

ABSTRACT: Discussions on computer-assisted qualitative data analysis software often begin with the assumption that research will automatically be improved through the use of such software. Consequently, reviews frequently focus on practical concerns with the various software packages. Rather than theoretical considerations of its suitability to the method of analysis, such descriptions frequently treat software as the method of analysis. The following article calls for a clearer understanding of the role of software within research, with critical evaluation focusing on the methodological issues surrounding software use, as well on its technological innovations. The authors examine a number of factors that foster a tendency toward uncritical appraisal--including unrealistic expectations of the software as a methodology in itself; the treatment of qualitative analysis as a single, homogenized category; and the use of grounded theory as a legitimating link between tool and method.

Mangabeira, Wilma C, Lee, Raymond M, & Fielding, Nigel G. (2004). Computers and qualitative research: Adoption, use and representation. Social Science Computer Review, 22(2), 167-178. 

ABSTRACT: Drawing on a range of sources, this article examines patterns in the adoption, appropriation, and use of qualitative analysis software (or CAQDAS--Computer-Assisted Qualitative Data Analysis Software) in the United Kingdom. It is argued that the take-up and use of CAQDAS, representations of computer-assisted as opposed to manual analysis, and certainty about the utility of CAQDAS, are related to

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user generation. The changing composition of the user base for CAQDAS programs is also discussed.

Richards, T., & Richards, L. (1994). Using computers in qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of Qualitative Research (pp. 445-462). Thousand Oaks, CA: Sage.

NOTE: A discussion of the new frontiers (of the time) in qualitative research computing and text-based data. The chapter places an emphasis on the relationship between data, software, and theory construction. After they compare the abilities of word processing, text retrievers, and relational databases, the authors explore rule-based theory-building systems, logic systems, the index-based approach, and conceptual network systems.

Silver, C., & Fielding, N. (2008). Using computer packages in qualitative research. In C. Willig & W. Stainton-Rogers (eds.), The SAGE handbook of qualitative research in psychology. Thousand Oaks, CA: Sage. pp. 334-351. 

Comparative studies and other (specific) QDA programs

Many of the papers in this section stem from the KWALON experiment in which five developers of software analysed the same dataset. See Evers et al., 2010, for further details..

Barry, C. A. (1998). Choosing qualitative data analysis software: Atlas/ti and NUD*IST compared. Sociological Research Online, 3(3). http://www.socresonline.org.uk/3/3/4.html

Abstract: Choosing between Nudist and Atlas/ti, the main qualitative data analysis software packages can be difficult. To assist researchers in making this choice, I have conceptualised their differences along two dimensions, related to the qualities of the software and of the research project. The software dimension is structural design, and the project dimension is complexity. Software structure is dichotomised between structured, sequential, verbal versus visual, spatial, interconnected modes of operation. Projects are dichotomised between homogeneous sample, short timeframe, single data-type, single data analyst; versus multiple samples, longitudinal data, multiple data types and team data analysis. First I review the CAQDAS literature. Then I outline the different personalities and strengths of Atlas/ti and Nudist, and show how they match these dimensions. I offer suggestions as to how to choose, and whether to use in tandem with complementary conceptual network software.

Corti, L., & Gregory, A. (2011, January). CAQDAS comparability. What about CAQDAS data exchange? Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 35.

ABSTRACT: This article seeks to address the theme of the comparability of Computer Assisted Qualitative Data AnalysiS (CAQDAS) packages through comparing current software exchangeability and portability. Our perspective is from a data sharing and archiving perspective and the need for open data exchange standards for qualitative data which will enable longer-term sustainability of both data collections and of annotations on these data. Descriptive metadata allow us to describe data robustly and using a common standard enables us to tap the common features of any complex collection. A set of "raw" research outputs (data) have common descriptive elements such as how the research project was funded and how the data were sampled, collected and analysed to form

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conclusions from that investigation. Data kept for the longer term must ideally be software and platform independent. In this way, we can help future-proof data resources. Most CAQDAS packages use proprietary databases to manage their data and annotations, and very few enable export of annotated data. In this article we argue for an open descriptive standard that will enable description and interpretation of data for the longer term in data archives and to which proprietary software, such as all CAQDAS packages, can import and export. The use of the term "annotation" or "annotating" is taken to mean any action on the text—classifying, coding, memoing or relating. This meaning of the term is commonly used in the linguistic community, but less so by social scientists.

Dempster, P. G., & Woods, D. K. (2011, January). The economic crisis though the eyes of Transana. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 16. http://nbn-resolving.de/urn:nbn:de:0114-fqs1101169

ABSTRACT: For the KWALON 2010 conference, several software representatives were given a common data set consisting of text and media files about the financial crisis of 2008 – 2009. This paper focuses on the process of analyzing the media files in this data set using Transana, a software package designed for the transcription and qualitative analysis of video and audio data. The authors describe several styles of transcription used in the process of making sense of the data, the selection and coding of analytically interesting segments of the media files, and working with coded data to develop a coherent narrative from this data. They also describe their collaborative process, as facilitated by the software, and how that affected the analysis of the data. Finally, the authors describe the results of their analysis in terms of the multi-layered narrative of the data, and discuss the limitations of that analysis.

Dicks, B., Mason, B., Coffey, A., & Atkinson, P. (2005). Qualitative research and hypermedia: Ethnography for the digital age, London: Sage.

PUBLISHER NOTES: Digital culture and digital technologies have rapidly become unavoidable and essential forms of social experience and communication in our emerging globalised society. If we want to attempt to analyse and understand our technology-saturated society, and all its new media, then we must also develop research methods and forms of analysis that can accommodate and exploit digital culture and digital technologies. This text sets out to equip qualitative researchers with the tools necessary to conduct ethnography in the age of email and the internet. It will investigate how digital technologies potentially transform the ways in which we do research. This text also introduces the reader to new emerging methods that utilise new technologies and explains how to conduct data collection, analysis and representation using new technologies and `hypermedia'

di Gregorio, S. (2011, January). Comment: KWALON conference: Is qualitative software really comparable? Reflections on "the experiment": An "expert" view. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1).http://nbn-resolving.de/urn:nbn:de:0114-fqs1101C35

NOTE: From the perspective of an expert in QDAS, this comment (available only in html) affirms that while there are differences among the QDAS options, they are not significant. She concludes with advice to novices: “My advice to novices is to 1. get a firm grounding of the various approaches to analyzing qualitative data and 2. learn one package very

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well. The choice of package depends firstly, on what colleagues in your area are using so you can tap into a support network and secondly, on what type of data you are likely to use.”

Evers, J. C., Silver, C., Mruck, K., & Peeters, B. (2010). Introduction to the KWALON experiment: Discussions on qualitative data analysis software by developers and users, Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 40. http://nbn-resolving.de/urn:nbn:de:0114-fqs1101405.

ABSTRACT: In this introduction to the KWALON Experiment and related conference, we describe the motivations of the collaborating European networks in organising this joint endeavour. The KWALON Experiment consisted of five developers of Qualitative Data Analysis (QDA) software analysing a dataset regarding the financial crisis in the time period 2008-2009, provided by the conference organisers. Besides this experiment, researchers were invited to present their reflective papers on the use of QDA software. This introduction gives a description of the experiment, the "rules", research questions and reflective points, as well as a full description of the dataset and search rules used, and our reflection on the lessons learned. The related conference is described, as are the papers which are included in this FQS issue.

Friese, S. (2011, January). Using ATLAS.ti for analyzing the financial crisis data. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 39. http://nbnresolving.de/urn:nbn:de:0114-fqs1101397

NOTES: The author claims that the use of QDAS enhances the research in five ways: 1) There is more flexibility in modifying code names and coded data segments and thus coding can be approached in a different way, 2) software offers many more analysis options and thus allows researchers to ask different questions, 3) it makes it easier to combine qualitative and quantitative methods, which of course does not preclude a pure qualitative approach, 4) it allows researchers to work in teams even across geographical boundaries, and 5) it allows qualitative researchers to move out of the black box of analysis and to make the entire analysis process more transparent, thus adding credibility, confirmability and dependability. The paper traces strategies for the development of codes and definitions within ATLAS.ti and uses 11 screen captures to demonstrate the evolution of thought during the coding process.

