It feels like I change the theme about once a decade or so, and it felt like time. I have not had a photo on this and now I do. Other image #1: zinnia from my backyard - these come out late and last a long time. They are still blooming now. Other image #2: laser cut wood patterns from the KSU Design Innovation Hub. I did not make those and do not know who did, so I cannot give credit. This is one of many examples of maker things on display in the DI Hub reactor room.
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aWidespread availability of and interest in AI models (e.g., ChatGPT) has not surprisingly motivated more researchers and/or people who need to demonstrate research activity to try to figure out new ways to report findings based on minimal direct engagement with data.
During the Golden Age of QDAS (Qualitative Data Analysis Software) emergence - by which I mean some point in the 2010s when NVivo, Atlas.ti, MAXQDA, and to a lesser extent the pioneer in web-based QDAS, Dedoose, were becoming known and more widely used, it seemed that a lot of people around me were particularly excited about the potential to use a qualitative analog of statistical software programs like SPSS or SAS. By this, I mean a mechanism for entering or directing toward your data, clicking some boxes and obtaining comprehensive output that showed the final analysis. Of course the QDAS programs did not quite work like that and were instead database builders were text was selected instead of typed, and categories could be created as you went, rather than needing to create the organizational structure beforehand. This to me was still great progress, especially in these programs' ability to run queries and hyperlink to context of each coded excerpt. I have coded in Word a lot, and still use Word. But it requires work arounds to see context and is not ideal for people who do not have great typing skills. And although I did not mention Quirkos above - because it came along a little later than the others, it is my go to QDAS program these days. These programs have all become more sophisticated over time. However, even the so-called auto coding (or theory building, available in HyperRESEARCH) functions are mostly researcher initiated, and require some prompts, and cannot interpret context although the program may identify patterns, typically based on frequency. Because, in a way, you could think of most analysis software - qualitative or quantitative, as a sort of calculator. The same thing applies to the things people used to talk about, like machine learning algorithms, before "artificial intelligence," which is admittedly a much sexier term, became so popular. As an aside, I wonder how many people visualize "artificial intelligence" as female? I'm thinking about many references through time including the original "Star Trek" computer voice, Siri and Alexa, OnStar, the 1970s "Stepford Wives," the 1929 Metropolis movie poster, Rosie from "The Jetsons," and Scarlett Johansson in "Her." Of course there are some discomforting cases, the "Lost in Space" robot, and Kit, the car from "Knight Rider." I am personally curious right now how often already ChatGPT and competitors have been asked to analyze qualitative data. |
AuthorI am Sheryl L. Chatfield, Ph.D, C.T.R.S. I am a member of the faculty in the College of Public Health at Kent State University. I also Co-coordinate the Graduate Certificate in Qualitative Research and I am a member of the Design Innovation Team at Kent State. Archives
February 2024
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