… according to the 2013 Census figures,
- 51 would be female, 49 male.
- 70 would be European, 14 Maori and 11 Asian.
- 24 would have been born overseas
- 21 would have a tertiary qualification
- 4 would be unemployed.
- 4 would earn over $100,000
… according to the 2013 Census figures,
There has been significant coverage in the press of New Zealand’s slip in the OECD PISA (Programme for International Student Assessment) rankings for mathematics, reading, and science.
We probably should be concerned.
However, today I stumbled across the following chart: in The Economist. Two things about it struck me. Firstly, part of the change (in the mathematics ranking at least) was driven by the addition of three countries/cities which did not participate in the 2006 round: Shanghai, Singapore, and Vietnam. The insertion of these countries is not enough to explain away New Zealand’s apparent drop, but it does move us from a change of down 11 places to a change of down 8 places. Secondly, I found it really hard to see what was going on in this graph. The colour coding does not help, because it reflects geographic location and the data is not grouped on this variable. Most of the emphasis is probably initially on the current ranking which one can easily see by just reading the right-hand ranked list from The Economist’s graphic. However, relative change is less easily discerned. It seems sensible, to me at least, to have a nice graphic that shows the changes as well. So here it is, again just for the mathematics ranking: .
The raw data (entered by me from the graph) has been re-ranked omitting Greece, Israel, and Serbia who did not participate in 2012, and China, Singapore, and Vietnam, who did not participate in 2006. I am happy to supply the R script to anyone who wants to change the spacing – I have run out of interest.
It is also worth noting that these rankings are done on mean scores of samples of pupils. PISA’s own reports have groups of populations that cannot be declared statistically significantly different (if you like to believe in such tests). This may also change the rankings.
Professor Neville Davies, Director of the Royal Statistical Society’s Centre for Statistical Education, and Elliot Lawes, kindly sent me the following links:
Firstly a blog article from the ever-thoughtful Professor David Spiegelhalter: The problems with PISA statistical methods
and secondly, a couple of articles from the Listener, which I believe Julie Middleton has also mentioned in the comments:
Education rankings “flawed” by Catherine Woulfe” and Q&A with Andreas Schieicher also by Catherine Woulfe.
To start with, the noseless guy doesn’t cast a shadow, although the almighty dollar he is holding casts a shadow on the empty air. Perhaps he’s a vampire. Also, the colours in the legend don’t actually match the colours in the graph. And, the graph manages to misrepresent not only the magnitude of the numbers but even their ordering, with the largest layer of the pyramid representing the smallest category. To top it all off, the numbers aren’t even right (or are seriously outdated) — for example, the US Bureau of Labor Statistics Consumer Expenditure Survey reports food expenditure between 12.5% and 13% of household expenditure every year from 2006 to 2012, not the 15% in the graph
Showing what can be done straightforwardly with online data, the site Fbomb.co (possibly NSFW) is a live map of tweets containing what the Broadcasting Standards Authority tells us is the 8th most unacceptable word for NZ. Surprisingly, it was written by a Canadian.
The Business Insider post says
[I]n the time we’ve watched it, it becomes clear that China really is big into Bitcoin relative to the rest of the world.
Below is a brief snapshot of what we saw in which the transactions were dominated by Chinese trading (at other times it’s more even, with more US action).
A big reason for the variation would be time of day, but my map above ran for about 17 hours, and it shows much more US than Chinese bitcoin activity — and even more Australian than Chinese. Longer sampling times are clearly needed to say anything definite.
The map website says “watch the world’s currencies flow into BTC in realtime”, which is the sort of exaggeration that’s unfortunately common with bitcoin enthusiasts. These are exchanges of bitcoin for other currencies: one person gets USD and gives up BTC, the other person gets BTC and gives up USD. The only net flow into (or out of) bitcoin comes from the seller’s profit (or loss) as bitcoin changes in price — and that can be much more easily and accurately estimated from the exchange rates, without needing to track individual transactions.
The Institute for Health Metrics, at my previous university in Seattle, has a new tool for visualising the causes of death and disability across the world with interactive graphics.
This pair of maps is for cancer in women.
The lower map is just cancer deaths per 100,000 women. That’s the easiest sort of number to obtain, but the problem with it is obvious: the orange and red countries are mostly just the places where the female population is older than average.
The upper map is age-standardised deaths per 100,000 women. That is, you take the rate in your country for women of a particular age, say 72 years old , and multiply by the proportion of 72-year olds in the UN’s standard reference population. When you do this for each year of age and add up the results, you get an estimate of what the cancer rate differences really are like, averaged over ages.
The map looks completely different after standardising by age. In particular, there’s a lot less variation between countries. The lowest rate is in Saudi Arabia, which is wealthy enough to afford good medical care but still has low rates of many cancer risk factors in women. The highest rate is Papua New Guinea, which has very high rates of cervical cancer (affecting younger women than many cancers).
With Harvard students Azalea A. Vo and Shashank Sunkavalli, as well as MIT graduate students Zoya Bylinskii and Phillip Isola, the team designed a large-scale study—in the form of an online game—to rigorously measure the memorability of a wide variety of visualizations. They collected more than 5,000 charts and graphics from scientific papers, design blogs, newspapers, and government reports and manually categorized them by a wide range of attributes. Serving them up in brief glimpses—just one second each—to participants via Amazon Mechanical Turk, the researchers tested the influence of features like color, density, and content themes on users’ ability to recognize which ones they had seen before
The researchers talk about what features were present in the more-memorable graphs, which tended to be visually dense and not to be of standard forms.
It’s good to see empirical evaluation of theories about graphics. However, as they admit, ’memorable’ may not be the right criterion. Even if it isn’t ‘memorable’ in the eyeball-bleach sense, memorability may not be a good proxy for informativeness.
Via @BenAtkinsonPhD: a map of heavy metal bands per capita
We’re ahead of the US, though behind the Scandinavian countries, as usual.
(for foreigners: NZ political cliche)
From Andrew Gelman, who is passing along research by some Columbia political scientists, the estimated support, by state, for the Employment Nondiscrimination Act, a gay rights bill that the US Senate will be voting on this Monday.
US Senators are elected by, and theoretically represent, their state as a whole. The bill has majority support in every state, well over 60% in most states. It’s not clear whether it will pass.
Part of the problem is multilevel democracy: to be a Senator, you have to be selected as a candidate as well as winning the election. And the people who vote at the preselection stage (primary elections, in the US) average more extreme than those who vote in the election. The more levels of selection you need, the worse the problem gets: Tim Gowers (prompted by the US government shutdown) does the mathematician thing and derives the extreme case. And the problem is exacerbated by the fact that politicians aren’t as knowledgeable about the views of their electorates as they think are.