By Minali Aggarwal, Sylvan Zheng, Sol Messing, Dan Frankowski, James Barnes, & the Barometer Team at ACRONYM
Do messages criticizing Donald Trump’s performance on Covid-19 reduce his support among swing voters? What if the message comes from a conservative personality, like Tucker Carlson?
It was our job at ACRONYM to answer questions like these during the lead up to the 2020 election. Doing so would allow us to spend more efficiently: to put more money behind the most effective ads, and “first do no harm” by taking down ads that cause backlash — increasing Trump’s approval. …
It’s becoming clear that the 2020 polls underestimated Trump’s support by anywhere from a 3–8 point margin depending on your accounting–a significantly worse miss than in 2016, when state polls were off but the national polls did relatively well.
In fact, this year we were better off using projections based on past vote history in each state to predict how things would go in battleground states, as I’ll show below.
Sol Messing is Chief Scientist at Acronym, Affiliated Researcher at Georgetown.
According to the latest polling research, Trump’s chances of hanging on to power beyond 2020 look pretty dismal. Nate Cohn published an impressive battleground poll from New York Times/Sienna showing Biden ahead of Trump by at least six points in pivotal states. The Economist’s forecast, powered by Elliott Morris and Andrew Gelman, is suggesting Biden is likely to get 64% of electoral college votes, and that if the election were held 100 times Biden would win 90 times to Trump’s 10.
My last post railed against the bad visualizations that people often use to plot quantitive data by groups, and pitted pie charts, bar charts and dot plots against each other for two visualization tasks. Dot plots came out on top. I argued that this is because humans are good at the cognitive task of comparing position along a common scale, compared to making judgements about length, area, shading, direction, angle, volume, curvature, etc. — a finding credited to Cleveland and McGill. I enjoyed writing it and people seemed to like it, so I’m continuing my visualization series with the scatterplot.
Inspired by Donald Trump’s shocking victory over Hillary Clinton in the 2016 general election, Sean Westwood, Yphtach Lelkes and I set out to interrogate the question of whether elecion forecasts — particularly probablistic forecasts — might have helped to create a false sense of confidence in a Clinton victory, and ultimately led many on the left to stay home on election day.
Clinton herself was quoted in New York Magazine after the election:
I had people literally seeking absolution… ‘I’m so sorry I didn’t vote. I didn’t think you needed me.’ I don’t know how we’ll ever calculate how many…
Regression models are a cornerstone of modern social science. They’re at the heart of efforts to estimate causal relationships between variables in a multivariate environment and are the basic building blocks of many machine learning models. Yet social scientists can run into a lot of situations where regression models break.
Famed social psychologist Richard Nisbett recently argued that regression analysis is so misused and misunderstood that analyses based on multiple regression “are often somewhere between meaningless and quite damaging.” …