Hesse-Biber, S. N., & Dupuis, P. (2000). Testing hypotheses on qualitative data: The use of HyperRESEARCH computer-assisted software. Social Science Computer Review, 18(3), 320-328. 

ABSTRACT: Describes the analysis strategy of the hypothesis-testing component of the HyperRESEARCH computer-assisted software program. Application of the software in quantitative and qualitative data analysis; Benefits of the strategy of analysis of HyperRESEARCH's Hypothesis Tester to researchers; Implications of using the Hypothesis Tester for qualitative researcher.

Kuckartz, A. M., & Sharp, M. J. (2011, January). Responsibility: A key category for understanding the discourse on the financial crisis—analyzing the KWALON data set with MAXQDA 10. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 22. http://nbn-resolving.de/urn:nbn:de:0114-fqs1101222

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ABSTRACT: This article explains the process and findings of a computer-supported (MAXQDA 10) qualitative analysis of the financial crisis based on text, audio and video files provided by the KWALON program committee. Initial findings show that those writing about the crisis found it important to name those persons and factors they considered responsible for the financial problems, although there was no consensus on who or what was most to blame.

Kuş Saillard, E. (2011, January). Systematic versus interpretive analysis with two CAQDAS packages: NVivo and MAXQDA. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 34. http://nbn-resolving.de/urn:nbn:de:0114-fqs1101345

ABSTRACT: The purpose of this study is to compare two different Computer Assisted Qualitative Data AnalysiS (CAQDAS) packages (NVivo and MAXQDA) on a specific aspect. The same data from an auto-photographic study is analyzed using the same approach to make the comparison. The comparison is not based on a data oriented evaluation, but the methodological approach used constitutes the basis for the evaluation. The criteria for the evaluation of these tools are: closeness to the data, ease of coding and memoing, and the interrelationship among the data, code and the memo which were derived from the methodological approach. The first level coding process was accomplished using Grounded Theory Methodology (GTM) with two different CAQDAS packages. As for the result, in the GTM interpretation is crucial and MAXQDA supports the interpretive style better than NVivo.

Lewins, A., & Silver, C. (2007). Using software in qualitative research: A step-by-step guide. Thousand Oaks: Sage. (New edition due late 2013)

PUBLISHER NOTES: Combines several aspects of Computer Assisted Qualitative Data Analysis (CAQDAS), helping the reader choose the most appropriate package for their specific needs and get the most out of the software once they are using it. The text considers tasks and processes, bringing them together to demystify qualitative software and encourage flexible and critical choices and uses of software in supporting analysis. This text can be read as a whole or chapters can be treated on a more stand-alone basis, building on one another to provide a holistic sense of the analytic journey without advocating a particular sequential process.

Schönfelder, W. (2011, January). CAQDAS and qualitative syllogism logic—NVivo 8 and MAXQDA 10 compared. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 21. http://nbn-resolving.de/urn:nbn:de:0114-fqs1101218

ABSTRACT: Qualitative research is a heterogeneous field comprised of different and sometimes competing analytical strategies. A growing number of researchers use computer programs to assist in the analysis of qualitative data. Some analytical tasks are common ground and will be performed by most researchers regardless of their methodological approach. Software vendors try to accommodate an increasing demand for common and specific analytical needs and include an ever growing number of features in their products. Depending on the methodological point of departure, some functions provided in the current generation of CAQDAS packages may appear controversial because they invite the user to exceed the limits for the conclusions which can be drawn from qualitative analysis. In this article the limits for drawing conclusions from qualitative data are discussed from a social constructivist, discourse analytical perspective. This is described with the concept of qualitative syllogism logic. Some tools

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provided by CAQDAS packages are used in most qualitative methodological frameworks. These are discussed before two CAQDAS packages, NVivo 8 and MAXQDA 10, are compared with regard to how intuitively they provide these basic tools along with those that appear controversial because they exceed the limits of qualitative syllogism logic.

Weitzman, E., & Miles, M. (1995). Computer programs for qualitative data analysis. Thousand Oaks, CA: Sage.

This is a now rather dated comparison of the many different types of QDAS that were available in the early 1990s – of historical more than practical interest.

Methodological issues associated with software use generally (including NVivo)

Basit, T. N. (2003). Manual or electronic? The changing role of coding in qualitative data analysis. Educational Researcher, 45(2), 143-154.

ABSTRACT: Data analysis is the most difficult and most crucial aspect of qualitative research. Coding is one of the significant steps taken during analysis to organize and make sense of textual data. This paper examines the use of manual and electronic methods to code data in two rather different projects in which the data were collected mainly by in-depth interviewing. The author looks at both the methods in the light of her own experience and concludes that the choice will be dependent on the size of the project, the funds and time available, and the inclination and expertise of the researcher.

Bergin, M. (2011). NVivo 8 and consistency in data analysis: Reflecting on the use of a qualitative data analysis program. Nurse Researcher, 18(3), 6-12.

ABSTRACT: Aim: Qualitative data analysis is a complex process and demands clear thinking on the part of the analyst. However, a number of deficiencies may obstruct the research analyst during the process, leading to inconsistencies occurring. This paper is a reflection on the use of a qualitative data analysis program, NVivo 8, and its usefulness in identifying consistency and inconsistency during the coding process. Background: The author was conducting a large-scale study of providers and users of mental health services in Ireland. He used NVivo 8 to store, code and analyse the data and this paper reflects some of his observations during the study. Discussion: The demands placed on the analyst in trying to balance the mechanics of working through a qualitative data analysis program, while simultaneously remaining conscious of the value of all sources are highlighted. Conclusion: NVivo 8 as a qualitative data analysis program is a challenging but valuable means for advancing the robustness of qualitative research. Implications for practice: Pitfalls can be avoided during analysis by running queries as the analyst progresses from tree node to tree node rather than leaving it to a stage

Berners-Lee, T. 2009 The next web of open, linked data [YouTube video]. Available at http://www.youtube.com/watch?v=OM6XIICm_qo

NOTES: A TED video (16 minutes) of the way diverse data on the internet can be used to generate important research questions and answers across disciplines.

Blismas, N. G., and Dainty, A. R. J. (2003). Computer-aided qualitative data analysis: panacea or paradox? Building Research & Information, 31(6), 455-463.

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NOTES: The authors reflect on the use of NVivo in research on a study within the construction industry about multiproject environments (MPEs). The data was comprised of interviews, program documentation, and organizational publications. They begin with the claim that little literature has been produced that reflects on the use of QDAS and that QDAS often restricts instead of assists the analysis. They present several cautions regarding the association between QDAS and rigor or transparency and they warn readers that QDAS use is often simply a strategy to convince skeptical positivists of the rigor of inductive research techniques. They detail several limitations/challenges in their use of the software and end with a call for additional research on the influence of QDAS on the research process.

Crowley, C., Harre, R. and Tagg, C. (2002) Qualitative research and computing: Methodological issues and practices in using QSR NVivo and NUD*IST. International Journal of Social Research Methodology 5(3), 193-197. 

NOTES: An editorial that introduces a special issue focused on two interdisciplinary conferences on qualitative research and computing that were convened at the Institute of Education, University of London.

Davidson, Judith and Jacobs, Cynthia. (2008). The Implications of Qualitative Research Software for Doctoral Work: Considering the Individual and Institutional Contexts. Qualitative Research Journal, 8 (2), 72-80. doi: 10.3316/QRJ0802072

ABSTRACT: As qualitative researchers struggle to come to grips with the technological revolution, they are faced with the necessity of learning and teaching qualitative data analysis software in higher education research courses. This change has significant implications for their practice as researchers and teachers. In this article we provide experienced-based recommendations for individual practice (research instructors, dissertation advisers, and doctoral students) and for institutional practice (scaling up for deep integration of qualitative data analysis software). Our recommendations are grounded in hard-earned experience gleaned from many years of working with individuals and institutional contexts to improve the use of qualitative research in higher education.

di Gregorio, S., & Davidson, J. (2008). Qualitative research design for software users. Berkshire: Open University Press.

