Category: Multilevel Modeling

Should we be concerned about MRP estimates being used in later analyses? Maybe. I recommend checking using fake-data simulation.

Someone sent in a question (see below). I asked if I could post the question and my reply on blog, and the person responded: Absolutely, but please withhold my name because this is becoming a touchy issue within my department. The boldface was in the original. I get this a lot. There seems to be […]

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Multilevel models for multiple comparisons! Varying treatment effects!

Mark White writes: I have a question regarding using multilevel models for multiple comparisons, per your 2012 paper and many blog posts. I am in a situation where I do randomized experiments, and I have a lot of additional demographic information about people, as well. For the moment, let us just assume that all of […]

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“Economic predictions with big data” using partial pooling

Tom Daula points us to this post, “Economic Predictions with Big Data: The Illusion of Sparsity,” by Domenico Giannone, Michele Lenza, and Giorgio Primiceri, and writes: The paper wants to distinguish between variable selection (sparse models) and shrinkage/regularization (dense models) for forecasting with Big Data. “We then conduct Bayesian inference on these two crucial parameters—model […]

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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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