A Qualitative Computing Revolution?

The challenges of data management and analysis on a large longitudinal qualitative research project Computer aided qualitative data analysis has the potential to revolutionise both the scale of research and possible analysis techniques. Yet, the software itself still imposes limits that hinder and prevent this full potential from being realised. This post looks at the large and complex dataset created as part of the Welfare Conditionality research project, the analytical …

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How to use a Word macro to fix interview transcripts for auto-coding in NVivo

Within NVivo, and likely other QDAS packages as well, it is possible to use the structure of interview transcripts for auto-coding. Basically, what auto-coding does is go through the transcript and using criteria specified by the user assigns text to chosen nodes (further explanation of auto-coding and how to do it in NVivo is available on the NVivo help website). This can be useful to separate out the different speakers …

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Series Intro: Useful apps, services, and software.

This post is an introduction and placeholder for a planned series of posts on useful apps, services, and software. Once there are a few posts in the series I will eventually promote this post to a page with an index of all the posts from the series. I decided to make a series for this because although using computers has become a key part of academic work, too many academics …

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Tasker: Lone Worker streamlining

Tasker is one of the top reasons I regularly give when asked why I prefer Android to iOS. The app was designed to provide a means to run tasks based on contexts as defined by the user. For example, turning the notification volume on the phone to silent at night or reading out text messages received while driving. Through the wide range of triggers, actions, and third-party plug-ins it is …

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Improving NVivo with AutoHotKey: Faster Attribute Values Input Script

The core component of the fieldwork for the Welfare Conditionality research project is an on-going three waves of qualitative interviews with 481 welfare service users sampled across nine different policy area. In order to assist with descriptive statistics and finding subgroups amongst our sample, we have a set of key attributes such as the participant’s age, household, benefits received,  etc. Furthermore, we have additional attributes specific to each policy area. …

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