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.
Nick Woolf acted as a consultant to a major study on Racial and Ethnic Fairness in the Legal System of the State of Utah... Nick's analyses were instrumental in drawing conclusions and allowing us to make constructive recommendations... his timeliness, responsiveness, and research excellence made it a true pleasure to work with him.Jennifer M. J. Yim, Director,
Utah Task Force on Racial and Ethnic Fairness in the Legal System
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.