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.

Creating EiTM’s Website & Logo

By Laura March

Dancing letters saying Welcome To My Site!

The opposite of EiTM’s digital aesthetics. GIF from Creative Market (2017).

One of my projects this semester was creating our lab’s logo and website. While some may think an online presence can be slapped together in an afternoon using free tools, taking the time to analyze and design a polished digital identity at the start saves headaches (and money) in the long term.

I started this project by asking my fellow lab members to collaboratively fill out a “Pre-Build Questionnaire” I developed for this purpose a few years ago. Questionnaire answers, combined with Dr. Melo’s aesthetic preferences, translated into the site’s focus on clarity and simplicity. Visually, this can be seen in our black-on-white color scheme, modern typefaces, and a simple logo that combines a LED graphic directly into the lab’s name. Potential accessibility oversights (like color contrast issues or screen reader errors) were checked and accommodated throughout the process using WebAIM’s WAVE tool.

We were also lucky enough to be able to use Carolina CloudApps to host our own instance of WordPress, allowing us the freedom to choose a theme and plugins. Using WordPress (the most popular content management system on the web) with a regularly updated and established premium theme ensures we will have the features (and technical support) needed for many years to come.

Some of the features from the custom plugins and theme we use on this website include:

  • Email subscriptions – see the “Sign up for News & Updates” call-out below and enter your information to receive updates delivered to your inbox.
  • Twitter RSS feed – our News page automatically collects and shows tweets with the hashtag #NSFEITM.
  • Portfolio pages and filters – see the 2020 Bibliocircuitry Project pages for more.

What do you think of our website and logo? Is there anything you’d like to see us change? Feel free to get in touch and let us know your thoughts.

Semi-structured interviews

Kimberly Hirsh, PhD candidate and member of the EITM Lab, writes on her blog:

One of my responsibilities in the Equity in the Making lab is to create an interview guide that will help us learn what makerspace leaders in the UNC system consider to be defining features of a makerspace. I originally thought this was going to be a survey, so I came up with a list of about ten questions and then in conversation with my colleagues on the project, added four more. I realized in that conversation, however, that it was an interview guide for a semi-structured interview, not a survey. I told my colleagues I’d take our list of questions and hone it so that it was “more interviewy, less survey-y.” What did that look like?

Read more about Kimberly’s thoughts on preparing for a semi-structured interview on her blog post: Semi-structured interviews: Stick to only a few big questions, but leave room for follow-ups.