To identify the impact of magnet schools in the Vista Unified School District, KPBS and inewsource needed a quantifiable way to measure what parents of students and teachers were saying: Magnet schools have disproportionately shifted the racial makeup of schools across the district.
Through anecdotal examples, they claimed that magnet schools have increased racial disparity within the district, particularly among Latino and white students.
We used publicly available data to help answer these questions. And as with any significant data reporting, for complete transparency and disclosure, we’re sharing our process so you can understand how we came to our conclusions.
What data was used to produce this analysis?
To appropriately compare individual schools by race, type and grade, and whether those reflected the communities in which they were located, we needed to gather data sets from state and federal sources. Those databases included:
- California Department of Education individual school enrollment data by racial/ethnic designation, gender and grade (school years 2010-2011 through 2016-2017).
- California Department of Education individual school free and reduced price meals data, by racial/ethnic designation, gender and grade (school years 2010-2011 through 2016-2017).
- U.S. Census Bureau five-year American Community Survey racial breakdowns by census tract.
How was the data normalized to combine and compare individual schools?
The California Department of Education keeps enrollment data in yearly master files that include each school in the state. Reporters first had to parse out schools within the Vista Unified School District from the more than 8,600 schools across California.
Because the analysis focused on the various types of schools within the district, specifically traditional district schools, charters and magnets, reporters removed schools that did not meet that criteria. For example, we removed non-traditional schools such as those that provide continuing education for adults, or have alternative and independent study curriculum.
This also required us to further classify the types and grade levels of schools. We added variables to each school to identify the overarching type – district, charter or magnet, and then broke those out further into grade levels – elementary, K-8, middle, high and K-12.
The enrollment data also required some cleanup. We combined data for schools where the name or school type changed. For example, Washington Middle is now Vista Innovation and Design Academy.
This data standardization process was done for each school year dating back to 2010.
Because we also wanted to compare school demographics to the surrounding community, we needed to conform the state Education Department’s demographic data to match that from the U.S. Census Bureau. Our final demographic breakdowns were based on the Census Bureau’s classifications:
- Not Reported.
- American Indian/Alaska Native, not Hispanic.
- Asian, not Hispanic.
- Pacific Islander, not Hispanic.
- African-American, not Hispanic.
- White, not Hispanic.
- Two or more races, not Hispanic.
The Education Department’s data included these breakdowns but also had a category for Filipino. We merged that into the Asian category.
This same data standardization model was followed for California’s breakdown of free and reduced price meals, which we used to better inform our reporting. The data showed a strong correlation between the share of Latino and white students by school, whereas schools with higher percentages of Latino students had a greater share of students eligible for free and reduced lunches.
How did you calculate the share of each racial group within a school?
We calculated the share, or percent of students by race in each school, by dividing the total number of students in each demographic group by the total number of students enrolled in each school. We did this for each school dating back to 2010.
At its core, this analysis showed us how school demographics changed from 2010 to 2017. This calculation was done for individual schools and by the total student population within the district. We used the total district student population breakdowns as our benchmark, a standard method used by courts to determine civil rights cases and recommended by academic researchers who focus on school segregation.
From there, we were able to compare the shift within individual schools, types of schools and various grade levels. This formed the foundation of our reporting.
How did you compare school demographics to the surrounding community?
To compare schools to the surrounding community, reporters first plotted the location of each school on a map, joining that information with the demographics of the census tract in which it was located. A census tract is roughly equivalent to the size of a neighborhood and encompases a population between 2,500 and 8,000 people. The size of each tract is dependent upon the density of the population.
We used the five-year American Community Survey data, which was released in 2016, because it is the most accurate survey of community demographics as compared to the 2010 census, which is nearly 10 years old.
But because Vista Unified allows students from outside the district to attend its schools, we used the census data as a general guide for community demographic comparisons. This method helped guide our reporting to identify schools that stood out. It also helped us understand the overall shifting demographics of the community.
We’ll let you know when big things happen.