ENV 600a () / 2023-2024

Qualitative Data Analysis & Academic Writing

Credits: 3
Fall 2023: W, 9:00-11:50, Sage 41c

What happens after qualitative field work?  In this course we will work through the complex, multi-staged process of qualitative data analysis (QDA). QDA, rarely examined in its full richness, includes at least 4 different processes. These are:  1) learning the mechanics of coding qualitative data; 2) developing an analytical and interpretive lens—or your epistemological and theoretical orientation in relationship to other scholarship in your field; 3) cultivating the ability to communicate your findings, both in writing and verbally. 4) learning to demonstrate the rigor, trustworthiness, and credibility of qualitative scholarship. QDA, practiced as an emergent analytical process, requires approaching these processes concurrently.
The first part of this course will focus on explicitly first two steps of this process: coding and developing an analytical or interpretive lens. As we progress through the semester, we will also pay close attention to how scholars in our field navigate the third and fourth step: communication of findings and demonstrating rigor, trustworthiness and credibility—which we will refer to as expertise and authority. As such we will be developing a tactic understand of these crucial processes in social science.  In the second semester we will focus exclusively and explicitly on individual writing and thus develop these skills of communication, expertise and authority through writing, seeking peer feedback, and critiquing the writings of scholars in our field. 
Due to the inherent interdisciplinarity and individualized nature of student research in YSE, students will need to be highly self-directed as they engage with their own data will need to be actively engaged in the peer reviewed literature related to your specific field of interest.
This course is designed masters or doctoral students designed for students who have completed a minimum of 8 weeks of qualitative research and are ready to analyze their own data.