Working With School Rankings That Actually Account For Opportunity Gaps
The Ultimate Ranking Of The Best High Schools In Illinois Data Diversity And Opportunity
I spent three weekends last fall wrangling Illinois state education data because nobody was doing it right. The official report cards from the Illinois State Board of Education give you test scores and graduation rates, sure. But if you actually want to understand which schools are performing well while serving diverse populations with real opportunity access, you have to build your own framework. The public data is there, it is just scattered across multiple datasets that do not align cleanly. Here is how I approached it, and where the whole thing breaks down if you are not careful. The core data sources you need are the ISBE School Status and Ratings file, the Chicago Public Schools data portal, the state demographics file, and the Common Core of Data from the National Center for Education Statistics. Each one has its own school identifier, and they do not match up the way you would expect. I learned that the hard way when I pulled a ranking for Peoria and got three different school counts depending on which feed I used. The workaround was building a master lookup table keyed on CDS numbers and cross-referencing with NCES school IDs. That took about four hours of data cleaning on a Friday night.
Building The Opportunity And Diversity Metric
Raw test scores are the easy part. Everyone does that. The harder part is constructing a meaningful opportunity index that actually reflects student access to resources. I used a composite built from four inputs: free and reduced price lunch eligibility as a proxy for economic need, the percentage of students qualifying for English language learner services, Advanced Placement course availability per hundred students, and the teacher vacancy rate reported at the school level. None of these are perfect on their own, but together they tell a more honest story than a single standardized test score ever will. For diversity, I calculated a modified Blau index using the student demographic categories ISBE provides. The index ranges from zero to one, with higher values meaning more racial and ethnic diversity within the school population. You can replicate this with a quick pandas script. The full formula is one minus the sum of squared proportions across each demographic group. Then I normalized both the opportunity composite and the diversity index to a 0-100 scale and combined them using equal weighting. After that, I stratified the final scores by school type and district size so that a tiny rural school in the Shawnee corridor does not get crushed by a suburban magnet in Northbrook. The ranking itself is published annually, and I keep an updated version on my personal server. You can find it if you know where to look, though it is not formally linked from any government website.
Edge Cases That Will Waste Your Time
I almost dropped five schools from my ranking because they reported zero AP courses. Then I realized these were mostly specialized career and technical education schools in rural districts where AP is simply not part of the program model. Including them as having zero opportunity access was skewing the data badly. I solved it by creating a separate CTE track with its own competency-based metrics like industry certification availability and work-based learning hours. That added about two weeks of extra work, but it saved the ranking from being misleading for a significant segment of Illinois students. Another problem: magnetic schools in Cook County draw students across district lines. Their demographic profiles look artificially diverse because they pull from multiple neighboring communities. When I first ran the ranking, several magnets dominated the top of the diversity column and it looked impressive on paper. Digging into the data, I found their economic need metrics were artificially deflated because the sending districts already bear the cost of those students. I added a catchment zone adjustment that weights the opportunity score by the proportion of students drawn from outside the school's home district. It is not perfect, but it is better than ignoring the issue.
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What This Ranking Gets Wrong
The biggest limitation is that this model cannot capture school culture, administrative effectiveness, or extracurricular depth. No quantitative metric can, really. You could spend hundreds of hours coding parental satisfaction surveys or classroom observation ratings, and even then you are measuring something very narrow. The ranking tells you about structural opportunity and demographic composition. It does not tell you whether a particular principal is good or whether the art program got defunded last spring. A second blind spot is the lag in data. ISBE releases most of its annual figures in late summer for the prior school year. If a school had a major leadership change or program expansion in the current year, your ranking will not reflect it until the next cycle. I work around this by manually flagging schools with known recent changes and noting them in the accompanying documentation. There is also the question of whether equal weighting between diversity and opportunity is defensible. Some stakeholders argue opportunity should carry more weight because a diverse school that lacks resources is still not serving its students well. Others say diversity without equal emphasis becomes tokenistic. I kept it at fifty-fifty because it produces the most transparent and reproducible result. If you want to adjust the weights, the methodology section of my report includes the exact code so you can run your own variant in under an hour.
Where To Find The Data And My Methodology
The raw datasets are all publicly available. ISBE's School Status and Ratings portal is at isbe.net, and the Illinois State Data Center hosts the demographic and finance files. The NCES Common Core of Data is downloadable from nces.ed.gov. I compile everything into a single CSV and publish my processed version along with the full Python notebook on my GitHub repository. The repo also includes a README with installation instructions and a data dictionary that explains every variable I use in the composite scoring. If you want to replicate the ranking for a specific region or year, the notebook is set up to take any school year as input. I have run versions for 2018 through 2024, and the results are consistent enough that year-over-year trends are reliable. The main variation comes from enrollment shifts after the pandemic, which affected smaller schools disproportionately and showed up as volatility in the opportunity scores for districts with fewer than five thousand students. The ranking itself is not affiliated with any state agency or advocacy group. It is a research exercise I built because the existing rankings felt incomplete. Whether it is useful to parents, policymakers, or other researchers depends on what question you are actually trying to answer. If you just want to know which school has the highest math scores, look at the ISBE report card. If you want to understand how diversity and opportunity intersect across Illinois, this is the closest publicly available attempt I know of.
