QDA Data Analysis Services

We have Decades of Experience in Qualitative Data Analysis

QDA Data Analysis Services

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.

Testimonials

The advanced NVivo workshop was so useful. I now have the skills to enable me to use NVivo much more powerfully. Christina is a brilliant trainer and she adapted the course to suit the requirements of those that attended. Christina also gave everyone individual help with their projects. I could not recommend Christina's course highly enough, or emphasise more the value of this course for researchers working with qualitative data.
Caroline Andow, Postgraduate Researcher
University of Southampton

Blog

Coding is a process, not an event

Coding is a process, not an event
By Christina Silver on Nov 21, 2017 at 06:04 PM in CAQDAS commentary

What lies behind the red flag question: “I’ve done all my coding – now what?” In my last blogpost I considered the first likely culprit: starting to code before thinking through its purpose. But thinking about the purpose isn’t enough. A second issue is the need to think about coding as an on-going process – not as a single event that gets “done” before moving on to the next event. Coding is the opportunity to repeatedly connect with our data.

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"OK I've done all my coding. What's next?" Err, didn't you plan that already?

By Christina Silver on Nov 07, 2017 at 10:47 AM in CAQDAS commentary

Yet again this week I was asked the red flag question in a CAQDAS workshop: “Coding’s done. Now what?” This flags the inappropriate use of CAQDAS: no analytic planning done before plunging into helter-skelter coding. In this post and the next I’ll deal with the two underlying problems: starting to code without thinking about its purpose, and thinking of coding as an event rather than a process. Taken together these can result in a mass of codes that don’t lead to a thoughtful response to the research question. First: how to think about the purpose of coding.

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Translation in Five-Level QDA: What's in a name? Actually, quite a lot

Translation in Five-Level QDA: What's in a name? Actually, quite a lot
By Nicholas Woolf on Jul 07, 2017 at 07:00 PM in Five-Level QDA in practice

“Translation” is the key concept in our Five-Level QDA method, so it’s important to know what it means. The word just showed up in the title of Susanne Friese’s blog post on the ATLAS.ti website – “Translating the process of open/initial coding in Grounded Theory” – and Susanne ended by inviting readers “to read more about this process of translation” in our textbooks on the Five-Level QDA method coming from Routledge in September. But as Susanne uses the word “translation” in a very different way from us we want to clear it up right away.

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Don't lose your analytic reflections: The value of writing spaces in CAQDAS packages

Don't lose your analytic reflections: The value of writing spaces in CAQDAS packages
By Christina Silver on Jun 17, 2017 at 09:30 AM in CAQDAS commentary

Writing spaces are one of the most valuable features of dedicated CAQDAS packages. But I often see projects that make little use of them. Here’s why they are so potentially powerful.

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Harnessing NVivo Classifications: it's all about units

Harnessing NVivo Classifications: it's all about units
By Christina Silver on May 29, 2017 at 06:01 AM in NVivo Learning, Five-Level QDA in practice

Kath McNiff’'s post on the NVivo Blog about classifying data in NVivo has prompted me to get writing about how I deal with this teaching challenge. For me, teaching students to choose between the available tools for classifying data and how to harness them appropriately revolves around units.

For years I've experimented with different ways of teaching how to harness the NVivo tools for classifying factual characteristics of data and respondents - for example the socio-demographics of participants or the metadata about documentary evidence. One of the great things about NVivo is that it offers several different ways of doing this, making it a very flexible tool.

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