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What is the YouGov model which estimates UK PM May could lose majority?
May 31, 2017 / 10:38 AM / 7 months ago

What is the YouGov model which estimates UK PM May could lose majority?

    LONDON, May 31 (Reuters) - Prime Minister Theresa May could
lose control of parliament in Britain's June 8 election,
according to a projection by polling company YouGov, raising the
prospect of political deadlock just as formal Brexit talks
begin.
    What is the YouGov model, how does it work and what does is
predict? 
    
    WHAT DOES THE YOUGOV MODEL PREDICT?
    
 Party                    YouGov      2015
                          estimates   election
 Conservative Party       310         331
 Labour Party             257         232
 SNP                      50          56
 Northern Irish parties   18          18
 Liberal Democrats        10          8
 Plaid Cymru              3           3
 Green                    1           1
 Other                    1           0
 UKIP                     0           1
 
    HOW DOES IT WORK? 
    Every day YouGov interviews approximately 7,000 panellists
about their voting intentions for the June 8 election. About 75
voters per constituency per week are asked their opinions. Over
the course of a week, data are collected from around 50,000
panellists. 
    That data is fed into YouGov's model, which factors in
interview date, constituency, voter demographics, past voting
behaviour, and other respondent profile variables. The model
estimates the probability that a voter with specified
characteristics will vote Conservative, Labour, or some other
party.
    Then the model uses data from the UK Office of National
Statistics, the British Election Study, and past election
results, to estimate the number of each type of voter in each
constituency.
    "Combining the model probabilities and estimated census
counts allows YouGov to produce a fairly accurate estimate of
the number of voters in each constituency intending to vote for
a party on each day," YouGov said.
    
    WHAT IS IT CALLED?
    YouGov is using a new technique called Multilevel Regression
and Post-stratification ('MRP' for short) to produce estimates
for small geographies (local authorities for the EU referendum,
states in the 2016 American Presidential election, and
parliamentary constituencies for the 2017 General Election).
    
    WHAT ARE THE LIMITS OF THIS MODEL?
    - the samples in each constituency are too small to be
reliable by themselves and are subject to more than just
sampling error. To compensate for small sample sizes, YouGov
relies on a model that pools data across constituencies. This
uses data from panellists who live in other constituencies to
augment the small number of actual interviews conducted in a
constituency. 
    - The model is based on the fact that people with similar
characteristics tend to vote similarly, but not identically,
regardless of where they reside, YouGov said.
    - Using MRP, YouGov have classified constituencies as safe,
likely, or leaning to a party or as a toss-up. 
    
    HAS IT BEEN USED BEFORE?
    Yes. YouGov said its MRP model in the 2016 EU referendum
consistently showed that more voters favoured leave than remain,
and that Hillary Clinton would win the popular vote in the 2016
US Presidential election by a narrow margin, but that midwestern
battleground states were too close to call.
   
    WHO MADE THE MODEL?
    The model was developed by Professor Ben Lauderdale of the
London School of Economics in conjunction with YouGov's Data
Science team, headed by Doug Rivers of Stanford University,
YouGov said.
    The data are streamed directly from YouGov's survey system
to its database. From there, the models are fit using
Hamiltonian Monte Carlo with the open source software Stan. 
    Stan was developed at Columbia University by Andrew Gelman
and his colleagues, with support from YouGov and other
organisations. 
    YouGov will be updating the model estimates on a daily
basis.

 (Reporting by Paul Sandle and Robin Gillham; Editing by Guy
Faulconbridge and Raissa Kasolowsky)
  

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