I spent last week at the National Center for Education Statistics STATS-DC Data Conference in Washington. Unlike Comic-Con, at this conference, heroes came costumed as agency administrations and data researchers. A large majority of the attendees were school administrators faced with the herculean task of collecting all sorts of mandated data from their districts. The rest of us were data users saying, “We want more data! More data please!”
The conference opened with a keynote speech by Catherine Lhamon, the Assistant Secretary for Civil Rights in the U.S. Department of Education. She talked about the Civil Rights Data Collection produced by the Office for Civil Rights. The CRDC is a really great dataset we’re hard at work trying to get on PolicyMap. It has really interesting stuff at the school level, like number of disciplinary events (broken down by race and sex), AP classes offered, sports and arts programs offered, teacher salaries, and tons more. It’s cool stuff.
The most recent CRDC data is from the 2012-2013 school year. Secretary Lhamon spoke to the attendees about the data collection for the 2014-2015 school year, which is about to begin. How important is this data? It found that black students are two to three times more likely than white students to be disciplined. Black females were disciplined more than any other demographic group. 7,500 preschoolers were expelled in the 2012-2013 school year. You read that right: preschoolers. Black preschoolers were expelled more than any other group. As a result of this data, Chicago eliminated preschool suspensions, and the city of Washington, DC, is considering doing away with them as well.
One session talked about alternatives to using Free and Reduced Price Lunch as an indicator for poverty. For years, researchers have used Free and Reduced Price Lunch (sometimes abbreviated FRL, sometimes FRPL) as a proxy indicator to see whether a student comes from a family in poverty. As it turns out, FRPL is becoming a somewhat less useful tool for this because the Community Eligibility Provision lets schools in low income areas offer all their students free lunches, regardless of individual students’ economic situations. And in other cases, some students are eligible for extended periods of time, during which their economic situation might improve.
So what alternatives are there to FRPL? Matt Cohen at the Ohio Department of Education proposed looking at students’ families’ eligibility for TANF, SNAP, Medicaid, and other programs for low income families. He also thought it would be worth looking into students’ families’ incomes, parental education level, and parental occupation. Cohen was looking at obtaining this data for individual students, but for aggregated data, you can get a lot of this on PolicyMap. Census data on PolicyMap shows educational attainment, income levels, and industry people are working in. The SAIPE data (in the education menu under “Student Populations”) has poverty data aggregated to school districts.
Speaking of SAIPE (Small Area Income and Poverty Estimates), Lucinda Dalzell at the Census talked about how SAIPE is put together. Why is SAIPE cool? The regular Census data shows local poverty estimates, but not at the school district level. SAIPE combines ACS data and data from sources like the IRS, SNAP, and SSI to come up with more precise poverty estimates, at the school district level. This presentation gave me a new appreciation for SAIPE.
The highlight of the conference, from an entertainment standpoint, was a session led by Patrick Keaton and Mark Glander, both from the NCES, about the Common Core of Data (CCD), which we recently loaded onto PolicyMap. It’s data on schools and school districts, showing all kinds of important things (like, where they are, and who goes to them). Keaton and Glander discussed what happens when they find errors in the data, values that are missing, or obviously incorrect. The name of their talk was “Dealing With the Misses”. They began by announcing that if you were looking for marital advice, this was not the right session. I’ve spent a lot of time on this data, and it’s good to see how fastidious the people creating it are. And how funny they are. A good time was had by all.
There was even more, like new techniques for calculating graduation and drop-out rates. But I’ll save that for another blog post.