post contributed by Lydia Klein ’22, Digital Scholarship Assistant
The Digital Scholarship and Curriculum Center (DSCC) now offers NVivo for qualitative data analysis. NVivo allows you to import research data from virtually any source, centralize and organize your data, and analyze unstructured text, audio, video, and image data, including interviews, focus groups, surveys, social media, and journal articles.
Functions of NVivo include:
- Data analysis: identify themes and patterns in qualitative data
- Data collection across multiple devices using Evernote
- Visualization of ideas and findings in charts, maps, and models
- Transcription of audio or video files
- Collaboration Cloud (not available with the DSCC license): allows groups to work on the same project within NVivo, and sharing and publishing conclusions, data, and visualizations
The DSCC is currently exploring NVivo in conjunction with Digital Scholarship Fellows Assistant Professor of Psychology Nakia Hamlett and Professor of Psychology Jefferson Singer. Their Just Futures New London project, funded as part of the Mellon Foundation’s Just Futures Initiative and University of Michigan’s Crafting Democratic Futures project, pairs New London high school students with people of color in the community to conduct oral history interviews about residents’ experiences of systemic racism and discrimination across their lifetimes. The teams conducted twenty interviews this past summer and have plans for a second round of interviews next summer. In the DSCC, we experimented with using Kaltura and NVivo to transcribe the interviews and found that NVivo yielded better results; however, a moderate amount of clean-up is still required for accuracy. I am now learning how to code, analyze, and visualize the data.
As this project progresses, I am developing documentation on how to use NVivo to assist future faculty and student projects. As someone who has little computer science or advanced software experience, I have no presumptions on how NVivo works and have to approach it from ground zero. This allows me to assess how easy (or complicated) NVivo is to learn and use. That said, learning the transcription process was quite straightforward, but learning to code has been a bit more complicated. Many of the tutorials that exist online are about older versions of NVivo, so adapting those to the latest interface is challenging. As I continue to work on the Just Futures New London project, I will be able to draw a more informed conclusion about the software’s functionality beyond its audio transcription services.
Please reach out to Lyndsay Bratton (lbratton@conncoll.edu) or Michael Dreimiller (mcdre@conncoll.edu) in the DSCC if you’d like to learn more!
