Alan Abramowitz writes: In five days, Clinton’s lead increased from 5 points to 12 points. And Democratic party ID margin increased from 3 points to 10 points. No, I don’t think millions of voters switched to the Democratic party. I think Democrats are were just more likely to respond in that second poll. And, remember, […]

**Multilevel Modeling**category.

## His varying slopes don’t seem to follow a normal distribution

Bruce Doré writes: I have a question about multilevel modeling I’m hoping you can help with. What should one do when random effects coefficients are clearly not normally distributed (i.e., coef(lmer(y~x+(x|id))) )? Is this a sign that the model should be changed? Or can you stick with this model and infer that the assumption of […]

## Taking Bayesian Inference Seriously [my talk tomorrow at Harvard conference on Big Data]

Mon 22 Aug, 9:50am, at Harvard Science Center Hall A: Taking Bayesian Inference Seriously Over the years I have been moving toward the use of informative priors in more and more of my applications. I will discuss several examples from theory, application, and computing where traditional noninformative priors lead to disaster, but a little bit […]

## Modeling correlation of issue attitudes and partisanship within states

John Kuk writes: I have taught myself multilevel modeling using your book and read your work with Delia Baldassarri about partisanship and issue alignment. I have a question about related to these two works. I want to find the level of correlation between partisanship and issues at the state level. Your work with Professor Baldassarri […]

## Documented forking paths in the Competitive Reaction Time Task

Baruch Eitan writes: This is some luscious garden of forking paths. Indeed. Here’s what Malte Elson writes at the linked website: The Competitive Reaction Time Task, sometimes also called the Taylor Aggression Paradigm (TAP), is one of the most commonly used tests to purportedly measure aggressive behavior in a laboratory environment. . . . While […]

## Smooth poll aggregation using state-space modeling in Stan, from Jim Savage

Jim Savage writes: I just saw your post on poll bounces; have been thinking the same myself. Why are the poll aggregators so jumpy about new polls? Annoyed, I put together a poll aggregator that took a state-space approach to the unobserved preferences; nothing more than the 8 schools (14 polls?) example with a time-varying […]

## Guy Fieri wants your help! For a TV show on statistical models for real estate

I got the following email from David Mulholland: I’m a producer at Citizen Pictures where we produce Food Network’s “Diners, Dives and Drive-Ins” and Bravo’s digital series, “Going Off The Menu,” among others. A major network is working with us to develop a show that pits “data” against a traditional real estate agent to see […]

## All maps of parameter estimates remain misleading

Roland Rau writes: After many years of applying frequentist statistical methods in mortality research, I just began to learn about the application of Bayesian methods in demography. Since I also wanted to change a part of my research focus on spatial models, I discovered your 1999 paper with Phil Price, All maps of parameter estimates […]

## What recommendations to give when a medical study is not definitive (which of course will happen all the time, especially considering that new treatments should be compared to best available alternatives, which implies that most improvements will be incremental at best)

Simon Gates writes: I thought you might be interested in a recently published clinical trial, for potential blog material. It picks up some themes that have cropped in recent months. Also, it is important for the way statistical methods influence what can be life or death decisions. The OPPTIMUM trial (http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)00350-0/abstract) evaluated use of vaginal progesterone […]

## Going beyond confidence intervals

Anders Lamberg writes: In an article by Tom Sigfried, Science News, July 3 2014, “Scientists’ grasp of confidence intervals doesn’t inspire confidence” you are cited: “Gelman himself makes the point most clearly, though, that a 95 percent probability that a confidence interval contains the mean refers to repeated sampling, not any one individual interval.” I […]

## Replin’ ain’t easy: My very first preregistration

I’m doing my first preregistered replication. And it’s a lot of work! We’ve been discussing this for awhile—here’s something I published in 2013 in response to proposals by James Moneghan and by Macartan Humphreys, Raul Sanchez de la Sierra, and Peter van der Windt for preregistration in political science, here’s a blog discussion (“Preregistration: what’s […]

## YouGov uses Mister P for Brexit poll

Ben Lauderdale and Doug Rivers give the story: There has been a lot of noise in polling on the upcoming EU referendum. Unlike the polls before the 2015 General Election, which were in almost perfect agreement (though, of course, not particularly close to the actual outcome), this time the polls are in serious disagreement. Telephone […]

## The answer is the Edlin factor

Garnett McMillan writes: You have argued about the pervasive role of the Garden of Forking Paths in published research. Given this influence, do you think that it is sensible to use published research to inform priors in new studies? My reply: Yes, I think you can use published research but in doing so you should […]

## How to design a survey so that Mister P will work well?

Barry Quinn writes: I would like some quick advice on survey design literature, specifically any good references you would have when designing a good online survey to allow for some decent hierarchal modeling? My quick response is that during the opening you should already be thinking about the endgame. In this case, the endgame is […]

## Betancourt Binge (Video Lectures on HMC and Stan)

Even better than binging on Netflix, catch up on Michael Betancourt’s updated video lectures, just days after their live theatrical debut in Tokyo. Scalable Bayesian Inference with Hamiltonian Monte Carlo (YouTube, 1 hour) Some Bayesian Modeling Techniques in Stan (YouTube, 1 hour 40 minutes) His previous videos have received very good reviews and they’re only […]

## A Primer on Bayesian Multilevel Modeling using PyStan

Chris Fonnesbeck contributed our first PyStan case study (I wrote the abstract), in the form of a very nice Jupyter notebook. Daniel Lee and I had the pleasure of seeing him present it live as part of a course we were doing at Vanderbilt last week. A Primer on Bayesian Multilevel Modeling using PyStan This […]

## Birthday analysis—Friday the 13th update, and some model checking

Carl Bialik and Andrew Flowers at fivethirtyeight.com (Nate Silver’s site) ran a story following up on our birthdays example—that time series decomposition of births by day, which is on the cover of the third edition of Bayesian Data Analysis using data from 1968-1988, and which then Aki redid using a new dataset from 2000-2014. Friday […]