In direct[message] we interviewed 33 older adults about their experiences and thoughts on arts and technology. The interviews were guided by a survey that was filled out as the interview progressed, but the interviews themselves were recorded and professionally transcribed to capture knowledge that the questions didn’t address.

When you ask ten people what their favourite colour is and four say blue, that’s something you can put in a chart. If you asked ten people to describe the last time they felt proud of something they’d created themselves, how would you chart that?

A process researchers use called qualitative data analysis employs subjective judgment based on data that is “soft” or non-quantifiable (i.e., not able to be measured). It is a way to deal with intangible and inexact information, and derive from that information lessons and other knowledge.

To show that community members can have a substantive role in community-based research and to inspire more research in your community we share here the process that our team developed and implemented.

Shared understanding

Bringing the research team into a shared understanding of qualitative analysis was important to the coding and analysis process. We discussed that qualitative analysis is partly about interpretation and that coding (assigning themes or concepts to fragments within the transcript) is a medium in relationship to more than one thing.

Coding: a process of assigning codes, words, or phrases that identify to which topics or issues portions of a transcript refer, and organizing the data in a way that is useful for further analysis.

One helpful metaphor we used was that coding is like having a bunch of buckets. Each bucket is a theme where you put pieces of participants’ stories.  This includes quotes, and it can also involve putting one piece of participants’ stories into more than one bucket.

Restrictions

As our coding and analysis process took place during the COVID-19 pandemic, all training, learning, coding and analyzing happened within Zoom meetings.

Code book development

In order to begin the coding process, we collected codes that came out of interviews done with peer facilitators when the project first began. As the project progressed, we wanted to develop a sense of what the themes meant to the community consultants after having done interviews and working with the project over a period of time.

peer facilitator: an older adult who interviewed a respondent to the research survey, transcripts of which were the subject of analysis

community consultant: an older adult who is part of the direct[message] project team and contributes to the direction of the project. some community consultants were peer facilitators but not all, and vice versa.

To do this, the team learned to use JamBoard, a digital whiteboard tool. We used it to collect definitions for the themes in a visual manner while using Zoom. We continued using the JamBoard to reflect on the coding process and eventually new codes (and code definitions) were developed.

Practice and technical skill development

A coding template was created (and eventually re-formatted) in order to make coding accessible for community consultants. We practiced coding in breakout groups during meetings and took home transcripts to practice with independently. The coding process involved many meetings and the need to train in and practice navigating technological practices including (but not limited to): enlarging and decreasing window size; view switching between windows; opening and downloading password protected documents from the google drive; and navigating a shared screen on zoom.

Analysis process  

At the bottom of each transcript, we invited those who were doing the coding to share what stood out to them about the transcript. We collected and summarized these, using a narrative approach to tell the overall story of each transcript from the coder perspective.

Next, a document was created for each of the ten codes. From there, all the excerpts from each coded transcript were collected for each code. For example, there was a document created for the code Participant Identity. We looked at all the transcripts, and each time someone had used the code Participant Identity, we collected the excerpt and put it into the Participant Identity document.

Each of the code documents was analyzed for subthemes. We looked for similarities and differences in responses, things that stood out, coder discrepancies, salient quotes in each code and subtheme, new things that we didn’t necessarily look for or ask about, and places where codes connected organically. We collected the subthemes from each code document and summarized these in the report.

New tools

A second qualitative data analysis subproject was undertaken later in the larger direct[message] project to analyze the transcripts of post-activity interviews with the 40 older adults that participated in the summer 2022 online art series.

Some community consultants who had engaged in the earlier analysis process (described above) reported on their experiences with coding and analysis and made suggestions to improve the process. The analysis was able to start sooner because these community consultants had experience doing the work, had already developed requisite technical skills, and recommended process efficiencies.

The most significant change in process was the adoption of a web-based, collaborative data analysis tool, Dedoose. In the original analysis project this had been considered but deemed too complex to learn in addition to other necessary skills and knowledge. In the second analysis project the coding team committed to learning Dedoose and found many benefits to offset the added learning required, in the form of less clerical work, less space for errors, increased ability to collaborate, and additional capacity for viewing coded data and making observations.