NOTES: The book provides guidance on how to manage a qualitative research project in a QDAS database, regardless of the particular software. The first section outlines the argument by explaining concepts such as the E-Project (the digital container for any study analyzed with QDAS). The second section describes eight QDAS projects in four sectors (higher education, basic science, public government, and commercial) with various methodologies (ethnography, traditional evaluation, case study, etc.) using four QDAS programs (ATLAS.ti, MAXqda, NVivo, and XSight). The final chapter addresses strategies for developing sustainable use of QDAS in academic and non-academic settings.

Evers, J. C. (2011, January). From the past into the future. How technological developments change our ways of data collection, transcription, and analysis. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 38. http://nbnresolving.de/urn:nbn:de:0114-fqs1101381.

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ABSTRACT: In the last fifty years, recording devices have taken a central position on stage in the empirical social sciences during data collection (tape and voice recorders, photo and video cameras). As Qualitative Data Analysis software (QDA software) enables us now to directly code digitalized media files, one might question the need for transcribing data files, thus transforming them into textual files. The central issue addressed in this article then, is whether or not QDA software enables us to skip the transcription of data (audio files and video files). To address this question, the why, what and how of transcription will first be explored. Secondly, manual transcription will be compared to transcription with voice recognition software. Thirdly, coding of textual transcripts will be compared to the direct coding of audio and/or video files. As QDA software is changing our analytic possibilities and to some extent our procedures, the conclusion will argue in favor of transcription, be it adapted to our research needs and integrated within QDA software.

Fielding, N. G. (2000). The shared fate of two innovations in qualitative methodology: The relationship of qualitative software and secondary analysis of archived qualitative data. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 1(3), Article 3. http://www.qualitative-research.net/index.php/fqs/article/view/1039/2248

ABSTRACT: This article considers the contribution that software to support qualitative data analysis can make in the secondary analysis of qualitative data. The article suggests some benefits of secondary analysis of qualitative data and addresses some of the methodological criticisms that have been made about secondary analysis in qualitative research. The article's focus is largely practical, but it also offers an account of why the apparent advantages of using qualitative software in the secondary analysis of qualitative data have not so far been fully exploited. It does so by reference to the social context of the research environment.

Fielding, Nigel G. (2012). Triangulation and mixed methods designs: Data integration with new research technologies. Journal of Mixed Methods Research, 6(2), 124–136.

ABSTRACT: Researchers who advocate the use of multiple methods often write interchangeably about 'integrating', 'combining' and 'mixing' methods, sometimes eliding these descriptors with 'triangulation', which itself encompasses several meanings. In this article we argue that such an elision is problematic since it obscures the difference between (a) the processes by which methods (or data) are brought into relationship with each other (combined, integrated, mixed) and (b) the claims made for the epistemological status of the resulting knowledge. Drawing on the literature for examples, we set out different rationales for using more than one method, then we develop a definition of integration of methods as a specific kind of relationship among methods. We also discuss different places in the research process where integration can occur: for instance, data from different sources can be integrated in the analysis stage, or findings from different sources at the point of theorizing. 

Gilbert, L. S. (2002). Going the distance: ‘closeness’ in qualitative data analysis software. International Journal of Social Research Methodology, 3(5): 215-228. 

NOTES: A paper on the findings from a qualitative study that followed researchers as they used NUD*IST. The tool metaphor is explored, and key concepts include the tactile-digital divide, the coding trap, the metacognitive shift, and issues of trustworthiness.

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Gilbert, L. S., Jackson, K., & di Gregorio, S. (2013). Tools for analyzing qualitative data: the history and relevance of qualitative data analysis software. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (Ch. 18). London: Routledge.

This 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 reflects the role of technology in the context of complex intellectual work. “Tools” is a broad term, with multiple interrelated but not unified dimensions; multiple technological tools can be used to achieve an intended analytic goal, and very different theoretical approaches (also tools) often involve the same generic tasks. This interrelationship is an important point: the most common question from novices is “which program should I use?” when they would be better served by asking “what analytical tasks will I be engaged in, and how can I leverage technology to do them well?” In this chapter, we will first provide a broad albeit cursory overview of tasks involved in analyzing qualitative data before we turn to the software meant to support those processes. This genre of software, known as Qualitative Data Analysis Software (QDAS or QDA software), is specifically 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 findings (such as presentation or modelling software). We will briefly review the historical development of QDA software – including associated theoretical questions and issues – before identifying expected features and functions in current programs. Finally, we will address potential directions as these programs are being influenced by Web 2.0 developments.

Jackson, K. (2003). Blending technology and methodology: A shift towards creative instruction of qualitative methods with NVivo. Qualitative Research Journal, 3(Special Issue), 96-110. http://www.aqr.org.au/docs/journals/special_AQR2003.pdf

NOTES: A guide for incorporating NVivo into qualitative methods courses, from the perspective of an evaluation researcher. The article provides a rationale for including the software in methods instruction and examples of class activities that help students simultaneously explore methodological issues (such as inductive and deductive coding) and software issues.

Kaczynski, D., & Kelly, M. (2004, November). Curriculum development for teaching qualitative data analysis online. Paper presented at the International Conference on Qualitative Research in IT & IT in Qualitative Research, Brisbane, Australia. (All conference papers – fee attached) http://www.griffith.edu.au/conference/qualit2004/

ABSTRACT: This paper explores the unique curriculum design issues involved with the integration of qualitative data analysis software (QDAS) in online instruction of qualitative research. The Qualitative Research III – Analysis course was designed for graduate students and offered in a blended online format using the NVivo® software program and the learning management system, Desire2Learn. Although embedding the NVivo®

software into the fabric of the course afforded several advantages, it also posed challenges for the students and the instructor.

Kaczynski, D. (2004, December). Examining the impact of qualitative data analysis software upon the analysis process. Paper presentation at the annual conference of the Australian

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Association for Research in Education, Melbourne, Australia. http://www.aare.edu.au/04pap/kac041065.pdf

ABSTRACT: The dramatic growth in the use of qualitative data analysis software (QDAS) in the qualitative methodological design process is changing how researchers approach analysis. Qualitative researchers are progressively expanding the adoption of QDAS as a tool in the interpretation and analysis stages, and the increasing application has been cited as a major contribution to the rigor and credibility of qualitative research. There has been little systematic discussion, though, of various QDAS functions relevant to educational research. Moreover, software use has also raised concerns that the tools increasingly drive methodological practices. Qualitative data collection, analysis, and reporting require consistent, diligent attention in order to ensure a rigorous study. Most qualitative researchers agree that a steadfast focus on a study’s purpose and a consistent adherence to a prescribed conceptual framework are critical to a rigorous study. Fewer researchers agree, however, on the appropriate use of QDAS in this process. As each new generation of qualitative software increasingly alters research methods, there is a need for continuing education of researchers in this dynamic process, and continued critique of methodological innovations. How researchers respond to this challenge will significantly impact our conceptualization of the future of qualitative research.

Kelle, U. (1995) (Ed.), Computer-aided qualitative data analysis: Theory, methods and practice. London: Sage.

NOTES: A collection of papers from the Bremen conference where all the major QDAS developers contributed to discussion about the development of qualitative software and the links between software and methodology.

Kelle, U. (1997). Theory building in qualitative research and computer programs for the management of textual data. Sociological Research Online, 2(2), Article 1. http://www.socresonline.org.uk/socresonline/2/2/1.html

ABSTRACT: This article refers to recent debates about the potential methodological costs and benefits of computer use in qualitative research and about the relationship between methodological approaches (eg. ‘Grounded Theory’) on the one hand and computer-aided methods of qualitative research on the other. It is argued that the connection between certain computer-aided strategies and methodological approaches is far more loose than is often assumed. Furthermore, the danger of methodological biases and distortion arising from the use of certain software packages is overemphasized in current discussions, as far as basic tasks of textual data management (‘coding and retrieval’) usually performed by this software are concerned. However, with the development of more advanced and complex coding and retrieval techniques, which are regarded by some authors as tools for ‘theory building’ in qualitative research, methodological confusion may arise if basic prerequisites of qualitative theory building are not taken into consideration. Therefore, certain aspects of qualitative theory building which are relevant for computer aided methods of textual data management are discussed in the paper.

