Thursday, February 3, 2022 11am to 12:30pm
About this Event
Join workshop in Webex: gsumeetings.webex.com/meet/aswygarthobaugh
NOTE: This session will NOT be recorded.
GSU Data Ready! Badges Micro-Credentialing: lib.gsu.edu/data-ready
The Logics and Logistics of Qualitative Research
A Framework for Exploring Concepts, Dimensions, and Relationships in Qualitative Data using NVivo Research Software
In this presentation, Dr. Ralph LaRossa, Professor Emeritus of Sociology, and Dr. Mandy Swygart-Hobaugh, Librarian Associate Professor for Sociology & Data Services and Team Leader for Research Data Services, will present both the theoretical-methodological logics and the applied-methodological logistics of conducting qualitative data analysis (i.e., non-statistical analysis of textual, audio, visual, and/or audiovisual sources). Dr. LaRossa will discuss the steps involved in building theoretically-rich qualitative analyses (the logics). Dr. Swygart-Hobaugh will highlight the specific features of NVivo qualitative research software that complement and facilitate these analyses (the logistics). There also will be opportunities for questions and discussion.
This presentation will be especially helpful for faculty and graduate students who are immersed – or about to be immersed – in a qualitative project and would like an overview on how to do qualitative analysis and how to use NVivo in the process. Those interested in publishing qualitative work and/or applying for grants based on qualitative work will also find it helpful.
NOTE: This presentation will NOT involve in-depth NVivo training – see the workshop listings on this page for other NVivo training opportunities.
Questions? Email Mandy Swygart-Hobaugh at aswygarthobaugh@gsu.edu
Suggested Reading:
If you have time, peruse this reading before the session to give you a grounding for the session's content:
Swygart-Hobaugh, M. (2019). Bringing method to the madness: An example of integrating social science qualitative research methods into NVivo data analysis software training. IASSIST Quarterly, 43(2), 1-16. doi.org/10.29173/iq956