Category: Political Science

“Statistical insights into public opinion and politics” (my talk for the Columbia Data Science Society this Wed 9pm)

7pm in Fayerweather 310: Why is it more rational to vote than to answer surveys (but it used to be the other way around)? How does this explain why we should stop overreacting to swings in the polls? How does modern polling work? What are the factors that predict election outcomes? What’s good and bad […]

The post “Statistical insights into public opinion and politics” (my talk for the Columbia Data Science Society this Wed 9pm) appeared first on Statistical Modeling, Causal Inference, and Social Science.

“She also observed that results from smaller studies conducted by NGOs – often pilot studies – would often look promising. But when governments tried to implement scaled-up versions of those programs, their performance would drop considerably.”

Robert Wiblin writes: If we have a study on the impact of a social program in a particular place and time, how confident can we be that we’ll get a similar result if we study the same program again somewhere else? Dr Eva Vivalt . . . compiled a huge database of impact evaluations in […]

The post “She also observed that results from smaller studies conducted by NGOs – often pilot studies – would often look promising. But when governments tried to implement scaled-up versions of those programs, their performance would drop considerably.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

A Bayesian take on ballot order effects

Dale Lehman sends along a paper, “The ballot order effect is huge: Evidence from Texas,” by Darren Grant, which begins: Texas primary and runoff elections provide an ideal test of the ballot order hypothesis, because ballot order is randomized within each county and there are many counties and contests to analyze. Doing so for all […]

The post A Bayesian take on ballot order effects appeared first on Statistical Modeling, Causal Inference, and Social Science.

2018: How did people actually vote? (The real story, not the exit polls.)

Following up on the post that we linked to last week, here’s Yair’s analysis, using Mister P, of how everyone voted. Like Yair, I think these results are much better than what you’ll see from exit polls, partly because the analysis is more sophisticated (MRP gives you state-by-state estimates in each demographic group), partly because […]

The post 2018: How did people actually vote? (The real story, not the exit polls.) appeared first on Statistical Modeling, Causal Inference, and Social Science.

“Law professor Alan Dershowitz’s new book claims that political differences have lately been criminalized in the United States. He has it wrong. Instead, the orderly enforcement of the law has, ludicrously, been framed as political.”

This op-ed by Virginia Heffernan is about g=politics, but it reminded me of the politics of science. Heffernan starts with the background: This last year has been a crash course in startlingly brutal abuses of power. For decades, it seems, a caste of self-styled overmen has felt liberated to commit misdeeds with impunity: ethical, sexual, […]

The post “Law professor Alan Dershowitz’s new book claims that political differences have lately been criminalized in the United States. He has it wrong. Instead, the orderly enforcement of the law has, ludicrously, been framed as political.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

Hey! Here’s what to do when you have two or more surveys on the same population!

This problem comes up a lot: We have multiple surveys of the same population and we want a single inference. The usual approach, applied carefully by news organizations such as Real Clear Politics and Five Thirty Eight, and applied sloppily by various attention-seeking pundits every two or four years, is “poll aggregation”: you take the […]

The post Hey! Here’s what to do when you have two or more surveys on the same population! appeared first on Statistical Modeling, Causal Inference, and Social Science.

2018: Who actually voted? (The real story, not the exit polls.)

Continuing from our earlier discussion . . . Yair posted some results from his MRP analysis of voter turnout: 1. The 2018 electorate was younger than in 2014, though not as young as exit polls suggest. 2. The 2018 electorate was also more diverse, with African American and Latinx communities surpassing their share of votes […]

The post 2018: Who actually voted? (The real story, not the exit polls.) appeared first on Statistical Modeling, Causal Inference, and Social Science.

2018: What really happened?

We’re always discussing election results on three levels: their direct political consequences, their implications for future politics, and what we can infer about public opinion. In 2018 the Democrats broadened their geographic base, as we can see in this graph from Yair Ghitza: Party balancing At the national level, what happened is what we expected […]

The post 2018: What really happened? appeared first on Statistical Modeling, Causal Inference, and Social Science.