Kelle, U. (2004). Computer-assisted qualitative data analysis. In C. Seale, G. Gobo, J. F. Gubrium & D. Silverman (Eds.), Qualitative research practice (pp. 473-489). London: Sage.

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FROM AUTHOR INTRODUCTION: In the first section of this chapter I shall discuss how code-and-retrieve facilities provided by almost every CAQDAS package can make visible a problem often hidden if manual methods are used – the problem of finding adequate categories and concepts for structuring data. In the second section it will be shown how this issue relates to the demanding methodological problem of defining the role of theory and the researcher’s previous knowledge in the research process. The final section will relate these considerations to sophisticated tools for theory building and hypothesis testing nowadays provided by almost all the available CAQDAS packages.

Lu, C.-J., & Shulman, S. W. (2008). Rigor and flexibility in computer-based qualitative research: introducing the Coding Analysis Toolkit. International Journal of Multiple Research Approaches, 2(1), 105-117.

ABSTRACT: Software to support qualitative research is both revered and reviled. Over the last few decades, users and skeptics have made competing claims about the utility, usability and ultimate impact of commercial-off-the-shelf (COTS) software packages. This paper provides an overview of the debate and introduces a new web-based Coding Analysis Toolkit (CAT). It argues that knowledgeable, well-designed research using qualitative software is a pathway to increasing rigor and flexibility in research.

Maietta, R. (2008). Computer-assisted data analysis. in Given L. (Ed.), The Sage encyclopedia of qualitative research methods, (pp. 103-108). Thousand Oaks: Sage Publications, Inc.

NOTES: A summary of the tools available in the most common QDAS options. QDAS is described as a tool kit that simulates off-screen approaches.

Marshall, H. (2002). What do we do when we code data? Qualitative Research Journal, 2(1), 56-70.

NOTES: QDAS offers possibility of more efficient data coding and management than previously, with a consequence of creating more codes but this doesn’t necessarily mean better analysis. The fear that computers will stifle creativity, reduce variety by imposing code and retrieve methods, stem from positivist assumptions. Some suggest coding should be more like evaluating art than using logic. Others prefer to see rigour and good audit trails, without losing creativity. QDAS won’t stifle a creative person, and it also won’t rescue a careless one. Suggestions for avoiding ‘the coding trap’.

Richards, L. (2002). Rigorous, rapid, reliable and qualitative? Computing in qualitative method. American Journal of Health Behavior, 26(6), 425-430.

ABSTRACT: Objective: To explore whether qualitative methods are problematic and persuasive in health education research. Method: Explored this problem through the 3 goals of rigor, rapidity, and reliability and their special meanings in qualitative analysis. Results: For each, contributions of qualitative computing software are identified and their effects assessed. Conclusion: Qualitative researchers are assisted by software tools in pursuit of each of these goals, but in each area there is a need for software design to address the tasks of research where rigor, rapidity, and reliability are paramount requirements.

Richards, L. (1998). Closeness to data: The changing goals of qualitative data handling. Qualitative Health Research, 8(3), 319-328.

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ABSTRACT: Identifies four different meanings of `closeness to data,' as applied in qualitative research and data handling. Meaning of closeness; Problems of closeness; Case for distance; Keeping data alive

Richards, L. (1999). Data alive! The thinking behind NVivo. Qualitative Health Research, 9(3), 412-428. doi: 10.1177/104973239900900310

ABSTRACT: Explores the goals of rich data in qualitative health research and the ways NUD.IST Vivo addresses these. What richness requires of the researcher; Aspects to do with rich text and dynamic documents; Conclusion

Roberts, K. A., & Wilson, R. W. (2002). ICT and the research process: Issues around the compatibility of technology with qualitative data analysis. Forum: Qualitative Social Research - Sozialforschung, 3(2), Article 23. http://www.qualitative-research.net/index.php/fqs/article/view/862/1873

ABSTRACT: This paper explores the nature of qualitative data and the uneasy relationship it holds with computer-aided analysis. Qualitative research produces data that are rich and voluminous, shedding light on the lived experience of the "being-in-the-world" and the interactions inherent in complex social phenomena. Analysis of such data, however, is complex and time consuming in addition to which there is a lack of specific guidance on how to carry it out. The authors note that the philosophy underpinning information and communication technology (ICT) is not wholly compatible with that which underpins qualitative research. ICT is based largely on logical, objective and quantifiable procedures whereas qualitative research requires a more subjective, interpretative stance and seeks to explore meaning. On this understanding of the philosophies involved it is argued that the role of computer software in qualitative data analysis is limited.

Schuhmann, C. (2011, January). Comment: Computer technology and qualitative research: A rendezvous between exactness and ambiguity. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1). http://nbn-resolving.de/urn:nbn:de:0114-fqs1101C27

A reflection on the KWALON experiment (see Evers et al., 2010 for further details), from a QDAS novice. She acknowledges that the participants in the “experiment” who used different software came up with similar findings in a common set of data. Schuhmann concludes, “So at the end of the conference, I had an answer to my question how software might improve qualitative research. But meanwhile, many new questions had popped up. Again I was reminded of my experience as a student in physics, where answering one question generally meant raising a couple of new ones, and where one experiment would almost inevitably generate new experiments. So when I left this conference, I was not just convinced of the importance of using software in qualitative research, but also quite certain that this had been the first but definitely not the last experiment in its kind, concerning that fascinating area where computer technology and qualitative research meet.”

Silver, C., & Patashnick, J. (2011, January). Finding fidelity: Advancing audiovisual analysis using software. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 37. http://nbn-resolving.de/urn:nbn:de:0114-fqs1101372

ABSTRACT: Specialised software for the analysis of qualitative data has been in development for the last thirty years. However, its adoption is far from widespread.

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Additionally, qualitative research itself is evolving, from projects that utilised small, text-based data sets to those which involve the collection, management, and analysis of enormous quantities of multimedia data or data of multiple types. Software has struggled to keep up with these changes for several reasons: 1. meeting the needs of researchers is complicated by the lack of documentation and critique by those who are implementing software use and 2. audiovisual data is particularly challenging due to the multidimensionality of data and substantial variety in research project aims and output requirements. This article discusses the history of Computer Assisted Qualitative Data AnalysiS (CAQDAS) as it relates to audiovisual data, and introduces the term "fidelity" as a conceptual mechanism to match software tools and researcher needs. Currently available software tools are examined and areas found lacking are highlighted.

Spickard Prettyman, S., & Jackson, K. (2006). Ethics, technology, and qualitative research: Thinking through the implications of new technologies. Paper presented at Strategies in qualitative research using QSR software. http://www.qual-strategies.org/previous/2006/papers/prettyman/index.html

NOTES: The authors argue that the digitization of audio, video, and photographic data make it possible to create, process, and analyze this data in new and different ways but that the ethical implications regarding the use of this data is often ignored. They recommend that qualitative researchers attend to ethics throughout the process from the conceptualization of questions to reporting the results. Issues such as confidentiality, validity, and rapport may surface and the related ethics must be considered in ways that may be different than in conventional, text-based studies.

Tesch, R. (1990). Qualitative research: analysis types and software tools. Basingstoke: Falmer.

