Using flexible coding, a team of public health researchers led by the University of California, Irvine, have produced a 4-part guide that could potentially shift the paradigm for how researchers analyze and share their findings.
Their study and the 4-part guide, published in the International Journal of Qualitative Methods, explores the value of how flexible coding can optimize the analysis of qualitative data sets, particularly in the field of public health where information is dynamic and constantly changing.
Flexible coding is a straightforward approach to organizing and analyzing qualitative data, making it easier for researchers of all experience levels to collaborate. Qualitative research is important within the field of public health where advancing health equity is at its core. Qualitative information such as perceptions and motivations towards engaging with our public health and health care institutions offer nuanced insights into structural determinants of health.
To be more efficient and quicker in analyzing qualitative data, flexible coding involves breaking down large amounts of data—like interviews, focus groups, or open-ended survey responses—into smaller, manageable pieces that can be categorized based on broad common topics. This allows researchers to focus on specific sections of their data, making it easier to capture new insights that may emerge during the analysis. By using flexible coding, teams can work together more effectively, combining their different perspectives and expertise.
“The scale and frequency of public health crises have accelerated in recent years from the COVID-19 pandemic to health effects of wildfires where health inequities are amplified,” said senior author, Alana LeBrón, PhD, associate professor of health, society, & behavior at UC Irvine Joe C. Wen School of Population & Public Health. “Therefore, the field of public health would benefit from a timelier and more inclusive approach to conducting research.”
To provide context to this approach, the team focused on two ongoing studies from their research partnership that needed to analyze qualitative data.
The first, the Community Activation to Transform Local Systems (CATALYST) study, is a multi-year NIH-funded project that grew out of the Orange County Health Equity COVID-19 Community-Academic Partnership (OCHEC-CAP), formed in May 2020. This study includes six local community-based organizations and academic collaborators. The second study, building on CATALYST findings, is part of the California Collaborative for Public Health Research (CPR3) initiative, exploring the mental health impacts of the pandemic on residents and community health workers. Together, these studies feature over 127 testimonies from unique community members.
Through these studies, the researchers employed their four-stage flexible coding analytical process, allowing them to sort through the raw data of 127 testimonies to enable multiple in-depth analyses.
The process includes preparing transcripts and other artifacts for flexible analysis (Stage 0); applying index codes to the raw data to identify segments of the data that relate to specific topics of focus (Stage 1); applying analytical codes to relevant indexed subsets of the raw data, based on specific research questions (Stage 2); and refining the analysis iteratively (Stage 3).
This four-stage process maximized data impact and validity by providing standardization, leveraging technology, and encouraging continuous engagement with the data. It also allowed the researchers to remain responsive to community priorities and accommodate different team models.
“As public health research continues to evolve, flexible coding enables researchers to tackle multiple research questions simultaneously and revisit large datasets for future analysis,” said corresponding author, Melina Michelen, a qualitative research manager in LeBrón’s Lab. “This ensures that community voices remain central in shaping interventions and that the research can adapt as new questions arise.”
The study underscores that flexible coding is more than just a method—it’s a reflection of how dynamic and evolving public health is as a field. Importantly, it serves as a training tool, so that the next generation of researchers can access qualitative tools to address complex health challenges.
“For students, this research highlights how they can actively contribute to developing new tools and strategies for public health,” says LeBrón, who also has an appointment with the Department of Chicano/Latino Studies at the UC Irvine School of Social Sciences. “Public health is not static, and flexible coding exemplifies how innovative methods can meet the growing complexity of health inequities and crises. As students enter the field, they can help lead these evolving approaches that better serve communities, promote equity, and improve research outcomes.”
Additional authors include Madeleine Phan, Arianna Zimmer, Natalie Coury, Brittany Morey, and Sora Tanjasiri from UC Irvine Joe C. Wen School of Population & Public Health; Gloria Montiel Hernandez from AltaMed Health Services & Latino Health Access; Patricia Cantero from Latino Health Access; Salvador Zarate from UC Irvine School of Social Sciences; Mary Anne Foo from Orange County Asian and Pacific Islander Community Alliance; and John Billimek from UC Irvine School of Medicine.
Research reported in this RADx® Underserved Populations (RADx-UP) publication was supported by the National Institutes of Health under Award Number (U01MD017433); NIH Loan Repayment Program; California Collaborative for Pandemic Recovery and Readiness Research (CPR3) Program, which was funded by the California Department of Public Health; and the University of California, Irvine Wen School of Population & Public Health, Department of Chicano/Latino Studies, Center for Population, Inequality, and Policy (CPIP) (California Collaborative for Pandemic Recovery), and Interim COVID-19 Research Recovery Program (ICRRP). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders Grant Number: U01MD017433; 140673F/14447SC.
