Launching the Neighborhood Faces Research Study: Our Methodology and Data Collection Process
We’re excited to announce cohort 1 of the Neighborhood Faces Research Study, an initiative to investigate the interplay between civic engagement and equitable outcomes in our urbanizing societies. With 65% of the world’s population predicted to live in cities by 2040, we’re on a mission to understand how civic engagement can be leveraged to manage this growth sustainably.
This study will explore the social dynamics within rapidly growing cities, such as New York City, Los Angeles, Chicago, San Fransisco, Boulder, Washington, D.C., and Minneapolis. We aim to address the challenges of housing, transportation, waste management, education, and public safety that these urban areas are likely to confront.
Data Collection Process
The Neighborhood Faces Study was conducted in two waves. The first wave was personal, focusing on building rapport and trust with stakeholders in Los Angeles, California, and New York, New York. The goal was to understand the field of civic engagement and the personal journeys of active participants.
We created a focus group of 7 stakeholders from various occupations, such as teachers, neighborhood empowerment officers, racial equity officers, executive directors, directors of public information, assistant land use planners, and non-profit founders. We took note of the commonalities in their paths and analyzed the relationships between events, actors, and artifacts contributing to their civic engagement.
For the second wave, we collaborated with Mentor Me Collective, a non-profit dedicated to closing the wealth gap in marginalized communities through mentorship and social capital. We facilitated a three-month fellowship introducing user experience for 50 participants, 35 of whom contributed to our study. The fellowship consisted of bi-weekly lessons on user experience research, design methodologies, and a team final project of creating an app addressing a social dilemma.
Data Management
To manage the data, we created a sample frame to collect stakeholders’ information, ensuring their confidentiality and respecting their privacy. We also used a flexible interview guide that allowed participants to share their personal narratives while ensuring the discussion covered relevant topics.
We employed instaminutes.com and otter.ai for audio transcription and Miro, a virtual whiteboard platform for collaborative organization and visualization of our qualitative data. These tools, coupled with our manual reviews, ensured the accurate capture, transcription, and storage of interview data.
Ethical considerations were at the forefront throughout the process. We secured informed consent from the participants, ensured their understanding of the study’s purpose, their role, potential risks, and benefits, and implemented strict measures to protect their privacy.
Data Collection Methods
Our data collection process employed a stratified sampling method, which involves dividing the target population into different strata based on specific criteria — in this case, occupation. This method ensures a representative sample of the population, providing an in-depth understanding of each participant’s unique perspective based on their occupation, which is considered a key factor shaping their experiences and thoughts.
Below is an interactive stacked bar chart that represents participants by occupations and gender.
Limitations and Future Plans
The initial phase of the Neighborhood Faces Study was focused on local neighborhoods within a 15-mile radius, with participants selected from various occupational groups including civil servants, teachers, engineers, social workers, policy makers, urban planners, and representatives from non-profit organizations, among others.
While we are grateful to the participants who have shared their stories with us, we are cognizant of the limitations encountered, such as the smaller-than-ideal sample size due to the participants’ existing commitments. To counter these limitations and improve the sample’s representativeness, we plan to increase the sample size and include a more diverse range of participants in future studies.
In our next cohort of the Neighborhood Faces Study, we are considering the application of cluster sampling. In this method, we will divide the cities we focus on into clusters based on demographic and socio-economic characteristics, and then select a random sample of these clusters to represent the larger population. This approach ensures a diverse range of perspectives while still maintaining our focus on local neighborhoods.
Inclusion and Exclusion Criteria
To maintain the integrity of the research, we’ve established the following criteria:
Inclusion criteria include:
Participants must be over 18 years old.
Current or past employment in an organization.
Active or previous use of at least one technology platform for work purposes.
Stakeholder in the study, including professionals from civil services, education, engineering, social work, policy making, urban planning, architecture, and more.
Ability to share personal narratives, experiences, and insights related to the study objectives.
Exclusion criteria include:
Participants not fitting the defined stakeholder titles.
Lack of knowledge about tools/processes used in their work.
Lack of relevant experiences related to the study objectives.
Below is a breakdown of the locations we focused on for the study:
Typeform: We standardize and automate the data collection process through Typeform, allowing participants to fill out a consistent and comprehensive form.
Google Sheets: We use Google Sheets for organizing and analyzing data such as our sample frame, qualitative codes, and participant morale. Its robust features, such as formulas, pivot tables, and charts, make it a versatile tool for complex data analysis.
Instaminutes, Otter.Ai, Dovetail: To streamline our audio transcriptions and note-taking abilities during interviews, researchers used either instaminutes, otter.ai or dovetail. This made it easier for our team to focus on connecting with their stakeholder rather than multi-tasking. This also helped make transferring data into Miro easier.
Miro: Miro, a digital whiteboard platform, offers a creative and flexible approach for visually organizing and analyzing data. From mind maps to understand relationships between different data pieces, Miro enhanced our research with its focus on visual, real-time collaboration. Qualitative coding was primarily done in Miro. Qualitative coding is a method researchers use to categorize data and identify patterns and themes.
We’re eager to share our findings and insights as we navigate the civic engagement landscape. Stay tuned for more updates as we delve deeper into the Neighborhood Faces Research Study!
