Grasping the immigration numbers

Written by Web News Editor on . Posted in Features

Immigration as defined by the public isn’t necessarily the same as defined by the official statistics, according to an Ipsos Mori survey on behalf of the Migration Observatory at Oxford University.
 
Thinking Behind the Numbers says ’members of the public and the government may be thinking about different things even when both are talking about “immigration”. Categories such as temporary immigrants and students loom large in official statistics, but less than a third of the public has in mind either of these categories when thinking about immigrants.’
 
People feel most strongly about low-skilled migrants and asylum seekers but the former often come from within the European Union, exercising a legal right, and the latter – a small proportion of the total flow – have a right under international agreements to have their claim for asylum assessed.
 
One of the largest categories of immigrants, students, seem to cause the public the least concern.
 

Good workmen don't blame their tools (so don't blame the stats)

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There are stats – data about the economy, child poverty levels etc  – and then there is Statistics, the techniques used by statisticians to arrive at those figures.
 
Statistics is a toolkit. It helps us to understand the world around us and the society we live in. But ‘statistics’ is often taken to mean ‘my facts’. And when my facts are controversial (sometimes wrong) the other side has a go at ‘statistics’.
 
In a typical debate,  there is usually a point where opponents start picking holes in the other side’s numbers.  As their tenuous grasp on the numbers slips, ‘statistics’ become the common enemy.  Pundits and presenters then talk about statistical ‘fog’, statistical ‘spin’, ‘magic’, and so on. They are implying that the stats are inherently misleading, divisive, made up and difficult to grasp.
 
Confidence around stats seems to be at the heart of the problem. …not distrust of the production of stats per se but mistrust of the handling and reporting of the stats and lack of confidence that users and communicators of stats really know their stuff!
 
If there were more stats users and communicators (and listeners,viewers and readers) with the understanding and confidence to make sense of stats so they have an independent sense of when numbers are being used well or badly……debate would be far less likely to turn into a verbal brawl…and if and when it did, stats would be less of a ’whipping boy’ and more ’the good guy’ who intervenes and refocuses the discussion on the evidence at hand.
 

Gang border data and new approaches to policing

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Statistics is all about measuring and finding patterns in data which offer up information and solutions.  It’s a ‘toolbox’ – a range of tools such as distributions, equations and models – which can be used to work things out in different arenas. It’s a matter of picking the right tool.
 
The Metro today ran a short piece ‘Gangs really do act like animals’ on a just-published, University of California study has used a tool – Lotka-Volterra equations (also known as predator-prey equations) - used since the 1930s by ecologists to model the way bees, chimps and lions compete for resources- to get a grip on gang crime. UCLA researchers have found links between between the way hunting animals map out their territory and the behaviour of street gangs especially in terms of their “range”
 
By modelling police record data  and other data on the gang’s bases,  researchers have worked out that 99.8% of crimes could be expected to occur within one mile of the borders between rival gangs (nearly 60% of crimes within 1/5 of a mile, nearly 90% within 2/5 of a mile). 
 
What solutions do these new findings offer?  researchers have been able to identify the real boundaries between gangs….ones that don’t just follow streets but run through backyards and alleyways.  This new knowledge wil help the police  to focus their efforts, allocate resources and achieve better outcomes. And incidentally, the study also offers an evidence-informed warning …. gut instinct might suggest that wandering into the heart of known gang territory would be the most dangerous thing to do….but most crime, it would appear, actually happens on or near the borders between rival gangs.
 

Evidence comes with numbers

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Politicians, including prime ministers, don’t always – if ever – study the available evidence before they make policy. This week’s kite flying by David Cameron about welfare was an example. There’s a lot of modelling and empirical data on the interaction of the benefits system, family size, work and so on, and it’s worth looking at.
 
But lately more voices are insisting policy interventions are put on trial beforehand, and proposals subjected to rigorous assessment before being rolled out. The Alliance for Useful Evidence is doing great work – it is co-funded by Nesta, the Economic & Social Research Council and the Big Lottery and the RSS has been an enthusiastic supporter.
 
