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Archive of posts filed under the Multilevel Modeling category.

Causal inference with time-varying mediators

Adan Becerra writes to Tyler VanderWeele: I have a question about your paper “Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders” that I was hoping that you could help my colleague (Julia Ward) and me with. We are currently using Medicare claims data to evaluate the following general mediation among dialysis […]

The garden of 603,979,752 forking paths

Amy Orben and Andrew Przybylski write: The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many […]

Random patterns in data yield random conclusions.

Bert Gunter points to this New York Times article, “How Exercise May Make Us Healthier: People who exercise have different proteins moving through their bloodstreams than those who are generally sedentary,” writing that it is “hyping a Journal of Applied Physiology paper that is now my personal record holder for most extensive conclusions from practically […]

We’re done with our Applied Regression final exam (and solution to question 15)

We’re done with our exam. And the solution to question 15: 15. Consider the following procedure. • Set n = 100 and draw n continuous values x_i uniformly distributed between 0 and 10. Then simulate data from the model y_i = a + bx_i + error_i, for i = 1,…,n, with a = 2, b […]

Pharmacometrics meeting in Paris on the afternoon of 11 July 2019

Julie Bertrand writes: The pharmacometrics group led by France Mentre (IAME, INSERM, Univ Paris) is very pleased to host a free ISoP Statistics and Pharmacometrics (SxP) SIG local event at Faculté Bichat, 16 rue Henri Huchard, 75018 Paris, on Thursday afternoon the 11th of July 2019. It will features talks from Professor Andrew Gelman, Univ […]

Question 15 of our Applied Regression final exam (and solution to question 14)

Here’s question 15 of our exam: 15. Consider the following procedure. • Set n = 100 and draw n continuous values x_i uniformly distributed between 0 and 10. Then simulate data from the model y_i = a + bx_i + error_i, for i = 1,…,n, with a = 2, b = 3, and independent errors […]

Question 14 of our Applied Regression final exam (and solution to question 13)

Here’s question 14 of our exam: 14. You are predicting whether a student passes a class given pre-test score. The fitted model is, Pr(Pass) = logit^−1(a_j + 0.1x), for a student in classroom j whose pre-test score is x. The pre-test scores range from 0 to 50. The a_j’s are estimated to have a normal […]

Question 13 of our Applied Regression final exam (and solution to question 12)

Here’s question 13 of our exam: 13. You fit a model of the form: y ∼ x + u full + (1 | group). The estimated coefficients are 2.5, 0.7, and 0.5 respectively for the intercept, x, and u full, with group and individual residual standard deviations estimated as 2.0 and 3.0 respectively. Write the […]

Question 7 of our Applied Regression final exam (and solution to question 6)

Here’s question 7 of our exam: 7. You conduct an experiment in which some people get a special get-out-the-vote message and others do not. Then you follow up with a sample, after the election, to see if they voted. If you follow up with 500 people, how large an effect would you be able to […]

My talks at the University of Chicago this Thursday and Friday

Political Economy Workshop (12:30pm, Thurs 23 May 2019, Room 1022 of Harris Public Policy (Keller Center) 1307 E 60th Street): Political Science and the Replication Crisis We’ve heard a lot about the replication crisis in science (silly studies about ESP, evolutionary psychology, miraculous life hacks, etc.), how it happened (p-values, forking paths), and proposed remedies […]

Vigorous data-handling tied to publication in top journals among public heath researchers

Gur Huberman points us to this news article by Nicholas Bakalar, “Vigorous Exercise Tied to Macular Degeneration in Men,” which begins: A new study suggests that vigorous physical activity may increase the risk for vision loss, a finding that has surprised and puzzled researchers. Using questionnaires, Korean researchers evaluated physical activity among 211,960 men and […]

Hey, people are doing the multiverse!

Elio Campitelli writes: I’ve just saw this image in a paper discussing the weight of evidence for a “hiatus” in the global warming signal and immediately thought of the garden of forking paths. From the paper: Tree representation of choices to represent and test pause-periods. The ‘pause’ is defined as either no-trend or a slow-trend. […]

“MRP is the Carmelo Anthony of election forecasting methods”? So we’re doing trash talking now??

What’s the deal with Nate Silver calling MRP “the Carmelo Anthony of forecasting methods”? Someone sent this to me: and I was like, wtf? I don’t say wtf very often—at least, not on the blog—but this just seemed weird. For one thing, Nate and I did a project together once using MRP: this was our […]

Scandal! Mister P appears in British tabloid.

Tim Morris points us to this news article: And here’s the kicker: Mister P. Not quite as cool as the time I was mentioned in Private Eye, but it’s still pretty satisfying. My next goal: Getting a mention in Sports Illustrated. (More on this soon.) In all seriousness, it’s so cool when methods that my […]

Continuing discussion of status threat and presidential elections, with discussion of challenge of causal inference from survey data

Last year we reported on an article by sociologist Steve Morgan, criticizing a published paper by political scientist Diana Mutz. A couple months later we updated with Mutz’s response to Morgan’s critique. Finally, Morgan has published a reply to Mutz’s response to Morgan’s comments on Mutz’s paper. Here’s a passage that is of methodological interest: […]

R-squared for multilevel models

Brandon Sherman writes: I just was just having a discussion with someone about multilevel models, and the following topic came up. Imagine we’re building a multilevel model to predict SAT scores using many students. First we fit a model on students only, then students in classrooms, then students in classrooms within district, the previous case […]

A question about the piranha problem as it applies to A/B testing

Wicaksono Wijono writes: While listening to your seminar about the piranha problem a couple weeks back, I kept thinking about a similar work situation but in the opposite direction. I’d be extremely grateful if you share your thoughts. So the piranha problem is stated as “There can be some large and predictable effects on behavior, […]

Research topic on the geography of partisan prejudice (more generally, county-level estimates using MRP)

1. An estimate of the geography of partisan prejudice My colleagues David Rothschild and Tobi Konitzer recently published this MRP analysis, “The Geography of Partisan Prejudice: A guide to the most—and least—politically open-minded counties in America,” written up by Amanda Ripley, Rekha Tenjarla, and Angela He. Ripley et al. write: In general, the most politically […]

What’s a good default prior for regression coefficients? A default Edlin factor of 1/2?

The punch line “Your readers are my target audience. I really want to convince them that it makes sense to divide regression coefficients by 2 and their standard errors by sqrt(2). Of course, additional prior information should be used whenever available.” The background It started with an email from Erik van Zwet, who wrote: In […]

Understanding how Anova relates to regression

Analysis of variance (Anova) models are a special case of multilevel regression models, but Anova, the procedure, has something extra: structure on the regression coefficients. As I put it in the rejoinder for my 2005 discussion paper: ANOVA is more important than ever because we are fitting models with many parameters, and these parameters can […]