NOTES: A classic text classifying and outlining the characteristics of 20+ QDAS available in the late 1980’s. Alongside a detailed discussion of qualitative methods in a range of fields. Tesch matches different software programs with different research approaches. In the book she points to 46 approaches to analysis, and also summarizes the ten most common principles across qualitative research: 1. Analysis is cyclic – concurrent with data collection 2. Analysis is systematic and comprehensive but not rigid 3. Analysis is reflective and results in analytical notes (memos) that guide the process 4. Data are segmented as we are unable to process large amounts of data all at once but the connection to the whole is maintained 5.Data segments are categorized according to an organizing system that is mostly derived from the data themselves 6. The main intellectual tool is comparison 7. Categories for sorting segments are tentative at the beginning; they remain flexible 8. There is no one ‘right‘ way to manipulate qualitative data during analysis 9. The procedures are neither ‘scientific‘ nor ‘mechanistic‘; qualitative analysis is ‘intellectual craftsmanship‘ (Mills, 1959) 10. The result of the analysis is some type of higher level synthesis. (Tesch, 1990: 95-97)

Wasser, J. D., & Bressler, L. (1996). Working in the interpretive zone: Conceptualizing collaboration in qualitative research teams. Educational Researcher, 25(5), 5-15.

NOTES: The authors take on the myth of the lone researcher and discuss their arts education team research process. They acknowledge that the social nature of learning and the social construction of knowledge has not often been applied to qualitative researchers, themselves, as they engage in their work.

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White, M., Judd, M. D., & Poliandri, S. (2012). Illumination with a dim bulb? What do social scientists learn by employing qualitative data analysis software in the service of multimethod designs. Sociological Methodology, 42, 43–76.

ABSTRACT: Although there has been much optimistic discussion of integrating quantitative and qualitative findings into sociological analysis, there remains a gap regarding the application of mixed approaches. We examine the potential gains and pitfalls of such integration in the context of the growing analytic power of contemporary qualitative data analysis software (QDAS) programs. We illustrate the issues with our own research in a mixed-methods project examining low fertility in Italy, a project that combines analysis of large nationally representative survey data with qualitative indepth interviews with women across four cities in Italy [using NVivo]. Despite the enthusiasm for mixed-methods research, the available software appears to be underutilized. In addition, we suggest that the sociological research community will want to address several conceptual and inferential issues with these approaches.

Wickham M., & Woods M. (2005). Reflecting on the strategic use of CAQDAS to manage and report on the qualitative research process. The Qualitative Report, 10(4), 687-702.

NOTES: A reflection by two doctoral students from the University of Tasmania regarding the use of QDAS for their literature review. They discuss the use of multiple software programs (QDAS, reference managers, word processing tools, etc.) to manage the various demands of producing a literature review for a dissertation and they provide 5 screen shots from the software to demonstrate their use of QDAS. They claim that much of the criticism directed at qualitative research stems from a perception that the process is not always demonstrated to be transparent or rigorous in the same ways as quantitative research. They propose a ‘transparency’ mechanism be attached to all qualitative research processes (from the construction of the literature review to the development of conclusions and recommendations). One such mechanism could be the use of QDAS, in an effort to ensure transparency in research reports and to preemptively address anticipated questions, concerns and issues by readers and reviewers.

Woods, D., & Dempster, P. (2011). Tales from the bleeding edge: The qualitative analysis of complex video data using Transana. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Article 17. http://www.qualitative-research.net/index.php/fqs/article/view/1516/3119

ABSTRACT: This paper explores the analysis of complex multi-media data that highlights the latest advances in the kinds of data that can be analyzed using qualitative analytic software. The data examined consists of multiple simultaneous video streams with different video and audio content. The researchers found that multiple simultaneous transcripts can be useful in making sense of the very fast-moving content of these complex media files. The data capture and analysis techniques described provide the researcher with a tremendous level of access to a huge amount of simultaneous data, but the authors suggest that researchers in the analytic environment described are able to function well in the task of managing, understanding, and analyzing very complex data that captures very complex occurrences in the real world.

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Applications/Methods/Practice

Annechino, R., Antin, T. M. J., & Lee, J. P. (2010). Bridging the qualitative-quantitative software divide. Field Methods, 22(2), 115-124.

ABSTRACT: To compare and combine qualitative and quantitative data collected from respondents in a mixed-methods study, the research team developed a relational database to merge survey responses stored and analyzed in SPSS and semi-structured interview responses stored and analyzed in the qualitative software package ATLAS.ti. The process of developing the database as well as practical considerations for researchers who may wish to use similar methods are explored.

Atherton, A., & Elsmore, P. (2007). Structuring qualitative enquiry in management and organization research: A dialogue on the merits of using software for qualitative data analysis. Qualitative Research in Organizations and Management: An International Journal, 2(1), 62-77, doi: 10.1108/17465640710749117. 

ABSTRACT: Purpose – To explore the cases for and against the use of computer-assisted qualitative data analysis software (CAQDAS) in qualitative organisation and management research.

Design/methodology/approach – Reflecting the debate inherent in the questions raised about the use of CAQDAS, a dialogue between the authors is used.

Findings – There are risks associated with using CAQDAS without considering its underpinning principles and assumptions about data analysis. If these are considered explicitly as part of a research methodology, then CAQDAS may be a valuable analytical tool. If not, there is risk of distortion and bias in results from the use of CAQDAS.

Originality/value – The paper addresses a commonly posed question for qualitative researchers, in a format and structure that is likely to stimulate further debate.

Bazeley, P. (2006). The contribution of computer software to integrating qualitative and quantitative data and analyses. Research in the Schools, 13(1): 64-74. http://www.msera.org/rits_131.htm

ABSTRACT: In published mixed methods studies, qualitative and quantitative approaches have typically been combined by using them side-by-side or sequentially, until the point when the separately generated results are interpreted and conclusions drawn. Integration of different forms of data during analysis, or of different approaches within a single analysis are much less commonly reported. In this paper, integration of these types are shown to be facilitated by use of computer software. Such integration is seen as occurring: (a) when text and numeric data are combined in an analysis; (b) when data is converted from one form to another during analysis; or (c) when combination and conversion occur together iteratively or in generating blended data for further analyses. Examples are provided to illustrate these various, computer-facilitated approaches to mixing methods.

Bazeley, P. (2010). Computer assisted integration of mixed methods data sources and analyses. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods research for the social and behavioral sciences (2nd ed., pp. 431-467). Thousand Oaks, CA: Sage.

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NOTES: Extensive review of possibilities for integration of qualitative, quantitative, social network, and GIS data, the different types of processes and programs available to assist such integration, with methodological implications.

Carvajal, D. (2002, May). The artisan's tool: Critical issues when teaching and learning CAQDAS. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 3(2), Article 14. http://www.qualitative-research.net/index.php/fqs/article/view/853

ABSTRACT: Nowadays we have a wide variety of computer-assisted qualitative data analysis software, CAQDAS, to choose from, and almost every qualitative researcher uses one or two of these programs to analyse his/her data. This demand for CAQDAS has brought not only more sophistication in the newest programs and updates but also the discussion about its methodological implications and the need for more training courses and workshops. A lot has been written about the relation between CAQDAS and qualitative methodology. Nevertheless, the ways the training courses and workshops have been developed and carried out have not been outlined. Who are these courses planned for? Is there any prerequisite that the attendants must fulfil? What must the main goal of these training courses be? This article discusses some facts I have found in my experience as a social researcher and CAQDAS user and trainer in a country where this kind of software is not widespread. The article also focuses on some of the problems that arise when training people in the use of CAQDAS and the consequences the globalisation of training courses and workshops focused on the acquisition of mechanical code-and-retrieve skills have for qualitative methodology. Finally, I propose some critical issues that CAQDAS trainers and qualitative researchers should bear in mind when teaching or learning the use of any qualitative data analysis software.

Darmody, M., & Byrne, D. (2006). An introduction to computerized analysis of qualitative data. Irish Educational Studies, 25(1), 121-133.

