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Archive of posts filed under the Political Science category.

Breaking the feedback loop: When people don’t correct their errors

OK, so here’s the pattern: 1. Someone makes a public statement with an error, an error that advances some political or personal agenda. 2. Some other people point out the error. 3. The original author refuses to apologize, or correct the error, or thank people for pointing out the error, and sometimes they don’t even […]

“Older Americans are more worried about coronavirus — unless they’re Republican”

Philip Greengard points us to the above-titled news article by Philip Bump. The article was just fine, a reminder of modern-day political polarization. The only thing that bothered me were the graphs. I redrew them above. Here were the original versions: I see a few problems with these graphs. First, the information is duplicated because […]

100 Things to Know, from Lane Kenworthy

The sociologist has this great post: Here are a hundred things worth knowing about our world and about the United States. Because a picture is worth quite a few words and providing information in graphical form reduces misperceptions, I [Kenworthy] present each of them via a chart, with some accompanying text. This is great stuff. […]

Bernie electability update

The other day we discussed an article, “New research suggests Sanders would drive swing voters to Trump — and need a youth turnout miracle to compensate,” by David Broockman and Joshua Kalla. A commenter pointed us to a post by Seth Ackerman, “Study Showing Bernie Needs Huge Youth Turnout Is Nonsense,” which states: In the […]

So . . . what about that claim that probabilistic election forecasts depress voter turnout?

A colleague pointed me to this article by Sean Westwood, Solomon Messing, and Yphtach Lelkes, “Projecting confidence: How the probabilistic horse race confuses and demobilizes the public,” which begins: Recent years have seen a dramatic change in horserace coverage of elections in the U.S.—shifting focus from late-breaking poll numbers to sophisticated meta-analytic forecasts that emphasize […]

The Great Society, Reagan’s revolution, and generations of presidential voting

> Continuing our walk through the unpublished papers list: This one’s with Yair and Jonathan: We build a model of American presidential voting in which the cumulative impression left by political events determines the preferences of voters. This impression varies by voter, depending on their age at the time the events took place. We find […]

“New research suggests Sanders would drive swing voters to Trump — and need a youth turnout miracle to compensate.”

Political scientists David Broockman and Joshua Kalla write: Decades of evidence from academic studies suggests that more moderate nominees tend to perform better in general elections than more ideologically extreme nominees. For example, [political scientist Alan Abramowitz found that] Democratic US House candidates who supported Medicare-for-all fared approximately 2.2 percentage points worse [on average] in […]

What up with red state blue state?

Jordan Ellenberg writes: I learned from your book that Democrats doing better in richer counties and Republicans doing better in poorer counties did not imply that richer people were more likely to vote for Democrats and that in fact, the opposite is true. I do wonder, though, to what extent that’s changing with the current […]

Is it really true that candidates who are perceived as ideologically extreme do even worse if “they actually pose as more radical than they really are”?

Most of Kruggy’s column today is about macroeconomics, a topic I’m pretty much ignorant of. But I noticed one political science claim: It’s easy to make the political case that Democrats should nominate a centrist, rather than someone from the party’s left wing. Candidates who are perceived as ideologically extreme usually pay an electoral penalty; […]

MRP Conference at Columbia April 3rd – April 4th 2020

The Departments of Statistics and Political Science and Institute for Social and Economic Research and Policy at Columbia University are delighted to invite you to our Spring conference on Multilevel Regression and Poststratification. Featuring Andrew Gelman, Beth Tipton, Jon Zelner, Shira Mitchell, Qixuan Chen and Leontine Alkema, the conference will combine a mix of cutting […]

Misleading vote reporting

Someone pointed me to this: The top few candidates looked like what I’d remembered hearing, but I was surprised that Yang did so poorly. Just 1% of the vote. But then I was told to carefully read the fine print at the bottom of the table: State delegate equivalents, multiplied by 100 That ain’t cool, […]

How much of Trump’s rising approval numbers can be attributed to differential nonresponse? P.S. With more analysis of recent polls from Jacob Long

Josh Marshall writes: If you follow polling you know that over the last couple weeks President Trump’s approval numbers have been trending up. . . . 538’s composite average has notched up a couple points over the second half of January and this morning Gallup released the highest approval rating of Trump’s presidency: 49%. Why […]

When I was asked, Who do you think is most likely to win the Democratic nomination?, this is how I responded . . .

A reporter emailed me: Question on deadline: As of now, who do you think is most likely to win the Democratic nomination: Sanders, Buttigieg or one of the others? I responded: My response is that primaries are hard to predict. Just to be clear: I’m not denying the possibility of useful predictive modeling, I’m just […]

MRP Carmelo Anthony update . . . Trash-talking’s fine. But you gotta give details, or links, or something!

Before getting to the main post, let me just say that I’m a big fan of Nate Silver. Just for one example: I’m on record as saying that primary elections are hard to predict. So I don’t even try. But there’s lots of information out there: poll data, fundraising numbers, expert opinion, delegate selection rules, […]

The intellectual explosion that didn’t happen

A few years ago, we discussed the book, “A Troublesome Inheritance: Genes, Race, and Human History,” by New York Times reporter Nicholas Wade. Wade’s book was challenging to read and review because it makes lots of claims that are politically explosive and could be true but do not seem clearly proved given available data. There’s […]

Is it accurate to say, “Politicians Don’t Actually Care What Voters Want”?

Jonathan Weinstein writes: This was a New York Times op-ed today, referring to this working paper. I found the pathologies of the paper to be worth an extended commentary, and wrote a possible blog entry, attached. I used to participate years ago in a shared blog at Northwestern, “Leisure of the Theory Class,” but nowadays […]

Call for proposals for a State Department project on estimating the prevalence of human trafficking

Abby Long points us to this call for proposals for a State Department project on estimating the prevalence of human trafficking: The African Programming and Research Initiative to End Slavery (APRIES) is pleased to announce a funding opportunity available through a cooperative agreement with the U.S. Department of State, Office to Monitor and Combat Trafficking […]

Steven Pinker on torture

I’ve recently been thinking about that expression, “A liberal is a conservative who’s been arrested.” Linguist and public intellectual Steven Pinker got into some trouble recently when it turned out that he’d been offering expert advice to the legal team of now-disgraced financier Jeffrey Epstein. I would not condemn Pinker for this. After all, everybody […]

Hey—the New York Times is hiring an election forecaster!

Chris Wiggins points us to this job opening: Staff Editor – Statistical Modeling The New York Times is looking to increase its capacity for statistical projects in the newsroom, especially around the 2020 election. You will help produce statistical forecasts for election nights, as part of The Times’s ambitious election results operation. That operation is […]

Fitting big multilevel regressions in Stan?

Joe Hoover writes: I am a social psychology PhD student, and I have some questions about applying MrP to estimation problems involving very large datasets or many sub-national units. I use MrP to obtain sub-national estimates for low-level geographic units (e.g. counties) derived from large data (e.g. 300k-1 million+). In addition to being large, my […]