QDA Data Analysis Services
We have Decades of Experience in Qualitative Data Analysis
In addition to being involved in projects on a consultancy basis we are also often contracted to undertake data analysis for clients who may not have the time or expertise to do so in-house. It may be that the methodology and analytic strategy is already defined in which case we can take the project over and undertake the analysis. Alternatively we can be involved in designing analytic plans as well as implementing them within the chosen software. Together with our QDAS colleagues we have many decades experience of qualitative data analysis and undertake high-quality analysis within the parameters of your needs. Details and references are available on request.
Christina's sessions were excellent and very well received. Feedback demonstrates that the students and staff who attended the sessions really benefited from and appreciated them. Thank you, once again, for preparing and delivering, what was clearly, a very successful package of training.Rachel Torr, PhD
Head of Researcher Development University of Exeter Doctoral College
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.
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.
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.
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.
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.