Python for qualitative analysis
Recently, I've been working with data from a mixed-methods research analyzing systems across several African countries (more on that soon). Traditionally, this kind of data has been explored using qualitative tools like NVivo, Dedoose, and Atlas.ti, but I’m taking a different approach, partly due to licensing constraints, and partly because I find those platforms limiting. I’m exploring alternative methods that better align with my current goals and interests. This time, I'm using libraries like spaCy, Pandas, Scikit-learn, and NLTK, exploring how structured text analysis can uncover patterns often missed in manual coding. And of course, I’m bringing the insights to life with Matplotlib and a few custom visualizations. The goal? To show that qualitative data isn’t just rich in meaning, it’s also ripe for scalable, repeatable analysis with data science tools. I’ll be sharing some aggregated findings soon, so stay tuned to see how we can rethink what “qual” can mean in the age of data science.
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