I have written a few posts discussing descriptive analyses of evaluation of National Standards for New Zealand primary schools.The data for roughly half of the schools was made available by the media, but the full version of the dataset is provided in a single-school basis. In the page for a given school there may be link to a PDF file with the information on standards sent by the school to the Ministry of Education.
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Category: rblogs (Page 9 of 22)
Eric and I have been exchanging emails about potential analyses for the school data and he published a first draft model in Offsetting Behaviour. I have kept on doing mostly data exploration while we get a definitive full dataset, and looking at some of the pictures I thought we could present a model with fewer predictors.
The starting point is the standards
dataset I created in the previous post:
I like the idea of having data on school performance, not to directly rank schools—hard, to say the least, at this stage—but because we can start having a look at the factors influencing test results. I imagine the opportunity in the not so distant future to run hierarchical models combining Ministry of Education data with Census/Statistics New Zealand data.
At the same time, there is the temptation to come up with very simple analyses that would make appealing newspaper headlines. I’ll read the data and create a headline and then I’ll move to something that, personally, seems more important. In my previous post I combined the national standards for around 1,000 schools with decile information to create the standards.csv file.
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Oddities tend to jump out when one uses software in a daily basis. The situation is even clearer when using software for teaching: many more people looking at it with fresh eyes.
Let’s say that we are fitting a simple linear model and we use the summary function, then POW! i- one gets all sorts of stars next to each of the coefficients and ii- some tiny p-values with lots of digits. Since immemorial times (childcare, at least) we got star stickers when doing a good job and here we have R doing the same. It is possible to remove the stars, I know, but the default is the subject of this post.
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This is one of those times of the year: struggling to keep the head above the water, roughly one month before the last lecture of the semester. On top trying to squeeze trips, meetings and presentations in between while dealing with man flu.
- Creating a list of introductory-level Bayesian blogs. Some suggestions in the comments: Probably Overthinking It, Maximum Entropy & Deus Dia Pente.
- Materials for Statistical Modelling in Stata. HT: Christophe Lalanne.
- That damned R-squared! understanding through simulation by Arthur Charpentier.
- Strange headlines: ‘Livestock Improvement Corporation offers semen credit in wake of ‘hairy calves’ genetic defect’. HT: Mem Sommerville.
- Attention Deficit Disorder sufferers have moved, at least for a while, from talking about Julia to talking about Stan.
- I bought a copy of Wes McKinney’s Python for Data Analysis. Worth the price.