Responding to a recent Detroit Free Press series, the CEO of National Heritage Academy (NHA) had this to say:
Apples-to-apples comparisons to neighboring district schools help explain the appeal. More than 90% of NHA’s Michigan schools had a higher overall proficiency rate than neighboring district schools on the Michigan Educational Assessment Program.
Which raises interesting questions that no one else has documented because life is short and math is hard. In this post I’m going to compare NHA schools performance with the nearest neighbor for 5th grade MEAP results in Math, Reading, and Science.
I’ve created a custom Google map to document the locations (be sure to click on the icons to see the schools results, demography, and socio-economic indicators).
Below is the analysis, but watch out, there will be MATH!
In my last post, I looked at factors impacting Michigan primary school test performance. In this post I look at high school performance using the Michigan Merit Examination (MME) 11th grade results to determine what is the role of race, location, poverty, and school type (public, not for profit, and for profit charter). In all of the exam subjects (Math, Reading, Science, Social Studies, and Writing) for profit charters out perform not for profit charters and public schools at a 95%, or higher, confidence level. Not for profit charter schools typically outperform public schools at lower confidence level than their for profit comparisons because their are so few in the data (only 16 as opposed to 79). The higher the ratio of Asian and female students, the better the school performs on the exams.
One of the advantages of a regression analysis is that it can be used to forecast what the result should be based on the factors involved. Some schools will vary more (above and below) the forecast. I hope to illuminate that distinction using an online map in a future post.
The Detroit Free Press recently ran an interesting series of stories about charter schools in Michigan which made me wonder if I could use regression analysis to partition the variation in test results on the basis of race, poverty, location, and type of school. This analysis uses MEAP scores for the 5th grade.
tl;dr — For profit charter schools generally produce worse results than public or not for profit charters while not for profit charters produce better results.
If you’re thinking “eeeeeeeeeehhhhhhhwwwwwwwww math”, I hope to soon incorporate the results into an on line map to make it easy to look at the results for particular schools .
I’ve been working for some time on a Perl module to parse XBRL, a complex XML based format for reporting financial information. The US SEC requires publicly traded firms to provide their financial reports in XBRL. The goal of the Perl module is to provide a clear and easy to use interface to extract data from an XBRL instance and use it for another purpose. In the initial release, the module features a function to render the XBRL instance into a very basic HTML document. Because the XBRL standard is large and complex, support for its features will be added over subsequent releases.
Today’s announcement from HP that WebOS will be set free as an Open Source project opens up room for some interesting changes in the mobile landscape. Both CNET’s Stephen Shankland and The VAR Guy think nothing much will come of this move. As both a long time Open Source zealot and a mobile developer, I think there is more there there than they do.
In a previous post, I showed a pretty simple regression analysis of housing prices and house size for my Zip code. The zip code was used as an easy way to include location in the output. Using PostGIS and geographic data from the City of Sacramento, this post will show a regression analysis ( using the R statistical programming project) using the city’s designated neighborhoods. The raw data real estate data comes from the Sacramento Bee. After describing the model, I’ll apply it the last few months of home sales (not used in developing the model), and see how well it does at predicting results. Read more…