Most of us in the nonprofit world were taught that real evaluation requires a research team, a grant, and a methodology section. So we measure what's easy to count — and quietly avoid the things that actually matter.

This episode is about doing it differently.

Hillary challenges the way program evaluation typically works: heavy on outputs, allergic to nuance, and overly deferential to "capital-R" Research. She makes the case for "small-r" research — using validated frameworks as a starting point, defining outcomes from lived organizational experience, and building measurement systems designed to help you learn, not just report.

You'll hear from Christian Quijano, Director of Data & Analytics at the Downtown Women's Center in Los Angeles, on how his team took academic research on economic mobility and turned it into something their organization could actually use — an internal measure they called "earning power." (Spoiler: their first definition wasn't good enough, and that's exactly the point.)

The episode closes with a case study from All Square, a social enterprise in Minneapolis working to shift public perception about incarceration. How do you measure something that lives in people's minds? Key informants, customer reviews, and existing research — it's more possible than you think.

Mentioned: Acs, G., Conner, A. L., Lyons-Padilla, S., Markus, H. R., Patel, N. G., Tumolillo, M. A., & Eberhardt, J. L. (2018). Measuring mobility from poverty. Stanford SPARQ. https://sparqtools.org/wp-content/uploads/2018/06/measuring_mobility_paper.pdf

Guest: Christian Quijano is a nonprofit data and strategy leader who helps organizations uncover the story their data is telling. He brings a continuous learning and improvement mindset to connect the dots between programs, operations, and outcomes. As Director of Data & Analytics at the Downtown Women’s Center in Los Angeles, he leads organization-wide data infrastructure, dashboards, and quality and compliance strategy to strengthen outcomes for women experiencing homelessness. With deep expertise in theory of change and monitoring and evaluation, he is known for answering big questions through clear, compelling visualizations, blending technical rigor with community-centered design—and is actively exploring how emerging tools like AI can support this work responsibly.

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