Every nonprofit knows that impact studies are vital — and expensive — to conduct. You need to be able to know (and show) that your efforts are making a difference, but you don’t want to divert your already limited resources to costly data collection and analysis. In this case study, we’ll show you how Shelter From the Storm added public data to their analysis in just seven simple steps.
The Nonprofit: Shelter from the Storm
Shelter from the Storm was a nonprofit serving low-income and vulnerable youth across Pennsylvania. Realizing that a number of factors contribute to the ultimate success and well-being of the state’s young people, they hypothesized that providing reliable access to health care services would improve teenagers’ chances of completing high school — which would improve their chances of long-term success. Their program secured health care access for vulnerable youth across the state.
The Problem: Impact Measurement
While their intentions were good and their hearts were in the right place, the team at Shelter From the Storm knew it was not enough to suspect health care access and education were related; they had to prove it. They needed to demonstrate their efforts were actually having an impact on the youth they served. Unfortunately, conducting an impact measurement study was prohibitively expensive and operationally challenging.
The Process: Combining Internal and Public Data for Analysis
Shelter From the Storm had statistics on the young people participating in their program. We decided to combine that information with public data on youth health care access and graduation rates across the state to determine if their work was making a difference.
We began searching for public data that would provide us with insight on other low-income youth who didn’t have access to the program (or other similar programs). Conveniently, the state of Pennsylvania had launched an open data portal just one year earlier — from there, we could examine graduation rates and health care access statistics for youth in counties across the state.
We merged the public datasets with Shelter From the Storm’s own statistics in Tableau, using the county name as the linking variable since both datasets included information by county.
After combining internal and public data, we began analyzing. Were young people in the program graduating at the same rate as similar youth not receiving the same kind of support? Using the public data, we made maps of health care access and graduation rates by county.
We analyzed the relationship between the two key variables — access to health care and graduation rate.
We added a regression line and were able to show a significant trend.
The Result: Clear Impact Measurement and Better Grant Applications
We won’t go into all the complex details of the analysis here — the result is the important part. Shelter From the Storm was able to generate a county-by-county map demonstrating the difference between youth participating in their program and similar youth without health care access support.
Their project was making a real difference in the lives of the young people they worked with, and by adding public data to their analysis, Shelter From the Storm was able to tell that story to the world — and include it in grant renewal applications. They were able to know — not just hope — they were helping the youth the program aimed to support.
Want Help Adding Public Data to Your Analysis?
At Datassist, we understand not everyone lives and breathes data analysis and visualization like we do. To help nonprofits, journalists, and policymakers alike make the leap into using public data in their analysis, we’ve developed a simple seven-stop process that we’re pleased to share at no cost.
Still have questions about using public data? Our experts are always here to help. Contact us anytime with your questions, comments, or feedback.