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Archive of posts filed under the Multilevel Modeling category.

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 […]

“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 […]

When anyone claims 80% power, I’m skeptical.

A policy analyst writes: I saw you speak at ** on Bayesian methods. . . . I had been asked to consult on a large national evaluation of . . . [details removed to preserve anonymity] . . . and had suggested treading carefully around the use of Bayesian statistics in this study (basing it […]

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 […]

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 […]

“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 […]

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 […]

“Bayesian Meta-Analysis with Weakly Informative Prior Distributions”

Donny Williams sends along this paper, with Philippe Rast and Paul-Christian Bürkner, and writes: This paper is similar to the Chung et al. avoiding boundary estimates papers (here and here), but we use fully Bayesian methods, and specifically the half-Cauchy prior. We show it has as good of performance as a fully informed prior based […]

Multilevel modeling in Stan improves goodness of fit — literally.

John McDonnell sends along this post he wrote with Patrick Foley on how they used item-response models in Stan to get better clothing fit for their customers: There’s so much about traditional retail that has been difficult to replicate online. In some senses, perfect fit may be the final frontier for eCommerce. Since at Stitch […]

Stan goes to the World Cup

Leo Egidi shares his 2018 World Cup model, which he’s fitting in Stan. But I don’t like this: First, something’s missing. Where’s the U.S.?? More seriously, what’s with that “16.74%” thing? So bogus. You might as well say you’re 66.31 inches tall. Anyway, as is often the case with Bayesian models, the point here is […]

Global shifts in the phenological synchrony of species interactions over recent decades

Heather Kharouba et al. write: Phenological responses to climate change (e.g., earlier leaf-out or egg hatch date) are now well documented and clearly linked to rising temperatures in recent decades. Such shifts in the phenologies of interacting species may lead to shifts in their synchrony, with cascading community and ecosystem consequences . . . We […]

The necessity—and the difficulty—of admitting failure in research and clinical practice

Bill Jefferys sends along this excellent newspaper article by Siddhartha Mukherjee, “A failure to heal,” about the necessity—and the difficulty—of admitting failure in research and clinical practice. Mukherjee writes: What happens when a clinical trial fails? This year, the Food and Drug Administration approved some 40 new medicines to treat human illnesses, including 13 for […]

Forking paths come from choices in data processing and also from choices in analysis

Michael Wiebe writes: I’m a PhD student in economics at UBC. I’m trying to get a good understanding of the garden of forking paths, and I have some questions about your paper with Eric Loken. You describe the garden of forking paths as “researcher degrees of freedom without fishing” (#3), where the researcher only performs […]

Against Screening

Matthew Simonson writes: I have a question that may be of interest to your readers (and even if not, I’d love to hear your response). I’ve been analyzing a dataset of over 100 Middle Eastern political groups (MAROB) to see how these groups react to government repression. Observations are at the group-year level and include […]

“This is a weakness of our Bayesian Data Analysis book: We don’t have a lot of examples with informative priors.”

Roy Tamura writes: I am trying to implement a recommendation you made a few years ago. In my clinical trial of drug versus placebo, patients were stratified into two cohorts and randomized within strata. Time to event is the endpoint with the proportional hazards regression with strata and treatment as independent factors. There is evidence […]

“I admire the authors for simply admitting they made an error and stating clearly and without equivocation that their original conclusions were not substantiated.”

David Allison writes: I hope you will consider covering this in your blog. I admire the authors for simply admitting they made an error and stating clearly and without equivocation that their original conclusions were not substantiated. More attention to the confusing effects of regression to the mean are warranted as is more praise for […]

Regularized Prediction and Poststratification (the generalization of Mister P)

This came up in comments recently so I thought I’d clarify the point. Mister P is MRP, multilevel regression and poststratification. The idea goes like this: 1. You want to adjust for differences between sample and population. Let y be your outcome of interest and X be your demographic and geographic variables you’d like to […]

How to reduce Type M errors in exploratory research?

Miao Yu writes: Recently, I found this piece [a news article by Janet Pelley, Sulfur dioxide pollution tied to degraded sperm quality, published in Chemical & Engineering News] and the original paper [Inverse Association between Ambient Sulfur Dioxide Exposure and Semen Quality in Wuhan, China, by Yuewei Liu, published in Environmental Science & Technology]. Air […]

Zero-excluding priors are probably a bad idea for hierarchical variance parameters

(This is Dan, but in quick mode) I was on the subway when I saw Andrew’s last post and it doesn’t strike me as a particularly great idea. So let’s take a look at the suggestion for 8 schools using a centred parameterization.  This is not as comprehensive as doing a proper simulation study, but […]

Individual and aggregate causal effects: Social media and depression among teenagers

This one starts out as a simple story of correction of a statistical analysis and turns into an interesting discussion of causal inference for multilevel models. Michael Daly writes: I saw your piece on ‘Have Smartphone Destroyed a Generation’ and wanted to flag some of the associations underlying key claims in this debate (which is […]