Date: Sunday, August 17, 2025
Hello, AEA365 community! Liz DiLuzio here, Lead Curator of the blog. This week is Individuals Week, which means we take a break from our themed weeks and spotlight the Hot Tips, Cool Tricks, Rad Resources and Lessons Learned from any evaluator interested in sharing. Would you like to contribute to future individuals weeks? Email me at AEA365@eval.org with an idea or a draft and we will make it happen.
Hi, I’m Midjina Richard, MPH, CHES. I’m an Association of Schools and Programs of Public Health Fellow at the US Centers for Disease Control and Prevention. This post continues the series: what is reflective practice (and why is it important to us as individuals and evaluators)? In previous and current work experiences, I’ve come to understand the crucial role data can play in individual and community health and wellbeing. With that understanding, I want to revisit longstanding debate regarding merits of quantitative versus qualitative methods. While I studied and used both, I believed that quantitative methods and data did not capture or convey a person’s voice. I attended training hosted by We All Count that debunked this belief and demonstrated how to humanize quantitative information and communicate important stories behind numbers. Humanizing quantitative data means presenting numbers and statistics in a way that emotionally or personally connects with people (QuestionPro). We can purposefully attempt to make numbers relatable, understandable, and meaningful by connecting them to real human experiences or stories. For example, reporting that a condition or can be more compelling accompanied by photos, quotes, or illustrations of people or families experiencing the issue and what it means to them in practical terms (e.g., expenses, unemployment or underemployment, emotional or physical pain, or strain on relationships).
As a Haitian immigrant who grew up in the US, first-generation college student, and woman of color, I recognize the importance of amplifying diverse voices and ensuring they’re authentically interpreted. My identity has shaped my commitment to how people are represented in data. I pursued a career in public health after learning more about complex social barriers to wellness and well-being. I wanted to work in a profession that gathers data to support work toward healthier and longer lives. But numbers without context or explanation left me wondering how quantitative data can fully describe social conditions.
Critiques of qualitative methods made me worry that my work wouldn’t be viewed as reliable or valid enough (i.e., compared to quantitative methods). I gravitated toward qualitative data to connect with people, hear their stories, and elevate their voices. I grew confident in qualitative methods when working with people and hesitant to solely choose quantitative methods. Participating in We All Count, I learned how our methodological or technical choices can prioritize whose experiences and perspectives are captured and how. These choices can help to humanize quantitative data or hinder opportunities to lived experiences and context. The workshop was an opportunity to reflect on how I understand quantitative data. For example, I can ask myself and others how to better connect people and numbers: did I use quantitative data in ways that align to or respect experiences of people who provided information (e.g. program participants), or have I or others made analysis or reporting choices that can misrepresent quantitative data? We All Count’s Data Equity Framework can aid in data analysis and interpretation. For example, their job aids can be used to realign a project more toward people-focused intents.
Ultimately, participation in the workshop started my reflection process. I’ve come to understand that I have power and privilege to select methods that bring individual and group experiences and perspectives to light, quantitative and qualitative. My thinking about both continue to evolve but I feel more prepared to actively question how to make people and places most visible and appropriately represented.
To learn more ways to humanize data, DataJournalism illustrates how adding human context to data is important in making statics more relatable and engaging for deeper impact.
Disclaimer: The findings and conclusions in this post are those of the author and do not necessarily represent the official position of the US Centers for Disease Control and Prevention.
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