Part 2: Qualitative Data Infrastructure and Research Methods
In Part 1 I talked about my experience using quantitative research methods at the MBTA. Over the past almost a year I have thought (and read) more about qualitative research and data (and the difference between the two).
For transit agencies to really consider and use community voices and lived experience as data they will need to institutionalize qualitative data and research methods. This will require different data infrastructure, data collection, and analysis skills.
In general transit agencies gather qualitative data for a particular project, plan, or policy decision as one-off efforts. Each effort sometimes regathers the same input from the community as previous ones. This can be a burden on both the community and the agency’s time. Part of the problem is that transit agencies (transportation more widely) don’t have a qualitative data infrastructure.
As agencies started getting more automated data from technology systems they developed data infrastructure to clean and store data. They hired IT staff to build and maintain data warehouses and data scientists to analyze the data. They built dashboards that visualize the data. Part of what this data infrastructure allows is for multiple teams to use the same data to answer different questions. For example, ridership data is stored in one place and many departments (and agencies and organizations) can access it for their analyses. The data infrastructure also provides transparency with open data.
In my experience transportation agencies don’t have the same infrastructure for qualitative data. There isn’t a centralized location and a storage system so multiple teams can find out what riders on a certain route or neighborhood are saying. The data from the customer call center remains siloed in that system. The input for a specific project stays with that project team. Data from the transit agency isn’t shared with the MPO or City. Or the public in a standardized way.
At the MBTA we did create an infrastructure for survey data. My team wrote an internal survey policy to standardize practices and data sharing. Part of this effort was standardizing basic questions so we could gather comparable results across surveys and time. This ‘question bank’ also allowed us to save time and money by getting all of our standard questions translated into the six languages the MBTA uses (based on its Language Access Plan) once. We attempted to create a single repository for survey data. (Most of the work to do this was organizational, not technical.)
Creating this type of qualitative data infrastructure requires thinking through data formats, how to code qualitative data (by location, type of input, topics, etc), and how to share and tell the stories of the data. And whoever is doing that has to have authority to impact all of the ways the agency gets community input. So importantly creating this infrastructure is also about where community engagement lives in an agency and how it is funded.
(An advocate I spoke to recently suggested that maybe this consolidation of qualitative data shouldn’t even live at a transit agency. That it should cut across all transportation modes and live at the MPO or other regional body.)
To move beyond using qualitative data in quantitative analysis, agencies need people with research and data collection skills not always found in a transit agency. Lots of agencies have started hiring data scientists to analyze their ‘big’ quantitative datasets. Agencies also need sociologists, ethnographers and other research skillsets grounded in community. These researchers can design community data collection efforts that go beyond public comments in a public meeting.
Qualitative research also asks different questions. Instead of using data to explain who is in the tail of a quantitative distribution, qualitative research asks questions like why is the distribution like this in the first place and how do we change it. Qualitative research is able to bring in historical context of structural racism, explore the impact of intersectional identities, and allows power dynamics to part of the analysis. Check out more from The Untokening on the types of questions that need to be asked.
Transit agencies often aren’t asking the types of research questions that qualitative research answers. Not just because they don’t have the staff skillsets, but because these questions don’t silo people’s experiences with transit from their experiences and identities overall. Lots of agencies try to stay in their silos, so they aren’t forced to address the larger structural inequities. It is easier to focus on decisions you think are in your control. For example, quantitative Title VI equity analyses are confined to only decisions made by that agency in that moment in time. But equity is cumulative and no one is just a transit rider.
Valuing community voices as essential data means agencies will need to invest in data collection, storage, analysis, and visualization or story-telling for qualitative data in a manner similar to quantitative data. The executive dashboards and open data websites will need to incorporate both types of analysis and data. More fundamentally it means that agencies will have to breakdown their silo and take an active role in fixing the larger structural inequalities that impact the lives of their riders everyday.
This series is about the data used to make decisions; clearly who is making the decisions is also important to valuing community voices. I chose to write this series in a way that shows how my thinking about data has evolved over time and will no doubt continue to do so. In fact it evolved in the act of writing this! For insightful dialogue about revision in writing and life, check out this podcast between Kiese Laymon and Tressie McMillan Cottom.