By Molly Hanrahan, Monitoring & Evaluation Intern
I love working with the data because it shows us where we’re at, where we’ve been, and where we’re going. It’s exciting to track our progress over multiple years, to dig into whether we’re improving or not and why. But as much as data is able to tell us, there is a lot that it can’t tell us, and a lot that is misleading with careful review.
At first glance, we can tell that enrollment numbers are increasing in some schools, while in others they are decreasing. I was excited to see the increasing numbers, but bummed to see those that were decreasing, and I wondered how we could reverse the trend. But numerical data can be deceiving in that it hides the details of the actual experience underneath. In some schools, the class sizes are huge and there aren’t enough teachers for the kids—so increasing enrollment may further that problem and result in a lower-quality education for the students. For one of our schools, enrollment decreased because another school was built in the area, and students left to attend that school as it was closer to them. So while for some schools, decreased enrollment may be a bad thing and increased enrollment may be a good thing, making that conclusion for every school ignores some of the moving parts beneath the data.
Take another of our metrics, the 9th grade graduation test scores (DEF scores). DEF scores in our schools are all over the place. They vary significantly from school to school and even year to year within schools. We’ve analyzed DEF scores against some of our other metrics—like book ratios, gender ratios, class sizes, and more—and have not yet found a trend. In this case, we have the hard data, the pass rate, but we’re still working to figure out the workings beneath the data to understand why, so that we can hopefully improve it.
Data is exciting and necessary because it allows us to see whether our work is having an impact, but we have to really delve into the numbers to find out why they are that way to confirm or disprove our initial assumptions. This has been a great lesson for me to learn this summer, and the more I work with the data, the more excited I get about figuring out what’s going on underneath.