Here’s last year’s course plan. Maybe I’ll change it a bit, haven’t decided yet. The course number is Political Science 4365, and it’s also cross-listed in Statistics.

**Multilevel Modeling**category.

## How does Brad Cooper analyze hierarchical survey data with post-stratification?

Laura Holder writes: I am working on a project involving a large survey data set and am interested in applying a model-based approach in the context of post-stratification (as you frequently discuss). I’m attempting to determine the most suitable approach for my circumstance. The data set I am working with is collected via a two […]

## Inference from an intervention with many outcomes, not using “statistical significance”

Kate Casey writes: I have been reading your papers “Type S error rates for classical…” and “Why We (Usually) Don’t Have to Worry…” with great interest and would be grateful for your views on the appropriateness of a potentially related application. I have a non-hierarchical dataset of 28 individuals who participated in a randomized control […]

## You won’t believe these stunning transformations: How to parameterize hyperpriors in hierarchical models?

Isaac Armstrong writes: I was working through your textbook “Data Analysis Using Regression and Multilevel/Hierarchical Models” but wanted to learn more and started working through your “Bayesian Data Analysis” text. I’ve got a few questions about your rat tumor example that I’d like to ask. I’ve been trying to understand one of the hierarchical models […]

## 3 new priors you can’t do without, for coefficients and variance parameters in multilevel regression

Partha Lahiri writes, in reference to my 2006 paper: I am interested in finding out a good prior for the regression coefficients and variance components in a multi-level setting. For concreteness, letâ€™s say we have a model like the following: Level 1: Y_ijk | theta_ij ~(ind) N( theta_ij, sigma^2) Level 2: theta_ij| mu_i ~(ind) N( […]

## Stop screaming already: Exaggeration of effects of fan distraction in NCAA basketball

John Ezekowitz writes: I have been reading your work on published effect sizes, and I thought you might be interested in this example, which is of small consequence but grates me as a basketball and data fan. Kevin Quealy and Justin Wolfers published an analysis in The NYT on fans’ effectiveness in causing road teams […]

## 3 postdoc opportunities you can’t miss—here in our group at Columbia! Apply NOW, don’t miss out!

Hey, just once, the Buzzfeed-style hype is appropriate. We have 3 amazing postdoc opportunities here, and you need to apply NOW. Here’s the deal: we’re working on some amazing projects. You know about Stan and associated exciting projects in computational statistics. There’s the virtual database query, which is the way I like to describe our […]

## Hierarchical logistic regression in Stan: The untold story

Corey Yanofsky pointed me to a paper by Neal Beck, Estimating grouped data models with a binary dependent variable and fixed effects: What are the issues?, which begins: This article deals with a very simple issue: if we have grouped data with a binary dependent variable and want to include fixed effects (group specific intercepts) […]

## Latest gay gene tabloid hype

The tabloid in question is the journal Nature, which along with Science and PPNAS (the Proceedings of the National Academy of Sciences, publisher of gems such as the himmicanes and hurricanes study) has in recent years become notorious for publishing flashy but unsubstantiated scientific claims. As Lord Acton never said, publicity corrupts, and absolute publicity […]

## Mindset interventions are a scalable treatment for academic underachievement — or not?

Someone points me to this post by Scott Alexander, criticizing the work of psychology researcher Carol Dweck. Alexander looks carefully at an article, “Mindset Interventions Are A Scalable Treatment For Academic Underachievement,” by David Paunesku, Gregory Walton, Carissa Romero, Eric Smith, David Yeager, and Carol Dweck, and he finds the following: Among ordinary students, the […]

## Constructing an informative prior using meta-analysis

Chris Guure writes: I am trying to construct an informative prior by synthesizing or collecting some information from literature (meta-analysis) and then to apply that to a real data set (it is longitudinal data) for over 20 years follow-up. In constructing the prior using the meta-analysis data, the issue of publication bias came up. I […]

## Fitting a multilevel model

Cui Yang writes: I have a question about the use of BRT (Boosting regression tree). I am planning to write an article about the effects of soil fauna and understory fine roots on forest soil organic carbon. The experiment was conducted in a subtropical forest area in China. There were 16 blocks each with 5 […]

## Pro Publica’s new Surgeon Scorecards

Skyler Johnson writes: You should definitely weigh in on this… Pro Publica created “Surgeon Scorecards” based upon risk adjusted surgery compilation rates. They used hierarchical modeling via the lmer package in R. For detailed methodology, click the methodology “how we calculated complications” link, then atop that next page click on the detailed methodology to download […]

## Survey weighting and regression modeling

Yphtach Lelkes points us to a recent article on survey weighting by three economists, Gary Solon, Steven Haider, and Jeffrey Wooldridge, who write: We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research, weighting is called for when it is needed […]

## Where does Mister P draw the line?

Bill Harris writes: Mr. P is pretty impressive, but I’m not sure how far to push him in particular and MLM [multilevel modeling] in general. Mr. P and MLM certainly seem to do well with problems such as eight schools, radon, or the Xbox survey. In those cases, one can make reasonable claims that the […]

## “Best Linear Unbiased Prediction” is exactly like the Holy Roman Empire

Dan Gianola pointed me to this article, “One Hundred Years of Statistical Developments in Animal Breeding,” coauthored with Guilherme Rosa, which begins: Statistical methodology has played a key role in scientific animal breeding. Approximately one hundred years of statistical developments in animal breeding are reviewed. Some of the scientific foundations of the field are discussed, […]