A pre-condition for good evidence is usually robust numbers, and that requires the evidence gatherers and analysts to be able to handle and understand statistics. Nesta argues ’for rigour in the generation of evidence’. That will often mean applying quantitative methods. That message is starting to feed back to social science teachers, and students intending to study sociology, social policy and politics. They need to have a capacity to gather and make sense of data.
 
Professor John Macinnes of the University of Edinburgh, a member of the RSS getstats campaign board, is leading the ESRC’s initiative and the Higher Education Academy is on board, as teachers build up a bank of tools and presentations to help embed stats in social research, such as the Destress collection.
 
Nesta says, and we agree, that alongside the ’Geek Manifesto’ (there’s an event at Nesta next week), we need a geek manifesto for the social sciences.
 

Cross your teas

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NHS Choices looked at the health conditions and life styles of men in Scotland over three decades. It’s a fascinating piece of work but more suggestive than definitive. Researchers found that men reporting drinking a large amount of tea each day (more than eight cups) were also found in the NHS records as having contracted prostate cancer. The study does not show how serious their cancer (many men with prostate cancer die of other causes) nor can it check whether the men have been big tea drinkers all their lives; the study did try to screen out other lifestyle factors such as drinking alcohol.
 
Media reports said bluntly heavy tea drinkers are 50 per cent more likely to develop prostate cancer. But relatively few men get prostate cancer, whether they drink tea or don’t. The study found that overall 6.4 per cent of those who drank the most tea developed prostate cancer during the study period, compared with 4.6 per cent of those who drank the least. Those drinking a moderate level of four to six cups of tea a day were not at any increased risk compared with those who drank the least.
 
We all know that you can’t get a sexy headline out of caveats and qualifications but isn’t it time the media – especially in the age of Leveson – made a bit more of an effort to avoid empty and misleading sensationalism?
 

Oh no, penalties again

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Euro 2012? ….England may be out but the statistics were not entirely out. In Ian McHale’s model, penalties were assumed to be 50-50. And as Roy Hodgson told BBC Sport earlier today: “A player’s reputation should not be forged on a penalty shoot-out, their reputation should be forged on the four games and in those four games we have not lost.”.
 
To find out more about a stats approach to penalty taking…and why, for example, it might help a penalty taker to look at a clock before he kicks the ball, read ’How to take a penalty: the hidden mathematics of sport’  by Rob Eastaway and John Haigh (republished as ‘Beating the Odds: the hidden maths of sport’). See also ‘What’s the point of probability?’ which gives us a few tips on penalty-taking.
 

Data revolution depends on stats understanding

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Underpinning the government’s plans for more open data are two big assumptions. Both are about the public. One is that people will take a positive view of data sharing and see the advantages in departments and agencies mixing and matching the information they hold.
 
The other is that people will make sense of what is released. That they will take in the numbers and percentages and understand what they are being told. Though Cabinet Office minister Francis Maude did not use the phrase when he published the white paper on open data, he clearly has in mind the ‘armchair auditors’ painted by his colleague Eric Pickles – citizens able to hold government more closely to account thanks to the voluminous information about the state that is becoming available.
 
But our colleague, the stats postdoctoral research fellow at the University of Cambridge  Robin Evans, sounds a warning. (he blogs at  itsastatlife.blogspot.com ) Say doctors are now going to release information about their patients’ cancer treatments. From it you could easily construct a rough and ready performance table showing the proportion of patients who recover. A fair appraisal would, among other things, ask how many cancer cases a doctor sees, and grade them according to severity - but that complicates the picture. The trouble is, that without the caveats, the open data result may be misleading.
 
Who’s going to insert the caution? Evans says there’s a challenge here for the statistically literate. ‘We have to ensure that naive and misleading interpretations of data are not allowed to predominate.’
 
But the white paper does not have much to say about who might step up and educate the public, shooting down erroneous interpretations and inserting qualifications into the picture.
 

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