ABSTRACT: Over the last two decades there has been an increase in the use of qualitative research, particularly in the human sciences. Such a move has resulted in an increasing number of researchers across disciplines using various types of qualitative software specially designed for managing text and facilitating analysis of qualitative data. However, we feel that limited information is available on the nature and practical use of these programmes in the Irish context. This has led to various misconceptions regarding the use of such programmes. In addition, international literature has highlighted the importance of making the qualitative research process more transparent in terms of describing in detail the analytical procedures applied in qualitative research. This article seeks to open up the debate surrounding qualitative data analysis and provoke discussion about the use of qualitative software packages in educational research. Based on a mixed-methods educational research project involving a substantive qualitative component, this article explores the joy and despair associated with using Qualitative Solutions Research (QSR) Non-numerical Unstructured Data: Indexing, Searching and Theorizing Version 6 (‘N6 software’), and aims to dispel some of the myths that exist around using such software.

Davidson, J., & Jacobs, C. (2008). The implications of qualitative research software for doctoral work: Considering the individual and institutional context. Qualitative Research Journal, 8(2), 72-80.

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ABSTRACT: As qualitative researchers struggle to come to grips with the technological revolution, they are faced with the necessity of learning and teaching qualitative data analysis software in higher education research courses. This change has significant implications for their practice as researchers and teachers. In this article we provide experienced-based recommendations for individual practice (research instructors, dissertation advisers, and doctoral students) and for institutional practice (scaling up for deep integration of qualitative data analysis software). Our recommendations are grounded in hard-earned experience gleaned from many years of working with individuals and institutional contexts to improve the use of qualitative research in higher education.

Fielding, N. & Cisneros-Puebla, C. (2009). CAQDAS-GIS Convergence: Toward a new integrated mixed method research practice? Journal of Mixed Methods Research, 3(4), 349-370. doi: 10.1177/1558689809344973

ABSTRACT: The article explores qualitative geography and qualitative social science as sites of mixed methods research practice. The authors argue that there is an emergent convergence of methodologies and analytical purposes between qualitative geography and qualitative social science. The authors show how methodological and analytical convergence has been enabled by technological convergence between geographical information systems (GIS) and qualitative software (CAQDAS [primarily MAXQDA]). The argument is illustrated by examples of convergent georeferenced mixed methods studies, including a main example from research on reproductive health in Paraguay.

Garcia-Horta, J. B., and Guerra-Ramos, M. T. (2009). The use of CAQDAS in educational research: Some advantages, limitations and potential risks. International Journal of Research & Method in Education, 32(2), 151-165.

ABSTRACT: The use of qualitative analysis software has become extremely popular over the recent years; educational researchers now have a range of tools at their disposal, from the ubiquitous word processor to more specialized computer packages. This paper deals with the use of CAQDAS (computer-assisted qualitative data analysis software) in educational research. We discuss its use in the context of two interview-based pieces of research. The first study explored the effectiveness of a governmental initiative with respect to teacher engagement and commitment, using MAXQDA to examine interviews. The second study looked at teachers’ science-related representations, where NVivo was used to analyse the data. The discussion puts forward the idea that CAQDAS is of great help and can enhance interview data analysis; however, careful and critical assessment of computer packages is encouraged. Their capabilities must not be overestimated, since computers are still unable to perform an independent rational process or substitute the analyst’s capacities.

Hoover R. S., & Koerber A. L. (2011). Using NVivo to answer the challenges of qualitative research in professional communication: Benefits and best practices tutorial. IEEE Transactions on Professional Communication, 54(1), 68-82. 

NOTES: A brief overview of what software choices are available and features of NVivo in particular. Report of experiences with the software discussing how it has benefited efficiency, multiplicity, and transparency; compilation of best practices for using the software.

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Jung, J.-K., & Elwood, S. (2010). Extending the qualitative capabilities of GIS: computer-aided qualitative GIS. Transactions in GIS, 14(1), 63-87. doi: 10.1111/j.1467-9671.2009.01182.x

ABSTRACT: A number of approaches for integrating GIS and qualitative research have emerged in recent years, as part of a resurgence of interest in mixed methods research in geography. These efforts to integrate qualitative data and qualitative analysis techniques complement a longstanding focus in GIScience upon ways of handling qualitative forms of spatial data and reasoning in digital environments, and extend engagements with ‘the qualitative’ in GIScience to include discussions of research methodologies. This article contributes to these emerging qualitative GIS methodologies by describing the structures and functions of ‘computer-aided qualitative GIS’ (CAQ-GIS), an approach for storing and analyzing qualitative, quantitative, and geovisual data in both GIS and computer aided data analysis software. CAQ-GIS uses modified structures from conventional desktop GIS to support storage of qualitative data and analytical codes, together with a parallel coding and analysis process carried out with GIS and a computer-aided data analysis software package. The inductive mixed methods analysis potential of CAQ-GIS is demonstrated with examples from research on children's urban geographies.

Mason, B., & Dicks, B. (2001). Going beyond the code: The production of hypermedia ethnography. Social Science Computer Review, 19(4), 445-457.

ABSTRACT: Discusses how the developments in multimedia and hypermedia technology affected sociological knowledge. Use of electronic media in connecting the gap between experimental ethnography and the fieldwork data; Role of the concept of the nature of social research in hypermedia ethnography; Details on the use of hypermedia for representation.

Rettie, R., Robinson, H., Radke, A., & Ye, X. (2008). CAQDAS: A supplementary tool for qualitative market research, Qualitative Market Research: An International Journal, 11(1), 76-88. doi: 10.1108/13522750810845568.

ABSTRACT: Purpose – The aims of the paper are twofold: to assess the usage of Computer Assisted Qualitative Data Analysis (CAQDAS) in the UK market research industry; and to evaluate the use of CAQDAS as a supplement to paper-coding in market research.

Design/methodology/approach – CAQDAS usage was assessed by a questionnaire, sent to a sample of 400 UK market researchers. The second part of the research is a case study of a research experiment. The authors conducted focus group research into online grocery shopping, supplementing a paper-coding-based analysis with a further analysis based on computer coding.

Findings – Usage of CAQDAS in commercial market research is very low at only 9 percent. The research suggests that CAQDAS can be a useful supplement to traditional methods. Using computer software, the paper was able to mine the data for more detail; clearly identify minority views; and produce a useful resource for future research.

Research limitations/implications – The survey response rate was 38 percent, but only 13 respondents used CAQDAS. Generalisation from a single experiment is problematic; the

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findings are affected by the research topic, research briefs and the two research analysts.

Practical implications – The study has important implications for commercial qualitative market research. Repositioning CAQDAS as supplementary, rather than as an alternative, circumvents arguments about time pressure, and highlights its data management role.

Originality/value – This is the first large-scale survey of qualitative research analysis in the UK market research industry. The case study describes an approach to CAQDAS that is innovative and relevant to commercial market research.

Rich, M., & Patashnick, J. (2002). Narrative research with audiovisual data: Video intervention/prevention assessment (VIA) and NVivo. International Journal of Social Research Methodology, 5(3), 245-226.

ABSTRACT: Video Intervention/Prevention Assessment (VIA) is a qualitative research method that investigates health conditions from the patient’s perspective. VIA’s primary data consist of visual illness narratives, video diaries made by participants of their experiences living with and managing chronic medical conditions. The visual narratives are viewed and listened to by a data logger and notated in sequential scenes by participant identification number, tape number, and video timecode. The content of the audiovisual data is logged as text comprising ‘objective descriptions’ of information that is visible or audible and ‘subjective accounts’ of what is observed, relating the participant’s perspective, emotional tone and psychosocial dynamics of a scene. Within the logs, distinct font headings designate different types of data, so that they can be recognized as such when the logs are imported into NVivo qualitative analysis software for data management, structuring and analysis. Using this software, multiple researchers can code, recode and refine the logs of the visual illness narratives and link their structured analyses to illustrative excerpts from the primary audiovisual data.

Ryan, M. (2009). Making visible the coding process: Using qualitative data software in a post-structural study. Issues in Educational Research 19(2), 142-161.

ABSTRACT: Qualitative research methods require transparency to ensure the 'trustworthiness' of the data analysis. The intricate processes of organising, coding and analysing the data are often rendered invisible in the presentation of the research findings, which requires a 'leap of faith' for the reader. Computer assisted data analysis software can be used to make the research process more transparent, without sacrificing rich, interpretive analysis by the researcher. This article describes in detail how one software package was used in a post-structural study to link and code multiple forms of data to four research questions for fine grained analysis. This description will be useful for researchers seeking to use qualitative data analysis software as an analytic tool.

