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Archive of posts filed under the Multilevel Modeling category.

Webinar: An introduction to Bayesian multilevel modeling with brms

This post is by Eric. This Wednesday, at 12 pm ET, Paul Bürkner is stopping by to talk to us about brms. You can register here. Abstract The talk will be about Bayesian multilevel models and their implementation in R using the package brms. We will start with a short introduction to multilevel modeling and to […]

State-level predictors in MRP and Bayesian prior

Something came up in comments today that I’d like to follow up on. In our earlier post, I brought up an example: If you’re modeling attitudes about gun control, think hard about what state-level predictors to include. My colleagues and I thought about this a bunch of years ago when doing MRP for gun-control attitudes. […]

Some issues when using MRP to model attitudes on a gun control attitude question on a 1–4 scale

Elliott Morris writes: – I want to run a MRP model predicting 4 categories of response options to a question about gun control (multinomial logit) – I want to control for demographics in the standard hierarchical way (MRP) – I want the coefficients to evolve in a random walk over time, as I have data […]

Question on multilevel modeling reminds me that we need a good modeling workflow (building up your model by including varying intercepts, slopes, etc.) and a good computing workflow

Someone who wishes to remain anonymous writes: Lacking proper experience with multilevel modeling, I have a question regarding a nation-wide project on hospital mortality that I’ve recently come into contact with. The primary aim of the project is to benchmark hospital performances in terms of mortality (binary outcome) while controlling for “case mix”, that is, […]

How much granularity do you need in your Mister P?

Matt Kosko writes: I had a question for you about the appropriate number of groups in an MRP model. I’m currently working on streamlining some of the code we use to estimate state-level political opinions from our surveys. I have state-level predictors and Census data for poststratification (i.e., population totals in each age-sex-state-education cell), but […]

A Bayesian state-space model for the German federal election 2021 with Stan

I didn’t do anything on this, just stood still and listened while others talked. I’ll share the whole thread with you, just to give you a sense of how these research conversations go. This post is for you if: – You’re interested in MRP, or – You’re interested in German elections, or – You want […]

The 5-sigma rule in physics

Eliot Johnson writes: You’ve devoted quite a few blog posts to challenging orthodox views regarding statistical significance. If there’s been discussion of this as it relates to the 5-sigma rule in physics, then I’ve missed that thread. If not, why not open up a critical discussion about it? Here’s a link to one blog post […]

No, I don’t like talk of false positive false negative etc but it can still be useful to warn people about systematic biases in meta-analysis

Simon Gates writes: Something published recently that you might consider blogging: a truly terrible article in Lancet Oncology. It raises the issue of interpreting trials of similar agents and the issue of multiplicity. However, it takes a “dichotomaniac” view and so is only concerned about whether results are “significant” (=”positive”) or not, and suggests applying […]

Bayesian methods and what they offer compared to classical econometrics

A well-known economist who wishes to remain anonymous writes: Can you write about this agent? He’s getting exponentially big on Twitter. The link is to an econometrician, Jeffrey Wooldridge, who writes: Many useful procedures—shrinkage, for example—can be derived from a Bayesian perspective. But those estimators can be studied from a frequentist perspective, and no strong […]

Come work with me and David Shor !

Open positions on our progressive data team: Machine Learning engineer – https://grnh.se/6713732b4us Software Engineer – https://grnh.se/15ee5a2e4us Devops – https://grnh.se/aa2bef714us We are a diverse team of engineers, data scientists, statisticians, and political insiders who are closely connected to some of the most important decision makers in the progressive ecosystem. We worked with central players to develop strategy and direct […]

Scaling regression inputs by dividing by two standard deviations

I just had reason to reread this article from 2009, and I think it holds up just fine! Just to emphasize, I’m not saying you have to scale predictors by dividing by two standard deviations, nor am I even saying that you should do this scaling. I’m just saying that this scaling is a useful […]

How to track covid using hospital data collection and MRP

Len Covello, Yajuan Si, and I write: The current way we track the prevalence of coronavirus infections is deeply flawed. Ideally, health officials would test random samples of citizens in each community in a systematic way. But throughout the pandemic, the United States has lacked the political will or funding to pursue it. Instead, testing […]

My thoughts on “What’s Wrong with Social Science and How to Fix It: Reflections After Reading 2578 Papers”

Chetan Chawla and Asher Meir point us to this post by Alvaro de Menard, who writes: Over the past year, I [Menard] have skimmed through 2578 social science papers, spending about 2.5 minutes on each one. What a great beginning! I can relate to this . . . indeed, it roughly describes my experience as […]

“There ya go: preregistered, within-subject, multilevel”

Kevin Lewis points to this article, Probing Ovulatory-Cycle Shifts in Women’s Preferences for Men’s Behaviors, by Julia Stern, Tanja Gerlach, and Lars Penke: The existence of ovulatory-cycle shifts in women’s mate preferences has been a point of controversy. There is evidence that naturally cycling women in their fertile phase, compared with their luteal phase, evaluate […]

New research suggests: “Targeting interventions – including transmission-blocking vaccines – to adults aged 20-49 is an important consideration in halting resurgent epidemics and preventing COVID-19-attributable deaths.”

In recent weeks we’ve been hearing a lot about the priority of vaccinations. Should we be vaccinating older people first? Essential workers? Just vaccinate as many as possible without worrying about who gets it? Giving out the vaccine is partly about protecting people and partly about slowing the chains of transmission. The results of a […]

Bayesian inference completely solves the multiple comparisons problem

I’m rerunning this one from 2016 because it came up at work recently, and I think the general topic is as important as it’s ever been. flat priors consistently give bad inferences. Or, to put it another way, the routine use of flat priors results in poor frequency properties in realistic settings where studies are […]

Hierarchical stacking

(This post is by Yuling) Gregor Pirš, Aki, Andrew, and I wrote: Stacking is a widely used model averaging technique that yields asymptotically optimal predictions among linear averages. We show that stacking is most effective when the model predictive performance is heterogeneous in inputs, so that we can further improve the stacked mixture by a […]

Routine hospital-based SARS-CoV-2 testing outperforms state-based data in predicting clinical burden.

Len Covello, Yajuan Si, Siquan Wang, and I write: Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community […]

“They adjusted for three hundred confounders.”

Alexey Guzey points to this post by Scott Alexander and this research article by Elisabetta Patorno, Robert Glynn, Raisa Levin, Moa Lee, and Krista Huybrechts, and writes: I [Guzey] am extremely skeptical of anything that relies on adjusting for confounders and have no idea what to think about this. My intuition would be that because […]

Include all design information as predictors in your regression model, then postratify if necessary. No need to include survey weights: the information that goes into the weights will be used in any poststratification that is done.

David Kaplan writes: I have a question that comes up often when working with people who are analyzing large scale educational assessments such as NAEP or PISA. They want to do some kind of multilevel analysis of an achievement outcome such as mathematics ability predicted by individual and school level variables. The files contain the […]

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