As well as our own Five-Level QDA blog Nick and Christina have contributed to other blogs, newsletters and magazines on aspects to do with the use of CAQDAS packages. In addition, there are posts and resources written by others which are related to our work that we think researchers using different CAQDAS packages may find useful. Here we collate these resources for easy access. We will update this post as additional resources come to our attention relate to Five-Level QDA. If you know of something that should be listed here, please let us know.
Note that these are not articles published in academic journals, for links to those publications, see our publications page.
The ATLAS.ti Research Blog, by Nick Woolf, July 2015
The purpose of this article is to present an alternative way to use comments and memos in ATLAS.ti, as an example of using the program creatively. Every research project has its own needs according to its methodology and objectives, and every researcher has their own preferences, and so it is helpful to know that the program offers great flexibility in how it can be harnessed.
The ATLAS.ti Research Blog, by Nick Woolf, March 2014
Most qualitative methodologies involve coding as a central activity, and coding is also a central software feature of ATLAS.ti. So they would seem to be the same activity. But they are not. When using ATLAS.ti, “coding” is always the same mechanical process. It involves creating code names in the CODE MANAGER, and linking the CODES to stretches of text.
But when thinking about the data analysis process, “coding” can involve very varied mental activities. In a content analysis it might involve counting the number of times various topics are mentioned. In more interpretive methodologies it might involve creating codes to subtly conceptualize the meaning of a participant’s experience, leading to a quite different kind of coding scheme from a content analysis. It would be better if the mental coding activity of the research process and the mechanical coding activity of operating ATLAS.ti had different names. A good name for what is done mentally in qualitative research would be “conceptualizing data”, and a good name for its mechanical equivalent when using the software would be “coding data”. Usually these activities are the same, with each concept in a research project getting its own CODE in ATLAS.ti. But not always, because a CODE in ATLAS.ti is not always the best way to represent a concept. Here is an example of when it is not.
The NVivo blog, by Christina Silver, June 2016
Christina Silver manages the CAQDAS Networking Project at the University of Surrey and is a co-author of Using Software in Qualitative Research (Lewins and Silver). She is also an authorised trainer of NVivo software. Here Christina explains the importance of letting strategies (not tactics) guide the use of NVivo.
For the past few years Nick Woolf and I have been unpacking the way that expert users of CAQDAS packages like NVivo harness these programs powerfully, so we can better help new researchers learn these skills without years of trial and error. The result is Five-Level QDA, a pedagogy that spans methodologies, software programs and teaching modes (Silver & Woolf, 2015) which we presented in the Digital Tools stream at the 12th International Congress of Qualitative Inquiry and which we will be discussing at the forthcoming Kwalon conference which will reflect on the future of QDA software, in Rotterdam, the Netherlands, 25-26 August. Here I’m sharing some of the reasons why Five-Level QDA workshops delay teaching how to operate the software until part way through, something that is both uncommon and unexpected.
The DMO project is an ESRC funded collaboration between the universities of Southampton, Birmingham and Surrey and the Mass Observation Archive (January 2015 – August 2016). Christina is a Co-Investigator on the DMO project. The overall aim was to combine and explore unexploited quantitative and qualitative data from writers contributing to the Mass Observation Project (MOP). Christina and Nick are using DMO as one of our Case Illustrations in the MAXQDA Five-Level QDA book, to be published in 2017. The DMO project has a blog where there are various posts about different aspects of the project. The ones that relate specifically to the use of MAXQDA and the principles of Five-Level QDA are listed here.
DMO Blog, by Christina Silver, April 2015
In this blogpost I’m sharing the process we went through in choosing to use the software package MAXQDA to analyse the writings of volunteers in relation to the MOP Directives “Social Divisions” and “Life Lines”. I hope this will help others make their own informed choice between the range of software available.
DMO Blog, by Christina Silver, November 2015
In her recent post, Rose commented on the variety in the responses to the 2008 Your Life Line Directive. This variety has shaped the way we are approaching the qualitative analysis of this and the 1990 Social Divisions Directives. So I thought I’d outline our analytic design and share how we are implementing it within MAXQDA (see here for an explanation of our choice of CAQDAS package). I’m doing that in a series of 4 posts, this is the first, check back over the next few weeks and months as our analysis proceeds for the next 3 posts, which detail the way we are going about each analytic phase.
Framed by the projects’ overall objectives, research questions and methodology, our analytic design evolved out of a pilot analysis phase when different approaches were trialled on a sub-sample of narratives from both Directives.
The resulting design involves three phases: i) high-level mapping of semantic content, ii) thematic prioritisation, and iii) in-depth latent thematic analysis.
DMO Blog, by Christina Silver, January 2016
Following on from discussion the factors informing the design of or analysis, I promised to outline each phase of our analytic plan. Here’s the first.
First, though, it’s useful to illustrate the analytic plan in its entirety, because in undertaking any phase of analysis it is always crucial to build on what has gone before, and anticipate what will happen as a result. That’s what makes analysis focused.
