Someone who wishes to remain anonymous writes: I just read your paper with Daniel Simpson and Michael Betancourt, The Prior Can Often Only Be Understood in the Context of the Likelihood, and I find it refreshing to read that “the practical utility of a prior distribution within a given analysis then depends critically on both […]

# Category: Multilevel Modeling

## “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 […]

## Fitting multilevel models when the number of groups is small

Matthew Poes writes: I have a question that I think you have answered for me before. There is an argument to be made that HLM should not be performed if a sample is too small (too small level 2 and too small level 1 units). Lot’s of papers written with guidelines on what those should […]

## 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 […]

## Principal Stratification on a Latent Variable (fitting a multilevel model using Stan)

Adam Sales points to this article with John Pane on principal stratification on a latent variable, and writes: Besides the fact that the paper uses Stan, and it’s about principal stratification, which you just blogged about, I thought you might like it because of its central methodological contribution. We had been trying to use computer […]

## “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 […]

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## 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.

## Classifying yin and yang using MRI

Zad Chow writes: I wanted to pass along this study I found a while back that aimed to see whether there was any possible signal in an ancient Chinese theory of depression that classifies major depressive disorder into “yin” and “yang” subtypes. The authors write the following, The “Yin and Yang” theory is a fundamental […]

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## Classifying yin and yang using MRI

Zad Chow writes: I wanted to pass along this study I found a while back that aimed to see whether there was any possible signal in an ancient Chinese theory of depression that classifies major depressive disorder into “yin” and “yang” subtypes. The authors write the following, The “Yin and Yang” theory is a fundamental […]

The post Classifying yin and yang using MRI appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Should we be concerned about MRP estimates being used in later analyses? Maybe. I recommend checking using fake-data simulation.

Someone sent in a question (see below). I asked if I could post the question and my reply on blog, and the person responded: Absolutely, but please withhold my name because this is becoming a touchy issue within my department. The boldface was in the original. I get this a lot. There seems to be […]

The post Should we be concerned about MRP estimates being used in later analyses? Maybe. I recommend checking using fake-data simulation. appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Should we be concerned about MRP estimates being used in later analyses? Maybe. I recommend checking using fake-data simulation.

Someone sent in a question (see below). I asked if I could post the question and my reply on blog, and the person responded: Absolutely, but please withhold my name because this is becoming a touchy issue within my department. The boldface was in the original. I get this a lot. There seems to be […]

The post Should we be concerned about MRP estimates being used in later analyses? Maybe. I recommend checking using fake-data simulation. appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Multilevel models for multiple comparisons! Varying treatment effects!

Mark White writes: I have a question regarding using multilevel models for multiple comparisons, per your 2012 paper and many blog posts. I am in a situation where I do randomized experiments, and I have a lot of additional demographic information about people, as well. For the moment, let us just assume that all of […]

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## “Economic predictions with big data” using partial pooling

Tom Daula points us to this post, “Economic Predictions with Big Data: The Illusion of Sparsity,” by Domenico Giannone, Michele Lenza, and Giorgio Primiceri, and writes: The paper wants to distinguish between variable selection (sparse models) and shrinkage/regularization (dense models) for forecasting with Big Data. “We then conduct Bayesian inference on these two crucial parameters—model […]

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## 2018: How did people actually vote? (The real story, not the exit polls.)

Following up on the post that we linked to last week, here’s Yair’s analysis, using Mister P, of how everyone voted. Like Yair, I think these results are much better than what you’ll see from exit polls, partly because the analysis is more sophisticated (MRP gives you state-by-state estimates in each demographic group), partly because […]

The post 2018: How did people actually vote? (The real story, not the exit polls.) appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Hey! Here’s what to do when you have two or more surveys on the same population!

This problem comes up a lot: We have multiple surveys of the same population and we want a single inference. The usual approach, applied carefully by news organizations such as Real Clear Politics and Five Thirty Eight, and applied sloppily by various attention-seeking pundits every two or four years, is “poll aggregation”: you take the […]

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