Whichever CAQDAS packages you are teaching, and whether you are doing so in a workshop or semester-based format, Five-Level QDA can help you teach your students to plan their analysis, choose their software tools appropriately, and shorten the time to complete their studies.
Many teachers are uncertain when and how to introduce technology into research methods courses. Five-Level QDA is a method that can be used to ensure that learners fully appreciate that their methodology drives the analytic process, and not the capabilities of the software. It provides a process to teach the identification of analytic tasks and to translate them into appropriate software operations. The method is adaptable to different teaching modes, and has been successfully used in workshops, semester-based and on-line courses. In addition it can be implemented according to the preferred sequence of instruction, whether methods followed by technology, or methods via technology.
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
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.