Category: Multilevel Modeling

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 […]

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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 […]

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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 […]

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“What Happened Next Tuesday: A New Way To Understand Election Results”

Yair just published a long post explaining (a) how he and his colleagues use Mister P and the voter file to get fine-grained geographic and demographic estimates of voter turnout and vote preference, and (b) why this makes a difference. The relevant research paper is here. As Yair says in his above-linked post, he and […]

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Can we do better than using averaged measurements?

Angus Reynolds writes: Recently a PhD student at my University came to me for some feedback on a paper he is writing about the state of research methods in the Fear Extinction field. Basically you give someone an electric shock repeatedly while they stare at neutral stimuli and then you see what happens when you […]

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Multilevel models with group-level predictors

Kari Lock Morgan writes: I’m writing now though with a multilevel modeling question that has been nagging me for quite some time now. In your book with Jennifer Hill, you include a group-level predictor (for example, 12.15 on page 266), but then end up fitting this as an individual-level predictor with lmer. How can this […]

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Perhaps you could try a big scatterplot with one dot per dataset?

Joe Nadeau writes: We are studying variation in both means and variances in metabolic conditions. We have access to nearly 200 datasets that involve a range of metabolic traits and vary in sample size, mean effects, and variance. Some traits differ in mean but not variance, others in variance but not mean, still others in […]

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Cool postdoc position in Arizona on forestry forecasting using tree ring models!

Margaret Evans sends in this cool job ad: Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation A post-doctoral fellowship is available in the Laboratory of Tree-Ring Research (University of Arizona) to work on an NSF Macrosystems Biology-funded project assimilating together tree-ring and forest inventory data to analyze patterns and drivers of forest productivity […]

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You’ve got data on 35 countries, but it’s really just N=3 groups.

Jon Baron points to a recent article, “Societal inequalities amplify gender gaps in math,” by Thomas Breda, Elyès Jouini, and Clotilde Napp (supplementary materials here), and writes: A particular issue bothers me whenever I read studies like this, which use nations as the unit of analysis and then make some inference from correlations across nations. […]

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Multilevel data collection and analysis for weight training (with R code)

[image of cat lifting weights] A graduate student who wishes to remain anonymous writes: I was wondering if you could answer an elementary question which came to mind after reading your article with Carlin on retrospective power analysis. Consider the field of exercise science, and in particular studies on people who lift weights. (I sometimes […]

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The hot hand—in darts!

Roland Langrock writes: Since on your blog you’ve regularly been discussing hot hand literature – which we closely followed – I’m writing to share with you a new working paper we wrote on a potential hot hand pattern in professional darts. We use state-space models in which a continuous-valued latent “hotness” variable, modeled as an […]

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“Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models”

Aki points us to this paper by Tore Selland Kleppe, which begins: Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterisation so that the re-parameterised target distribution has close to constant […]

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The competing narratives of scientific revolution

Back when we were reading Karl Popper’s Logic of Scientific Discovery and Thomas Kuhn’s Structure of Scientific Revolutions, who would’ve thought that we’d be living through a scientific revolution ourselves? Scientific revolutions occur on all scales, but here let’s talk about some of the biggies: 1850-1950: Darwinian revolution in biology, changed how we think about […]

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The competing narratives of scientific revolution

Back when we were reading Karl Popper’s Logic of Scientific Discovery and Thomas Kuhn’s Structure of Scientific Revolutions, who would’ve thought that we’d be living through a scientific revolution ourselves? Scientific revolutions occur on all scales, but here let’s talk about some of the biggies: 1850-1950: Darwinian revolution in biology, changed how we think about […]

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Cool tennis-tracking app

Swupnil Sahai writes that he’s developed Swing, “the best app for tracking all of your tennis stats, and maybe we’ll expand to other sports in the future.” According to Swupnil, the app runs on Apple Watch making predictions in real time. I hope in the future they’ll incorporate some hierarchical modeling to deal with sparse-data […]

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Cool tennis-tracking app

Swupnil Sahai writes that he’s developed Swing, “the best app for tracking all of your tennis stats, and maybe we’ll expand to other sports in the future.” According to Swupnil, the app runs on Apple Watch making predictions in real time. I hope in the future they’ll incorporate some hierarchical modeling to deal with sparse-data […]

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“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 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.