Category: Political Science

“The most important aspect of a statistical analysis is not what you do with the data, it’s what data you use” (survey adjustment edition)

Dean Eckles pointed me to this recent report by Andrew Mercer, Arnold Lau, and Courtney Kennedy of the Pew Research Center, titled, “For Weighting Online Opt-In Samples, What Matters Most? The right variables make a big difference for accuracy. Complex statistical methods, not so much.” I like most of what they write, but I think […]

The post “The most important aspect of a statistical analysis is not what you do with the data, it’s what data you use” (survey adjustment edition) appeared first on Statistical Modeling, Causal Inference, and Social Science.

The replication crisis and the political process

Jackson Monroe writes: I thought you might be interested in an article [by Dan McLaughlin] in NRO that discusses the replication crisis as part of a broadside against all public health research and social science. It seemed as though the author might be twisting the nature of the replication crisis toward his partisan ends, but […]

The post The replication crisis and the political process appeared first on Statistical Modeling, Causal Inference, and Social Science.

The replication crisis and the political process

Jackson Monroe writes: I thought you might be interested in an article [by Dan McLaughlin] in NRO that discusses the replication crisis as part of a broadside against all public health research and social science. It seemed as though the author might be twisting the nature of the replication crisis toward his partisan ends, but […]

The post The replication crisis and the political process appeared first on Statistical Modeling, Causal Inference, and Social Science.

If you have a measure, it will be gamed (politics edition).

They sometimes call it Campbell’s Law: New York Governor Andrew Cuomo is not exactly known for drumming up grassroots enthusiasm and small donor contributions, so it was quite a surprise on Monday when his reelection campaign reported that more than half of his campaign contributors this year gave $250 or less. But wait—a closer examination […]

The post If you have a measure, it will be gamed (politics edition). appeared first on Statistical Modeling, Causal Inference, and Social Science.

Mister P wins again

Chad Kiewiet De Jonge, Gary Langer, and Sofi Sinozich write: This paper presents state-level estimates of the 2016 presidential election using data from the ABC News/Washington Post tracking poll and multilevel regression with poststratification (MRP). While previous implementations of MRP for election forecasting have relied on data from prior elections to establish poststratification targets for […]

The post Mister P wins again appeared first on Statistical Modeling, Causal Inference, and Social Science.

He wants to model a proportion given some predictors that sum to 1

Joël Gombin writes: I’m wondering what your take would be on the following problem. I’d like to model a proportion (e.g., the share of the vote for a given party at some territorial level) in function of some compositional data (e.g., the sociodemographic makeup of the voting population), and this, in a multilevel fashion (allowing […]

The post He wants to model a proportion given some predictors that sum to 1 appeared first on Statistical Modeling, Causal Inference, and Social Science.

PNAS forgets basic principles of game theory, thus dooming thousands of Bothans to the fate of Alderaan

Under the subject line, “I needed this information to make a go/no-go decision on my new Death Star,” Kevin Lewis points to this press release from a prestigious journal: Because versions of the below articles were previously posted online, PNAS is publishing the articles without embargo: Potential atmospheres around TRAPPIST-1 planets Simulations of stellar winds […]

The post PNAS forgets basic principles of game theory, thus dooming thousands of Bothans to the fate of Alderaan appeared first on Statistical Modeling, Causal Inference, and Social Science.

About that claim in the NYT that the immigration issue helped Hillary Clinton? The numbers don’t seem to add up.

Today I noticed an op-ed by two political scientists, Howard Lavine and Wendy Rahm, entitled, “What if Trump’s Nativism Actually Hurts Him?”: Contrary to received wisdom, however, the immigration issue did not play to Mr. Trump’s advantage nearly as much as commonly believed. According to our analysis of national survey data from the American National […]

The post About that claim in the NYT that the immigration issue <em>helped</em> Hillary Clinton? The numbers don’t seem to add up. appeared first on Statistical Modeling, Causal Inference, and Social Science.

Flaws in stupid horrible algorithm revealed because it made numerical predictions

Kaiser Fung points to this news article by David Jackson and Gary Marx: The Illinois Department of Children and Family Services is ending a high-profile program that used computer data mining to identify children at risk for serious injury or death after the agency’s top official called the technology unreliable. . . . Two Florida […]

The post Flaws in stupid horrible algorithm revealed because it made numerical predictions appeared first on Statistical Modeling, Causal Inference, and Social Science.

Does “status threat” explain the 2016 presidential vote? Diana Mutz replies to criticism.

A couple months ago we reported on an article by sociologist Steve Morgan, criticizing a published paper by political scientist Diana Mutz. Mutz’s original article was called, “Status Threat, Not Economic Hardship, Explains the 2016 Presidential Vote,” and Morgan’s reply is called, “Status Threat, Material Interests, and the 2016 Presidential Vote” (it originally had the […]

The post Does “status threat” explain the 2016 presidential vote? Diana Mutz replies to criticism. appeared first on Statistical Modeling, Causal Inference, and Social Science.