The diagram below shows the four phases of analytic plan as it currently stands. You’ll notice that in my last blog post I said our plan had three phases. Since then we’ve got further into the analysis and now are thinking about the phases slightly differently. That’s the nature of qualitative research design, it develops as the project proceeds.
DMO Blog, by Christina Silver, March 2016
The processes of undertaking stage 1 of the analysis of Mass Observation writers’ responses to the Life Lines directive leads me to reflect on something I often say to participants of my analysis courses – the various ‘moments of contact’ we have with our data during qualitative analysis.
Revisiting data at different times, from different perspectives and for different purposes is characteristic of the iterative nature of qualitative analysis, and must be prized as essential in the development of a valid interpretation. Transcription is a key ‘moment of contact’ that shouldn’t be undervalued.
Social Research Association – Research: Matters, by Christina Silver, December 2015
Computer Assisted Qualitative Data AnalysiS (CAQDAS) packages have been around for more than 25 years but many researchers still undertake qualitative data analysis manually, using highlighter pens and scissors or non-bespoke software, such as MS Word, Excel, Onenote and so on. Critics of CAQDAS rightly stress that top-quality analysis can be done manually, and that using CAQDAS does not guarantee robust results. So what are the benefits of using bespoke CAQDAS? For me, they fall into three categories: project management; analytic affordance; and illustrating quality.
REVY, Vol 39(1), by Christina Silver, January 2016
Software designed to facilitate qualitative and mixed methods research is collectively known as CAQDAS (Computer Assisted Qualitative Data AnalysiS). Available since the 1980s it provides tools designed to support a range of analytic activities across disciplines and sectors. Using references from bibliographic software (e.g. Endnote, Zotero, Mendeley) enables systematic literature reviewing, integrated with data analysis. This article outlines some possibilities, highlighting considerations in planning a literature review for librarians and others.
Post on the Quirkos Blog, by Daniel Turner, July 2015
I’m increasingly seeing qualitative research software as being the middle piece of a jigsaw puzzle that has three stages: collection, coding/exploring, and communication. These steps are not always clear cut, and generally there should be a fluid link between them. But the process, and enacting of these steps is often quite distinct, and the more I think about the ‘typical’ workflow for qualitative analysis, the more I see these stages, and most critically, a need to be flexible, and allow people different ways of working.
At any stage it’s important to choose the best tools (and approach) for the job. For qualitative analysis, people have so many different approaches and needs, that it’s impossible to impose a ‘one-size-fits-all’ approach. Some people might be working with multimedia sources, have anything from 3 to 300 sources, and be using various different methodological and conceptual approaches. On top of all this are the more mundane, but important practical limitations, such as time, familiarity with computers, and what software packages their institution makes available to them.
But my contention is that the best way to go about facilitating a workflow is not to be a jack-of-all trades, but a master of one. For CAQDAS (Computer Assisted Qualitative Data AnalysiS) software, it should focus on what it does best: aiding the analysis process, and realise that it has to co-exist with many other software packages.
Post on the Quirkos Blog, by Daniel Turner, July 2015
I briefly mentioned in my last blog post an interesting new article by Silver and Woolf (2015) on teaching QDA (Qualitative Data Analysis) and CAQDAS (Computer Assisted Qualitative Data AnalysiS). It’s a great article, not only because it draws from more than 20 years combined pedagogical experience, but suggests a new way to guide students through using software for qualitative analysis.
The basis of the strategy is the ‘Five-Level QDA’ approach, which essentially aims to get students to stop and think about how they want to do their qualitative analysis before they dive head-first into learning a particular CAQDAS package. Users are guided through a five-step tool that I would paraphrase as:
- Stating the analysis/research objectives
- Devising an analytic plan
- Identifying matches between the plan and available tools
- Selecting which operations to do in which tools
- Creating a workflow of tools and software to meet all the aims above
For more detail, it’s worth checking out the full article which includes example worksheets, and there is also a textbook due out covering the approach in more depth. It’s also interesting to see how they describe the development of their pedagogical approach in the last decade or so.
Post on Christina Schmieder’s Blog: Qualitative Methods and Software, August 2015
After looking at my curriculum on question generation using QDA software, Daniel Turner (founder & director of the QDA software company Quirkos) pointed me to a recent article on CAQDAS pedagogy by Silver & Woolf. I was happy to see that the discussion around QDA software and teaching is picking up – and I think the article is a must-read for anyone reflecting on their own teaching strategies and teaching experiences.
Daniel asked me whether I think that my curriculum is complementary to Silver and Woolf’s approach. I think it is – so I jotted down a few thoughts.
Nick Woolf is the best instructor I have had for learning a software application....Nick's highly interactive teaching style significantly increased my success...I returned to work with renewed insight and ideas about where my research was leading. Nick presented a remarkably effective class for both new and experienced researchers.Mary F. Annese, MPA, Research Specialist
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