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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.

"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.

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.

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.

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.

As well as our own Five-Level QDA blog Nick and Christina have contributed to other blogs, newsletters and magazines on aspects to do with the use of CAQDAS packages. In addition, there are posts and resources written by others which are related to our work that we think researchers using different CAQDAS packages may find useful. Here we collate these resources for easy access. We will update this post as additional resources come to our attention relate to Five-Level QDA. If you know of something that should be listed here, please let us know.