Project Consulting

in CAQDAS Packages

Project Consulting in CAQDAS Packages

Our involvement in team and funded research projects varies greatly from project to project. For some projects we serve only as outside consultants. This typically involves participating in the initial design of the data analysis and the consequent set up of the project in the chosen software, initial training of team members in the needed software skills, and then continuing on an as needed basis to provide guidance and advice at turning points of the data analysis, and provide additional software training and coaching on an as-needed basis.

For other projects we are more involved as a team member, participating in or leading the data analysis, and training and coaching the researchers in whatever features of the program they need to use. With some projects we undertake the entire data analysis ourselves, and participate in the writing of the resulting papers. We have been involved in a range of projects, from evaluations to grounded theory studies, including studies in  family medicine, public health, education, and leadership and management studies.

Using CAQDAS packages for collaborative data analysis

Using CAQDAS packages for collaborative data analysis is rewarding if the procedures are well-designed, but problematic if approached in the same manner as lone research. We are experienced in consulting to collaborative analysis projects, having served as project managers or principal data analysts in many regional, national, and international qualitative and evaluation studies in diverse areas of the human sciences. Details and references are available on request.

Testimonials

So often I have attended training courses and wondered what I was doing there, feeling my life slip away into oblivion while I could be doing other more useful and interesting activities such as watching iron rust or paint flake. However, Christina's was superb. It was exactly what I had hoped for. Pitched at precisely the right level and presented in a very clear, engaging way. Being able to spend time having some real content and context given by someone who really understands the software and researcher's needs, along with a sensible amount of time to try things out and get help was great. I'm much more confident about getting to grips with my data now.
Paul Rause
Interdisciplinary researcher, Southampton University

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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