The FIVE LEVEL QDA Method

for harnessing CAQDAS packages powerfully

Developed by Nicholas Woolf and Christina Silver

A method for harnessing CAQDAS packages powerfully, independent of methodology, software package, or mode of learning. It is not a new method of data analysis but a way of making clear what CAQDAS experts unconsciously already do.

QDA = Qualitative Data Analysis

CAQDAS = Computer Assisted Qualitative Data Analysis

                Faculty

QDA Faculty

              Students

QDA Students

            Researchers

QDA Researchers

Harnessing CAQDAS Blog

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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