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Archive of posts filed under the Multilevel Modeling category.

Why I don’t use the terms “fixed” and “random” (again)

A couple months ago we discussed this question from Sean de Hoon: In many cross-national comparative studies, mixed effects models are being used in which a number of slopes are fixed and the slopes of one or two variables of interested are allowed to vary across countries. The aim is often then to explain the […]

They don’t fit into their categories

I was reading the newspaper today and came across an article entitled, “Black Holes Inch Ahead to Violent Cosmic Union.” No big deal, except that it was in the National section. Not the International. This reminded me of other examples where items didn’t fit into their categories, for example when Slate magazine published an article, […]

Using to predict y: What’s that all about??

Toon Kuppens writes: After a discussion on a multilevel modeling mailing list, I came across this one-year-old blog post written by you. You might be interested to know that in social psychology, taking the aggregate outcome variable to predict the outcome variable has been used as a test of ‘convergence’, the phenomenon that people’s responses […]

My talk tomorrow (Thurs) at MIT political science: Recent challenges and developments in Bayesian modeling and computation (from a political and social science perspective)

It’s 1pm in room E53-482. I’ll talk about the usual stuff (and some of this too, I guess).

VB-Stan: Black-box black-box variational Bayes

Alp Kucukelbir, Rajesh Ranganath, Dave Blei, and I write: We describe an automatic variational inference method for approximating the posterior of differentiable probability models. Automatic means that the statistician only needs to define a model; the method forms a variational approximation, computes gradients using automatic differentiation and approximates expectations via Monte Carlo integration. Stochastic gradient […]

Stan Down Under

I (Bob, not Andrew) am in Australia until April 30. I’ll be giving some Stan-related and some data annotation talks, several of which have yet to be concretely scheduled. I’ll keep this page updated with what I’ll be up to. All of the talks other than summer school will be open to the public (the […]

Six quick tips to improve your regression modeling

It’s Appendix A of ARM: A.1. Fit many models Think of a series of models, starting with the too-simple and continuing through to the hopelessly messy. Generally it’s a good idea to start simple. Or start complex if you’d like, but prepare to quickly drop things out and move to the simpler model to help […]

Crowdsourcing data analysis: Do soccer referees give more red cards to dark skin toned players?

Raphael Silberzahn Eric Luis Uhlmann Dan Martin Pasquale Anselmi Frederik Aust Eli Christopher Awtrey Štěpán Bahník Feng Bai Colin Bannard Evelina Bonnier Rickard Carlsson Felix Cheung Garret Christensen Russ Clay Maureen A. Craig Anna Dalla Rosa Lammertjan Dam Mathew H. Evans Ismael Flores Cervantes Nathan Fong Monica Gamez-Djokic Andreas Glenz Shauna Gordon-McKeon Tim Heaton Karin […]

Planning my class for this semester: Thinking aloud about how to move toward active learning?

I’m teaching two classes this semester: – Design and Analysis of Sample Surveys (in the political science department, but the course has lots of statistics content); – Statistical Communication and Graphics (in the statistics department, but last time I taught it, many of the students were from other fields). I’ve taught both classes before. I […]

Trajectories of Achievement Within Race/Ethnicity: “Catching Up” in Achievement Across Time

Just in time for Christmas, here’s some good news for kids, from Pamela Davis-Kean and Justin Jager: The achievement gap has long been the focus of educational research, policy, and intervention. The authors took a new approach to examining the achievement gap by examining achievement trajectories within each racial group. To identify these trajectories they […]