In the past, I have calculated UC admittance rates for schools in Los Angeles. I take the UC admit raw numbers, and compare it with the 12th grade enrollment numbers from that year, and publish a percent. I did it for 2015 and 2016.
I’ve been meaning to do an update for the most recent data (2017), but the results always left me feeling a little meh.
One thing I noticed is that sometimes one school would have a strong year with a huge number of acceptances and the next year they would be lower. There was no way to gauge consistency. In addition, the results were rather predictable. The schools in wealthier areas with more privileged students had high admittance rates, while many of the schools in the poorer more disadvantaged neighborhoods had lower admittance rates. (I recognize that those are opposite ideas, but I believe both can be true).
So this year I am mixing it up.
I created a tool to look at admittance rates for the past 4 years, and get a rolling average. This will smooth out the natural ups and downs of classes that are higher or lower, giving us a better picture of the overall admittance rate of a school over four years.
I also created a regression that created a predicted UC acceptance rate. I included a poverty indicator (free or reduced price meal eligibility), english learner population percentage, and ethnicity percentages of the senior class. Then, I found the residual of each school compared to its predicted score. Finally, I then gave them a decile rating (1 through 10) about how they outperformed or underperformed compared to their predicted score. The result is a demographically balanced score – a way to judge whether the percent you are seeing is strong or weak based on their demographic make up. 10 is strongest, 1 is weakest.
In a future post, I will review some of the results, but for now, I am just going to put this out there. To use it, use the drop down menu on the top left to select a school. So here it is:
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