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Archive of posts filed under the Multilevel Modeling category.

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 (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 […]


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 […]

Forking paths vs. six quick regression tips

Bill Harris writes: I know you’re on a blog delay, but I’d like to vote to raise the odds that my question in a comment to discussed, in case it’s not in your queue. It’s likely just my simple misunderstanding, but I’ve sensed two bits of contradictory advice in your writing: fit one complete model all at […]

Where the fat people at?

Pearly Dhingra points me to this article, “The Geographic Distribution of Obesity in the US and the Potential Regional Differences in Misreporting of Obesity,” by Anh Le, Suzanne Judd, David Allison, Reena Oza-Frank, Olivia Affuso, Monika Safford, Virginia Howard, and George Howard, who write: Data from BRFSS [the behavioral risk factor surveillance system] suggest that […]

Stunning breakthrough: Using Stan to map cancer screening!

Paul Alper points me to this article, Breast Cancer Screening, Incidence, and Mortality Across US Counties, by Charles Harding, Francesco Pompei, Dmitriy Burmistrov, Gilbert Welch, Rediet Abebe, and Richard Wilson. Their substantive conclusion is there’s too much screening going on, but here I want to focus on their statistical methods: Spline methods were used to […]

One thing I like about hierarchical modeling is that is not just about criticism. It’s a way to improve inferences, not just a way to adjust p-values.

In an email exchange regarding the difficulty many researchers have in engaging with statistical criticism (see here for a recent example), a colleague of mine opined: Nowadays, promotion requires more publications, and in an academic environment, researchers are asked to do more than they can. So many researchers just work like workers in a product […]

rstanarm and more!

Ben Goodrich writes: The rstanarm R package, which has been mentioned several times on stan-users, is now available in binary form on CRAN mirrors (unless you are using an old version of R and / or an old version of OSX). It is an R package that comes with a few precompiled Stan models — […]