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.

Five Level QDA is a trademark owned by Christina Teal and Nicholas H. Woolf, registered as European Community Trademark Registration Number 015596976, and United States Trademark Serial Number 87080134

QDA = Qualitative Data Analysis

CAQDAS = Computer Assisted Qualitative Data Analysis


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Harnessing CAQDAS 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.


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.


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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