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Archive for May, 2012

Available at its ‘Better Life Index‘ site, this index is part of the move, in the spirit of the Stiglitz commission, to broaden evaluations of economic performance beyond GDP, which Robert Kennedy famously commented

does not allow for the health of our children, the quality of their education or the joy of their play. It does not include the beauty of our poetry or the strength of our marriages, the intelligence of our public debate or the integrity of our public officials. It measures neither our wit nor our courage, neither our wisdom nor our learning, neither our compassion nor our devotion to our country, it measures everything in short, except that which makes life worthwhile

The OECD Better Life index measures few of these things either, but it does have a variety of indicators, and what is especially noteworthy from a QM teaching perspective, it allows individuals to weight each indicator according to their own preferences and compare countries on that basis.

The results are of limited use for analysis, but they could be a good way to teach about weights, or the issues involved in constructing summary indicators.

The website is interactive and has a range of information about the data used to construct each element of the index, country rankings on each of these, and discussion of the nature of measurement as well as the data itself downloadable as excel files.

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I’ve just read David Hand’s Statistics a Very Short Introduction (2008, Oxford UP)
Statistics introductions, especially when they are not subject specific, tend to be uninspiring and formula driven. This is not: a good short read that helps give students context to what they might do with quants.

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Richard Alldritt of the UKSA has an excellent blog entry ‘Numbers are Not Enough’ at the RSS’s (beta) StatsUserNet site on the need to have a good grasp of the ‘metadata’ when using official statistics, and even more so, the ‘open data’ that will increasingly be released by central and local authorities.

Dilnot and Blastland have an excellent chapter in Tiger That Isn´t about the way measurement error, gaming, definitions and the whole process of the social construction of data conspire to transform data from what we might think is a transparent representation of the obvious into anything but. They put it extremely well (p. 15; 158):

“we can establish a simple rule. If it has been counted it has been defined, and that will almost always have meant squeezing reality into boxes that don´t fit…The idealised perception of where numbers come from is that someone measures something, the figure is accurate and goes straight in the database. That is about as far from the truth as it is possible to get.”

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Stat-JR

The Bristol Centre for Multilevel Modelling has released the beta version of Stat-JR. The software has two features that may make it attractive for teaching.

For those teaching advanced techniques the software can sit on top of other packages (such as SPSS or Stata) and use their features within a single command language (so that students do not need to learn whole new packages in order to execute new techniques).

However the (for me) really exciting bit of this software is the ‘ebook’ interface that it offers. It is possible to author ebooks with dynamic pages populated from datasets.The dynamic ebook page receives instructions from a reader which it then executes and posts results back to the page. This makes it a useful tool in building teaching materials, since the Stat-JR ebook can sit on top of spss or other applications. Interactive learning materials can be updated with later releases of data sets or releases of statistics software, or different examples suited to different discipline backgrounds, with much less effort needed to tailor materials to different audiences or take account of other changes.

From the CMLM message:

At present whilst the software is a beta release we are only distributing it in (renewable) 30-day limited licence form but it is our intention after a period to release fully when, as with our MLwiN software, Stat-JR will be free to UK academics with potentially a small one off fee to non-academics and non-UK users. Note that currently, as with MLwiN, Stat-JR is a Microsoft Windows only piece of software.
If you would like to test out the software and give us feedback then
for more details on the software, its documentation and how you can download it please visit http://www.bristol.ac.uk/cmm/research/estat/downloads/index.html and fill in a request form for a download.
Best wishes,

The Stat-JR team.

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I find it useful to get students thinking abut quantitative evidence by examining how ignorant we are of the order of magnitude of numbers that nevertheless feature in highly visible public debates. Most probably have some idea that the incarceration rate is higher in the US than UK. But how many people does either jurisdiction lock up, and what ‘should’ the rate be?
PLenty of good data at

http://www.prisonstudies.org/info/worldbrief/

The US locks up more people per head than anywhere else, with an incarceration rate of 0.73%. It has 5% of the world’s population but about 25% of the world’s prisoners. With 2.27m it has almost as many as the combined total for Russia and China )

And which part of the UK has fewer prisoners per head than any other? Step forward… Northern Ireland with a rate of 99 per 100,000 pop, compared to 155 for Eng & Wales and 157 for Scotland.

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Daniel Kahneman’s Thinking Fast and Slow has many relevant ideas for statistics teaching, regardless of how far one agrees or not with the details of ‘Prospect Theory’, behavioral economics or all of Kahneman’s arguments about the psychology of cognition.
The most important insight is his (to my mind convincing) experimental demonstrations of the manifold forms of the ‘What You See Is All There Is’ bias in cognition, together with a plausible account of its evolutionary origins. Quantitative, mostly statistical, evidence that goes beyond individual observation, together with effortful ‘System 2’ logical thought that follows axioms of probability or arithmetical calculation, are the only possible correctives to WYSIATI. This ought to be a stronger selling point for statistics. ‘Fear of Stats’ probably has an element of ‘discomfort of undermining cherished intuitions’ to it.
His account of regression to the mean and pilot instructors (picked up by Dilnot and Blastland in Tiger That Isnt) is probably a point to start with in introducing statistics. I also like the way he introduces the idea of correlation consequent to that of regression: reversing the order of most statistics texts.

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I’m currently putting together a course on numerical and statistical literacy aimed at all undergraduates at Edinburgh. This is a challenge, not least because it means that I cannot assume any particular substantive academic discipline as a context. I’ve also been going round in a few circles over what to go into first. Start with randomness and then the idea of a variable distribution and then correlation? Or start with description and the nature of variables? Text for the course will be Dilnot and Blastland’s ‘Tiger that Isnt’. The ‘learning outcomes’ I’ve come up with are below: all suggestions about additions, deletions or amendments gratefully received… Currently it is a very long list: probably too long, but what to leave out??

Understand what a variable is, what is meant by its distribution, and some of the ways in which the latter can be described and summarised.
Recognise and carry out simple manipulation of proportions, fractions, decimals, and percentages.
Understand rates and rates of change, and their expression by logarithms.
Use procedures of informal estimation to check the orders of magnitude of quantities used in reports (including academic or scientific output, policy documents or the mass media) and to avoid spurious accuracy.
Understand the meaning of randomness and of the independence of events or states.
Recognise a ‘Normal’ or Gaussian distribution.

Understand probability and risk as ways of measuring uncertainty.
Undertake simple calculations of cumulative and conditional probability
Understand the distinction between absolute and relative risks, and perform simple calculations of risk using natural frequencies and Bayes rule.
Understand the concept of correlation and its distinction from causation.
Know how to read, interpret, produce and present data in the form of contingency tables.

Understand the difference between experiment and observation
Understand the difference between observational and experimental control
Understand what is meant by a random sample and sampling fluctuation
Understand what a confidence interval is and how it is expressed
The distinction between significance and substance
The concept of regression to the mean and its implications

Understand how data can be visualised, including bar charts, histograms, box plots, scatterplots and Venn diagrams.
Be able to use Excel to record, store, manipulate and present numerical data.

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