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Archive of posts filed under the Multilevel Modeling category.

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 […]

Bayesian Linear Mixed Models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

This article by Tanner Sorensen, Sven Hohenstein, and Shravan Vasishth might be of interest to some of you.

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 […]

Point summary of posterior simulations?

Luke Miratrix writes: ​In the applied stats class ​I’m teaching ​on​ hierarchical models I’m giving the students (a mix of graduate students, many from the education school, and undergrads) a taste of Stan. I have to give them some “standard” way to turn Stan output into a point estimate (though of course I’ll also explain […]

Drive-by

Jona Sassenhagen writes: Here is a paper ***, in case you, errrrr, have run out of other things to blog about … I took a look and replied: Wow—what a horrible paper. Really ignorant. Probably best for me to just ignore it!

Controlling for variation in the weather in a regression analysis: Joe and Uri should learn about multilevel models and then they could give even better advice

Joe Simmons and Uri Simonsohn have an interesting post here. Unfortunately their blog doesn’t have a comment section so I’m commenting here. They write this at the end of their post: Another is to use daily dummies. This option can easily be worse. It can lower statistical power by throwing away data. First, one can […]

Multilevel regression

Mike Hughes writes: I have been looking a your blog entries from about 8 years ago in which you comment on the number of groups that is appropriate in multilevel regression. I have a research problem in which I have 6 groups and would like to use multilevel regression. Here is the situation. I have […]

My quick answer is that I would analyze all 10 outcomes using a multilevel model.

David Shor writes: Suppose you’re conducting an experiment on the effectiveness of a pain medication, but in the post survey, measure a large number of indicators of well being (Sleep quality, self reported pain, ability to get tasks done, anxiety levels, etc). After the experiment, the results are insignificant (or the posterior effect size isn’t […]

Actually, I’d just do full Bayes

Dave Clark writes: I was hoping for your opinion on a topic related to hierarchical models. I am an actuary and have generally worked with the concept of hierarchical models in the context of credibility theory. The text by Bühlmann and Gisler (A Course in Credibility Theory; Springer) sets up the mixed models under the […]

Mister P: Challenges in Generalizing from Sample to Population (my talk at the Ross-Royall Symposium at Johns Hopkins this Friday)

Mister P: Challenges in Generalizing from Sample to Population Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University With internet surveys, nonrepresentativeness and nonresponse are bigger concerns than ever. The natural approach is to adjust for more information, demographic and otherwise, to align the sample with the population. We demonstrate the effectiveness […]

“Don’t get me started on ‘cut'”

Brendan Rocks writes: I have a request for a blog post. I’ve been following the debates about ‘cut’ on the Stan lists over the last few years. Lots of very clever people agree that it’s bad news, which is enough to put me off. However, I’ve never fully groked the reasoning. [I think that should […]

Hierarchical models for phylogeny: Here’s what everyone’s talking about

The other day on the Stan users list, we had a long discussion on hierarchical models in phylogeny that I thought might be of general interest, so I’m reconstructing it here. It started with this question from Ben Lambert: I am hoping that you can help me settle a debate. My collaborators and I have […]

Pooling is relative to the model

Ryan Raaum writes: I’m hoping you’ll be willing to shed some light on a question I have regarding “pooling” in modeling. In your book with Jennifer Hill, you lay out two ends of a spectrum for dealing with structured data: (1) “Complete pooling” – ignoring the groups and pooling everything together for an overall average […]