This morning Radio New Zealand covered a story (audio) where Tomatoes New Zealand (TNZ, the growers association) was asking the Government to introduce compulsory labeling for irradiated products (namely imported Australian tomatoes), stating that consumers deserve an informed choice (TNZ Press Release). Two points that I think merit attention: Continue reading
Category: policy (Page 2 of 3)
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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This week I’ve tried to i-stay mostly in the descriptive statistics realm and ii-surround any simple(istic) models with caveats and pointing that they are very preliminary. We are working with a sample of ~1,000 schools that did reply to Fairfax’s request, while there is a number of schools that either ignored the request or told Fairfax to go and F themselves. Why am I saying this? If one goes and gets a simple table of the number of schools by type and decile there is something quite interesting: we have different percentages for different types of schools represented in the sample and the possibility of bias on the reporting to Fairfax, due to potential low performance (references to datasets correspond to the ones I used in this post):
summary(standards$school.type) # Composite (Year 1-10) Composite (Year 1-15) Contributing (Year 1-6) # 1 29 403 # Full Primary (Year 1-8) Intermediate (year 7 and 8) Restricted Composite (Yr 7-10) # 458 62 1 # Secondary (Year 7-15) # 56
Now let’s compare this number with the school directory:
summary(factor(directory$school.type)) # Composite (Year 1-10) Composite (Year 1-15) Contributing (Year 1-6) # 4 149 775 # Correspondence School Full Primary (Year 1-8) Intermediate (year 7 and 8) # 1 1101 122 #Restricted Composite (Yr 7-10) Secondary (Year 11-15) Secondary (Year 7-10) # 4 2 2 # Secondary (Year 7-15) Secondary (Year 9-15) Special School # 100 238 39 # Teen Parent Unit # 20
As a proportion we are missing more secondary schools. We can use the following code to get an idea of how similar are school types, because the small number of different composite schools is a pain. If
# Performance of Contributing (Year 1-6) and # Full Primary (Year 1-8) looks pretty much the # same. Composites could be safely merged qplot(school.type, reading.OK, data = standards, geom = 'jitter') qplot(school.type, writing.OK, data = standards, geom = 'jitter') qplot(school.type, math.OK, data = standards, geom = 'jitter') # Merging school types and plotting them colored # by decile standards$school.type.4 <- standards$school.type levels(standards$school.type.4) <- c('Composite', 'Composite', 'Primary', 'Primary', 'Intermediate', 'Composite', 'Secondary') qplot(school.type.4, reading.OK, colour = decile, data = standards, geom = 'jitter')
I’m using jittering rather than box and whisker plots to i- depict all the schools and ii- get an idea of the different participation of school types in the dataset. Sigh. Another caveat to add in the discussion.
P.S. 2012-09-27 16:15. Originally I mentioned in this post the lack of secondary schools (Year 9-15) but, well, they are not supposed to be here, because National Standards apply to years 1 to 8 (Thanks to Michael MacAskill for pointing out my error.)
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:
In two previous posts I put together a data set and presented some exploratory data analysis on school achievement for national standards. After those posts I exchanged emails with a few people about the sources of data and Jeremy Greenbrook-Held pointed out Education Counts as a good source of additional variables, including number of teachers per school and proportions for different ethnic groups.
The code below call three files: Directory-Schools-Current.csv, teacher-numbers.csv and SchoolReport_data_distributable.csv, which you can download from the links.
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