Seale, Clive F. (2001). Computer-assisted analysis of qualitative interview data. In J. F. Gubrium & J. A. Holstein (eds.), Handbook of interview research: Context and method (pp. 651-670). Thousand Oaks, CA: Sage. 

ABSTRACT (INCOMPLETE): Social researchers have long appreciated the usefulness of computers for data analysis. Statistical software run on increasingly powerful personal computers has automated mathematical calculations on large data sets to the extent that quantitative analysis can be increasingly interactive. Analysts can run procedures and

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get instant feedback on the results, freeing up time for the creative interplay of ideas and research data. In the humanities, the development of software based on various elaborations of string searches for content analysis has led to new conceptions of what is possible in linguistic analysis (Miall 1990). Computer-assisted qualitative data analysis software (CAQDAS) for social research data is a more recent development that, unlike statistical or string-search software, has depended largely on the proliferation of personal computers since the early 1980s. In this chapter, I assess the contribution that CAQDAS can make to a variety of analytic approaches to interview data.

Tagg, C. (2011). Reflecting on the impact of qualitative software on teaching. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research 12(1). Article 27.

ABSTRACT: When teaching people how to use qualitative software, a number of factors influence what functions in the software are covered. This paper will discuss the teaching of short intensive small group courses on qualitative software and identify the features in the software that influence the structure of the course and the material covered. I have taught QSR software (N4, N5, N6, NVivo2, NVivo7 and NVivo8) to small groups of researchers in universities and research organisations for 15 years. Participants have had various levels of research experience including members of faculty with extensive qualitative experience and Masters and first year PhD students. Participants have come from a wide range of disciplines. The design of each course is generally different to meet the needs of the group, their experience, the practical facilities and when known, the requirements of their project. But how significant is the software in the design of a course? This paper will use records of past courses to identify the elements taught and the reasons for the selection of topics and ordering. The paper will compare the impact of the software user interface, structure, and features by comparing the teaching of similar courses in N6 and NVivo7. The paper will conclude by reflecting on the impact of course design on the use that participants make of qualitative software.

Xiao, H., & Mair, H. (2006). A paradox of images. Journal of Travel & Tourism Marketing, 20(2), 1-14. doi: 10.1300/J073v20n02_01

ABSTRACT: This paper analyzes the image(s) of China as a tourist destination through the representational narratives of major English newspapers. Feature travel accounts—thirty-five articles from twenty sources—were used as information-rich discourse to explore the portrayal of the destination's image. Data were obtained through a focused search from LexisNexis Academic—one of the largest databases of international newspapers, and coded through Nudist Nvivo for an inductive analysis. It was found that a paradox of images has emerged from the contrasting perceptions of the changing versus the unchanged in the representational dynamics. The finding of paradoxical images provides an alternative to the interpretation of representational frames or patterns prevalent in the academic discourse with regard to the portrayal of culturally different tourist destinations. Implications, limitations of this analysis and future research issues are also discussed.

Guidelines for and experiences in using NUD*IST or NVivo

Bazeley, P. (2007). Qualitative data analysis with NVivo. Thousand Oaks, CA: Sage.

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NOTES: Replaced by second edition (Bazeley & Jackson, 2013). The first edition had a more extensive review of links between software, methods, and methodology, especially in Chapter 8.

Beekhuyzen, J. (2008). Conducting a literature review: A puzzling task. Paper presented at the AARE 2008 International Education Research Conference, Brisbane, Australia. http://www.aare.edu.au/08pap/bee08127.pdf

ABSTRACT (PARTIAL): A literature review is much like a jigsaw puzzle; piecing together the seemingly endless pieces of published research and other sources, and telling a story with the finished ‘picture’. There is a lack of available practical information on how to conduct a literature review, and there is even less available that use qualitative research software to support the process. To address this gap, this paper discusses the journey of an information systems PhD research student using Nvivo for a literature review. In this paper Nvivo8 is proposed as a tool to help any researcher accomplish a rigorous and transparent literature review. Here a practical example of such a process is presented in seven steps, using a well-known qualitative research software that gives the researchers new opportunities to explore and piece together the challenging task of a literature review.

Bringer, J. D., Johnston, L., & Brackenridge, C. H. (2004). Maximizing transparency in a doctoral thesis: The complexities of writing about the use of QSR*NVIVO within a grounded theory study. Qualitative Research, 4(2), 247-265.

NOTES: A reflection on a grounded theory dissertation with the goal of demonstrating the way NVivo was used to analyze the data and to foster a transparent account of the analysis process. The authors argue for the importance of transparency for qualitative research, discuss writing to achieve transparency (including an awareness of the audience one is writing for), and recommend an electronic audit trail (primarily through the use of a research journal that traces the timing and content of the literature review and coding). The article makes use of eight screen-shots taken directly from the software to demonstrate the ability of the software to help create an audit trail.

Bringer, J. D., Johnston, L. H., & Brackenridge, C. H. (2006). Using computer-assisted qualitative data analysis software to develop a grounded theory project. Field Methods, 18(3), 245-266.

ABSTRACT: The promise of theory and model development makes grounded theory an attractive methodology to follow. However, it has been argued that many researchers fall short and provide a detailed description of only the research area or simply a quantitative content analysis rather than an explanatory model. This article illustrates how the researchers used a computer-assisted qualitative data analysis software program (CAQDAS) as a tool for moving beyond a thick description of swimming coaches’ perceptions of sexual relationships in sport to an explanatory model grounded in the data. Grounded theory is an iterative process whereby the researchers move between data collection and analysis, writing memos, coding, and creating models. The nonlinear design of the selected CAQDAS program, NVIVO, facilitates such iterative approaches. Although the examples provided in this project focus on NVIVO, the concepts presented here could be applied to the use of other CAQDAS programs. Examples are provided of how the grounded theory techniques of open coding, writing memos, axial coding, and creating models were conducted within the program.

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Hutchinson, A. J., Johnston, L. H., & Breckon, J. D. (2010). Using QSR-NVivo to facilitate the development of a grounded theory project: an account of a worked example. International Journal of Social Research Methodology, 13(4), 283-302. doi: 10.1080/13645570902996301

ABSTRACT: This paper demonstrates how the software package QSR-NVivo can be used to facilitate a grounded theory approach. Recent research evidence has questioned the methodological quality of many studies that claim to utilise grounded theory. This paper argues that qualitative data analysis software can be used to encourage good quality grounded theory research by facilitating many of the key processes and characteristics associated with this approach. To achieve this, the paper identifies a number of grounded theory characteristics, common to all revisions of the methodology. It then describes the development of a recent study, which examined how people successfully maintain long-term physical activity behaviour change. The purpose of this is to demonstrate how different functions of QSR-NVivo may be used in conjunction with the key grounded theory characteristics. In summary, QSR-NVivo is a powerful tool that, if used appropriately, can facilitate many aspects of the grounded theory process from the design and early sampling procedures, through to the analysis of data, theoretical development and presentation of findings.

Johnston, L. (2006). Software and method: Reflections on teaching and using QSR NVivo in doctoral research. International Journal of Social Research Methodology, 9(5), 379-391. doi: 10.1080/13645570600659433

NOTES: A reflection from eleven years of experience using and teaching NUD*IST and NVivo to doctoral students. Among the claims made: 1) Instead of blaming QDA software for promoting mechanistic, problematic or sloppy coding, scholars should consider whether the transparency afforded by software is simply highlighting a problem that has always existed but was difficult to detect. 2) QDAS provides potential for unprecedented levels of transparency, although the potential has not been realized in practice for two main reasons. First, sample data accompanying the software may foster inappropriate strategies among novices who fail to customize their use of tools to fit their analytical goals. Next, when instructing novices, teachers often fail to A) discuss the importance of a research journal, B) clarify the problem of redundant nodes in the coding structure, C) point to tools in the software that can help research achieve analytical distance (to see patterns in the data).

