Instructors

Faculty

Whichever CAQDAS packages you are teaching, and whether you are doing so in a workshop or semester-based format, Five-Level QDA can help you teach your students to plan their analysis, choose their software tools appropriately, and shorten the time to complete their studies.

Many teachers are uncertain when and how to introduce technology into research methods courses. Five-Level QDA is a method that can be used to ensure that learners fully appreciate that their methodology drives the analytic process, and not the capabilities of the software. It provides a process to teach the identification of analytic tasks and to translate them into appropriate software operations. The method is adaptable to different teaching modes, and has been successfully used in workshops, semester-based and on-line courses. In addition it can be implemented according to the preferred sequence of instruction, whether methods followed by technology, or methods via technology.

Testimonials

The 3 day NVivo course was based around Five-Level QDA and was an excellent balance between the technicalities of using analytical software and its real-world use as a research tool. Christina grounded the training in how to conduct high quality post-graduate research as a means to enhance good research practice, allow effective data analysis, and support research project management. This is far preferable to an approach focusing largely or wholly on the technical details of software, with little relation to conducing actual academic research. Although the 3 days was very intensive it comes highly recommended.
Simon Cox
PhD Student, Nottingham Business School, Chief Officer, NHS Scarborough and Ryedale Clinical Commissioning Group

Blog

No 'basic' or 'advanced' CAQDAS features

No 'basic' or 'advanced' CAQDAS features
By Christina Silver on May 13, 2017 at 09:25 AM in CAQDAS commentary

This blog post is a response to Steve Wright’s reaction to a post I made on Twitter: “There are no basic or advanced #CAQDAS features, but straightforward or more sophisticated uses of tools appropriate for different tasks”

Thanks Steve for starting this conversation – it’s really important to debate these issues, and fun too! The sentiment behind the Twitter post underlie the Five-Level QDA® method that Nick Woolf and I have developed. Our forthcoming series of books explain our position, so here I briefly respond to Steve’s comments.

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The Five-Level QDA method books are in production

The Five-Level QDA method books are in production
By Christina Silver on Mar 05, 2017 at 08:04 PM

We're really excited to have submitted to Routledge our manuscripts for three books on the Five-Level QDA method - one each for ATLAS.ti, MAXQDA and NVivo. Nick developed the theory and when we met in 2013 we realized that we had both come to very similar conclusions about the issues involved in teaching and learning to harness CAQDAS packages powerfully. We've since been working together to refine, test and write-up the method.

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Don't blame the tools: researchers de-contextualise data, not CAQDAS

Don't blame the tools: researchers de-contextualise data, not CAQDAS
By Christina Silver on Jan 07, 2017 at 06:26 PM in CAQDAS commentary

In an earlier post on CAQDAS critics and advocates I promised to provide evidence for my position that CAQDAS packages are not distancing, de-contextualising, and homogenising, as is sometimes claimed. I have already argued that CAQDAS packages actually bring us closer to our data, and given an illustration of how this can happen, so here I consider the de-contextualizing issue.

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An illustration of how CAQDAS tools can bring us closer to data

An illustration of how CAQDAS tools can bring us closer to data
By Christina Silver on Nov 14, 2016 at 10:49 AM in CAQDAS commentary

In my previous post I argued that using dedicated CAQDAS packages for analysis could bring us closer to our data, rather than distance us from it, as some critics suggest. Here I illustrate this by outlining how different CAQDAS tools can be used in to fulfil a specific analytic task, thus bringing us closer to data.

Let's imagine we are doing a project in which we need to generate an interpretation that is data-driven rather than theory-driven. It could involve one of a number of analytic methods, for example, inductive thematic analysis, narrative analysis, grounded theory analysis, interpretive phenomenological analysis'. Whatever the strategy, an early analytic task may be to familiarize with the transcripts in order identify potential concepts. There are several different ways we could go about fulfilling this analytic task using dedicated CAQDAS packages. Here I discuss three.

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Does CAQDAS distance us or bring us closer to our data?

Does CAQDAS distance us or bring us closer to our data?
By Christina Silver on Oct 01, 2016 at 09:29 AM in CAQDAS commentary

In an earlier post on CAQDAS critics and advocates I promised to provide evidence for my position that CAQDAS packages are not distancing, de-contextualising, and homogenising, as is sometimes claimed. So I'm starting a series of posts. First I'm taking the suggestion that the use of CAQDAS distances us from our qualitative data and illustrate why I believe the converse to be true. Here I outline my position, and I'll illustrate my argument with examples in subsequent posts.

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