Category: Multilevel Modeling

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 […]

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 […]

Mister P for surveys in epidemiology — using Stan!

Jon Zelner points us to this new article in the American Journal of Epidemiology, “Multilevel Regression and Poststratification: A Modelling Approach to Estimating Population Quantities From Highly Selected Survey Samples,” by Marnie Downes, Lyle Gurrin, Dallas English, Jane Pirkis, Dianne Currier, Matthew Spittal, and John Carlin, which begins: Large-scale population health studies face increasing difficulties […]

“Using 26,000 diary entries to show ovulatory changes in sexual desire and behavior”

Kevin Lewis points us to this research paper by Ruben Arslan, Katharina Schilling, Tanja Gerlach, and Lars Penke, which begins: Previous research reported ovulatory changes in women’s appearance, mate preferences, extra- and in-pair sexual desire, and behavior, but has been criticized for small sample sizes, inappropriate designs, and undisclosed flexibility in analyses. Examples of such […]

Of multiple comparisons and multilevel models

Kleber Neves writes: I’ve been a long-time reader of your blog, eventually becoming more involved with the “replication crisis” and such (currently, I work with the Brazilian Reproducibility Initiative). Anyway, as I’m now going deeper into statistics, I feel like I still lack some foundational intuitions (I was trained as a half computer scientist/half experimental […]

“Objective: Generate evidence for the comparative effectiveness for each pairwise comparison of depression treatments for a set of outcomes of interest.”

Mark Tuttle points us to this project by Martijn Schuemie and Patrick Ryan: Large-Scale Population-Level Evidence Generation Objective: Generate evidence for the comparative effectiveness for each pairwise comparison of depression treatments for a set of outcomes of interest. Rationale: In current practice, most comparative effectiveness questions are answered individually in a study per question. This […]

MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about

Someone pointed me to this thread where I noticed some issues I’d like to clear up: David Shor: “MRP itself is like, a 2009-era methodology.” Nope. The first paper on MRP was from 1997. And, even then, the component pieces were not new: we were just basically combining two existing ideas from survey sampling: regression […]

The post MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about appeared first on Statistical Modeling, Causal Inference, and Social Science.

MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about

Someone pointed me to this thread where I noticed some issues I’d like to clear up: David Shor: “MRP itself is like, a 2009-era methodology.” Nope. The first paper on MRP was from 1997. And, even then, the component pieces were not new: we were just basically combining two existing ideas from survey sampling: regression […]

The post MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about appeared first on Statistical Modeling, Causal Inference, and Social Science.

Using multilevel modeling to improve analysis of multiple comparisons

Justin Chumbley writes: I have mused on drafting a simple paper inspired by your paper “Why we (usually) don’t have to worry about multiple comparisons”. The initial idea is simply to revisit frequentist “weak FWER” or “omnibus tests” (which assume the null everywhere), connecting it to a Bayesian perspective. To do this, I focus on […]

The post Using multilevel modeling to improve analysis of multiple comparisons appeared first on Statistical Modeling, Causal Inference, and Social Science.

Using multilevel modeling to improve analysis of multiple comparisons

Justin Chumbley writes: I have mused on drafting a simple paper inspired by your paper “Why we (usually) don’t have to worry about multiple comparisons”. The initial idea is simply to revisit frequentist “weak FWER” or “omnibus tests” (which assume the null everywhere), connecting it to a Bayesian perspective. To do this, I focus on […]

The post Using multilevel modeling to improve analysis of multiple comparisons appeared first on Statistical Modeling, Causal Inference, and Social Science.

Comparing racism from different eras: If only Tucker Carlson had been around in the 1950s he could’ve been a New York Intellectual.

TV commentator Carlson in 2018 recently raised a stir by saying that immigration makes the United States “poorer, and dirtier, and more divided,” which reminded me of this rant from literary critic Alfred Kazin in 1957: Kazin put it in his diary and Carlson broadcast it on TV, so not quite the same thing. But […]

The post Comparing racism from different eras: If only Tucker Carlson had been around in the 1950s he could’ve been a New York Intellectual. appeared first on Statistical Modeling, Causal Inference, and Social Science.

Comparing racism from different eras: If only Tucker Carlson had been around in the 1950s he could’ve been a New York Intellectual.

TV commentator Carlson in 2018 recently raised a stir by saying that immigration makes the United States “poorer, and dirtier, and more divided,” which reminded me of this rant from literary critic Alfred Kazin in 1957: Kazin put it in his diary and Carlson broadcast it on TV, so not quite the same thing. But […]

The post Comparing racism from different eras: If only Tucker Carlson had been around in the 1950s he could’ve been a New York Intellectual. appeared first on Statistical Modeling, Causal Inference, and Social Science.