Leech, N. L., & Onwuegbuzie, A. J. (2011). Beyond constant comparison qualitative data analysis: Using NVivo. School Psychology Quarterly, 26(1), 70-84. doi: 10.1037/a0022711

ABSTRACT: The purposes of this paper are to outline seven types of qualitative data analysis techniques, to present step-by-step guidance for conducting these analyses via a computer-assisted qualitative data analysis software program (i.e., NVivo9), and to present screenshots of the data analysis process. Specifically, the following seven analyses are presented: constant comparison analysis, classical content analysis, keyword- in-context, word count, domain analysis, taxonomic analysis, and componential analysis. It is our hope that providing a clear step-by-step process for conducting these analyses with NVivo9 will assist school psychology researchers in increasing the rigor of their qualitative data analysis procedures.

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Li, S., & Seale, C. (2007). Learning to do qualitative data analysis: An observational study of doctoral work. Qualitative Health Research, 17(10), 1442-1452. 

ABSTRACT: Using examples from written assignments and supervisory dialogues, the authors report a longitudinal observational case study of a doctoral research project, focusing on the teaching and learning of qualitative data analysis on a project that involved coding and analysis of nursing talk. Written drafts contain concrete exemplars illustrating the problems and solutions discussed in supervisions. Early problems include the difficulty of knowing where to start with coding, ambiguities in the definition of codes, inaccurate reporting and recording of data, failure to distinguish researcher and actor categories, and overinterpretation of evidence. Solutions to these problems required their accurate identification, communication of practical solutions, and care in the interactional management of delivery and receipt of feedback. This detailed analysis informs readers of sources of validity, rigor, and, eventually, creativity in carrying out a social research project. It also assists in explicating an apprenticeship model for the learning of research skills.

Morison, M., & Moir, J. (1998). The role of computer software in the analysis of qualitative data: efficient clerk, research assistant or Trojan horse? Journal of Advanced Nursing, 28: 106–116. doi: 10.1046/j.1365-2648.1998.00768.x

ABSTRACT: In the last 15 years there has been a proliferation of computer software packages designed to facilitate qualitative data analysis. The programs can be classified, according to function, into a number of broad categories such as: text retrieval; text base management; coding and retrieval; code-based theory building; and conceptual-network building. The programs vary enormously in the extent to which they can facilitate the diverse analytical processes involved. The decision to use computer software to aid analysis in a particular project may be influenced by a number of factors, such as the nature of the data and the researcher’s preferred approach to data analysis which will have as its basis certain epistemological and ontological assumptions. This paper illustrates the way in which a package called NUD.IST facilitated analysis where grounded theory methods of data analysis were also extensively used. While highlighting the many benefits that ensued, the paper illustrates the limitations of such programs. The purpose of this paper is to encourage researchers contemplating the use of computer software to consider carefully the possible consequences of their decision and to be aware that the use of such programs can alter the nature of the analytical process in unexpected and perhaps unwanted ways. The role of the Computer Assisted Qualitative Data Analysis (CAQDAS) Networking Project, in providing up-to-date information and support for researchers contemplating the use of software, is discussed.

Ozkan, B.C. (2004). Using NVivo to analyze qualitative classroom data on constructivist learning environments. The Qualitative Report, 9(4), 589-603.

ABSTRACT: This article describes how a qualitative data analysis package, NVivo, was used in a study of authentic and constructivist learning and teaching in the classroom. The paper starts with a summary of the research study in which NVivo was used to analyze the data and overviews the methodology that was adopted in this study. It, then, describes how NVivo was used in the analysis of observational (video) data, interviews and field notes. Key Words: Computer Based Qualitative Data Analysis, Qualitative Data Analysis, Computer Based Data Analysis, NVivo, and Constructivist Learning Environments.

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Richards, L. (2009). Handling qualitative data. London: Sage Publications, Inc.

PUBLISHER NOTES: This Second Edition of Lyn Richards' best-selling book provides an accessible introduction to qualitative research for students and practitioners. Recognizing that for many new researchers dealing with data is the main point of departure, this book helps them to acquire a progressive understanding of the skills and methodological issues that are central to qualitative research. Lyn Richards provides clear and pragmatic guidance on how to handle, reflect on and get results from small amounts of data, while at the same time showing how a consideration of methods and their philosophical underpinnings informs how we should best handle our data. This book also covers all the processes of making, meeting, sorting, coding, documenting and exploring qualitative data, smoothly integrating software use and the discussion of the main challenges that readers are likely to encounter. It guides novice researchers to achieve valid and useful outcomes from qualitative analysis, and to ensure they do justice to their data

Robertson, S. P. (2008). The qualitative research process as a journey: Mapping your course with qualitative research software. Qualitative Research Journal, 8(2), 81-90. doi: 10.3316/QRJ0802081

ABSTRACT: This paper examines two planning tools incorporating the use of a computer-assisted qualitative data analysis software package as applied to an actual research study. The first is the ‘NVivo Shell’. In creating a shell, the researcher is prompted to consider the role qualitative research software will take within the context of the project as a whole. Further, it acts as an initial framework to contemplate how the collected data might be organised and provides a way to organise it from the very beginning. The second tool is visual modelling. At a basic level, it can literally provide a map of the research process, which can be used to chart the progress of the research. With ever-increasing complexity, it can be a way to visually represent a variety of ideas, concepts, sources, or beliefs and explore existing or potential relationships between and among them. The study used to contextualise their application is a doctoral dissertation (Robertson, 2007).

Ryan, Mary. (2009). Making visible the coding process: Using qualitative data software in a post-structural study. Issues in Educational Research, 19(2), 142-161. 

NOTES: The author claims that qualitative research methods require transparency to ensure the 'trustworthiness' of the data analysis. She reflects on post-structural study about the capacity of socially critical pedagogical and curriculum approaches in schools to be genuinely transformative. Like many other advocates of QDAS, the author claims that this genre of software provides considerable potential to bring transparency to the research process. She uses four screen captures from the NVivo modeling tool and three other snapshots of data (transcripts and drawings).

Siccama, C. J., & Penna, S. (2008). Enhancing validity of a qualitative dissertation research study by using NVIVO. Qualitative Research Journal, 8(2), 91-103. doi: 10.3316/QRJ0802091

NOTES: This paper shares how Qualitative Data Analysis Software (QDAS), specifically NVIVO, was used in conducting a qualitative dissertation research study and how the software played a powerful role in coding data and addressing validity threats. The

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authors describe five strategies for extending validity by using NVIVO. Specific examples are shared from a doctoral student who utilised NVIVO qualitative research software while conducting a qualitative dissertation.

Sin, C. H. (2007). Using software to open the ‘black box’ of qualitative data analysis in evaluations: The experience of a multi-site team using NUD*IST Version 6. Evaluation, 13(1), 110-120.

NOTES: The author reflects on a program evaluation of a large-scale multi-component community and neighborhood renewal initiative in Britain, commissioned by a government department. He argues that QDAS can facilitate a transparent account of research, which is often missing in the reporting of qualitative research findings. QDAS can demonstrate the way “data are manipulated, managed and represented is part and parcel of the social construction of evidence used in evaluation.” (p 116) The article discusses the role of QDAS in team research, primarily regarding the process of coding.

Wiltshier, F. (2011, January). Researching with NVivo 8. Forum Qualitative Sozialforschung /Forum: Qualitative Social Research, 12(1), Article 23. http://nbnresolving.de/urn:nbn:de:0114-fqs1101234

ABSTRACT: This paper is based on a project created for the KWALON conference in April, 2010. Due to time constraints and the lack of research experience of some of the people working on the project, we took a team based approach to develop the analysis, and focused on descriptive coding. This paper addresses both the items for reflection in the "instructions for developers" and the research question posed. Though the analysis of the data was done using software which enabled more to be done than would otherwise have been the case, it is contended that the results obtained are influenced by the researchers themselves, and factors such as the time available, rather than the software itself.

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