Collaborative Qualitative Analysis: An Analytical Workflow

By Kimberly Hirsh

In the last blog post, I described our technical workflow for collaborative coding in the EITM Lab. In this blog post, I will explain our analytical process. It works as follows:

  1. For the first three transcripts, we undertook line-by-line coding, assigning a label to each line of code.
  2. At the end of line-by-line coding, we had hundreds of individual codes, but many overlapped in meaning as each lab member created her own verbiage.
  3. Laura and Kimberly then coded the codes, creating possible categories to use in the coding of future transcripts.
  4. Dr. Melo reviewed the categories Laura and Kimberly had generated and built a codebook based on these categories and her own coding experience, providing definitions and examples for each code.
  5. All three lab members then applied the categories Dr. Melo included in the codebook in later rounds of coding.
  6. As we engaged in later rounds of coding, we continued to identify new codes and categories as we noticed them recurring in the data.

Do you have any questions about our data analysis process? Feel free to get in touch.

Collaborative Qualitative Analysis: A Technical Workflow

By Kimberly Hirsh

A key piece of our research process at the Equity in the Making Lab is conducting collaborative qualitative analysis. In this blog post and the next, I’ll share some of the crucial elements of our process thus far.

Our research approach is based in Charmazian Grounded Theory, so we knew that we would need to begin analyzing the interviews we collected with line-by-line data analysis. We needed to create a workflow that would allow us to code individually, compare our codes, and discuss how the coding scheme should evolve.

Because we are using MaxQDA for our data analysis, we rely heavily on MaxQDA’s Teamwork Feature. Our workflow uses this feature to enable us to code individually, import all of our codes into a central project document, and then continue to work individually in later rounds.

We use Microsoft Teams, OneNote, and OneDrive. The technical workflow goes something like this:

  1. Dr. Melo received the initial set of transcripts from our transcriptionist and uploaded them to OneDrive.
  2. Kimberly created the lab-wide MaxQDA project and imported the initial set of transcripts into it as documents.
  3. Kimberly created three copies of this MaxQDA project; each copy had one lab member’s name appended to the filename.
  4. Each lab member downloaded her respective MaxQDA project and coded the transcripts.
  5. After finishing coding, each lab member created a MaxQDA exchange file and uploaded it to the lab OneDrive.
  6. Each member also wrote a memo with her notes and reflections on the coding process and added it to a team notebook in OneNote.
  7. Kimberly imported each lab member’s exchange file into the lab-wide MaxQDA project.
  8. During our weekly meetings, we reviewed the coded documents, finding mostly agreement in our coding, and discussed our memos.
  9. Prior to the next meeting, Dr. Melo would upload new transcripts and each lab member would import them into her own MaxQDA project, code, and memo them.

Creating a technical workflow, of course, doesn’t capture the analytical components of the process. The next blog post, Collaborative Qualitative Analysis: An Analytical Workflow, will address our analytical process.


Research Program Recap

By Dr. Marijel (Maggie) Melo

June 2020-January 2021

The EiTM Lab is gearing up for a new semester, so I (Dr. Melo) wanted to take a moment to share a project update on Phase 1 of our research program. We’ve completed our study design, interviews, and coding – and began the early stages of analysis (although I will say data collection and analysis are co-acted throughout the entire process). We’ve gathered rich, interesting, and surprising data that inform how we will respond to our research question, “What are the defining features of a makerspace?” We designed this study with the intent to accomplish two major tasks. First, we will identify 3D models, assets, and avatars for the VR environment (Phase 2). Second, we will develop a theory of makerspaces. We’re confident in the data we’ve collected and are fascinated by some of the early findings we’ve discovered.

Early Findings

We asked participants to imagine walking into a room they soon realize is a makerspace. How do they know they’re in a makerspace? What do they see, smell, and/or hear?

Here are a few early responses:

  • Sight: 3D printers and tools, people working/making, blurring of staff/user, and projects
  • Smell: Burning wood, “waffles,” burning plastic, and disinfectant
    • Honorable mentions: “generic library smell,” Bojangles, and “project du jour”
  • Sound: Conversation, tools/machines, music

While the identification of makerspace features was fairly consistent, we were surprised to encounter some resistance to define makerspaces more holistically. We identified a pattern in the way that several participants responded: many began their descriptive musings with affective (not physical) descriptions of the space. Descriptors such as “good vibrations,” “bright,” and “inviting” showed up frequently in responses. We also felt a palpable sense of tension emerging during the interviews and subsequently in the coding process. Asking participants to define a makerspace seemed to be difficult, revealing, and anxiety-provoking. I have a lot more to say regarding this tension, but will leave this brief update with an invitation to view my recent presentation at the Coalition for Networked Information. I outline and contextualize our early findings in this video.

I’m eager to see where the data takes us this semester as we begin transitioning to Phase 2 of EiTM’s research program. Our sincerest thanks to the information professionals we’ve interviewed for Phase 1.

Re-making the Library Makerspace Book Launch

Jan 29, 2021

10am PST/1pm EST

You’re invited to the (virtual) book launch of Re-making the Library Makerspace: Critical Theories, Reflections, and Practices, from Library Juice Press, edited by Jennifer T. Nichols and Maggie Melo. The volume offers chapters that acknowledge power and structural inequity, reflect on moving forward toward social justice, and celebrate successes and progress.

Read more about the event, Virtual book launch of Re-making the Library Makerspace, or register for the webinar directly via LibraryJuice.

Book Launch Poster featuring the book cover of Remaking the Librarian Makerspace