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Benjamin Lauderdale proslijedio/la je Tweet
New w/
@j_kalla in APSR@APSAtweets: field experiments on reducing exclusionary attitudes towards unauthorized immigrants and transgender people. https://www.ocf.berkeley.edu/~broockma/kalla_broockman_reducing_exclusionary_attitudes.pdf … A few key findings: (1/7)pic.twitter.com/kyCF609KLu
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Benjamin Lauderdale proslijedio/la je Tweet
A tale of two different political cultures: In the US cabinet members are becoming ancient, while they are getting younger and younger in Denmark.pic.twitter.com/34paUVx3PU
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The centre of this colour scale is presumably programmed to drift upwards over time to help
@United upper management hit their customer satisfaction targets.pic.twitter.com/Nasm4iz38d
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Benjamin Lauderdale proslijedio/la je Tweet
Nice to see an MP citing the great work on Brexit by colleagues from
@alanjrenwick and@ConUnit_UCL. I agree that some attempt to reach common ground earlier in the process could have worked. By the 2019 election, electorate was polarised leaving only hard choices for labourhttps://twitter.com/peterkyle/status/1206994623899545601 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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There may have been a substantial error in the national point prediction, but we did very well on our seat-level predicted probability calibration and interval coverage, which I hope at least makes
@d_spiegel happy, if no one else.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
We have produced a more detailed series of analysis of the performance of the YouGov MRP for the 2019 UK general election. Apologies for the slide deck format, prose to come in the new year. http://benjaminlauderdale.net/files/blog/2019_YouGov_MRP_Initial_Assessment.pdf …
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It is also not even the correct naive reading of those numbers, as the Con-Lab margin change is nearly identical!
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Benjamin Lauderdale proslijedio/la je Tweet
You just *knew* it was going to happen: I wrote on the link between constituency level rainfall and turnout in this electionhttps://medium.com/@chrishanretty/rainfall-and-the-2019-election-a396b56302ba …
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Whether we asked voting intention too early or of the wrong people is an important difference for figuring out how to do better next time! (17/17)
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One possibility is the Brexit party overprediction was tactical voting, and we will see lower support when we ask the same people how they actually voted. Another possibility is that we had a sample composition problem. (16/n)
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These will tell us if there was more late swing than even what we observed in the day between our public release and the election as well as providing information about what happened with the Brexit party (15/n)
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A critical piece of evidence we will be looking at in the coming weeks are the recontact surveys of respondents to our pre-election surveys… (14/n)
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There is probably more that we will find that went wrong in the details, but some late swing and a specific problem with Brexit party are what jump out in the data we have now. (13/n)
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This meant that a less than 1% miss on the national Brexit Party translated to overstatement of Brexit Party vote by far more than that in the key marginal seats that swung Tory. (12/n)pic.twitter.com/SHiZVzMrOb
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Given that we predicted Brexit Party 2.9% vs 2.1% actual, how is it possible that this explains so many seat misses? The answer is that the Brexit Party had support heavily in Con-Lab marginal seats held by Labour. (11/n)
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Even Con 448 is still is too low on Con seats by 16. It appears that much of this is attributable to the Brexit Party coming in lower than we predicted. (10/n)
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We have now made a report on these available. https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/udkj77111h/MRP_Tables_2019_Election_Public_Release%20(11).pdf … It looks like we were a bit unlucky in the timing of our pre-scheduled final release, which happened to show the lowest Con seat total of any estimate we produced. (9/n)
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First, for various reasons, our final release was Tuesday at 10pm, using data up to Tuesday at 6am. We produced estimates for a client at 9pm on Wednesday, using exactly the same model, with data to 8am Wednesday, which moved up to 348 Con seats. (8/n)
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