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Archive of posts filed under the Multilevel Modeling category.

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 – Software Engineer – Devops – 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 […]

Flaxman et al. respond to criticisms of their estimates of effects of anti-coronavirus policies

As youall know, as the coronavirus has taken its path through the world, epidemiologists and social scientists have tracked rates of exposure and mortality, studied the statistical properties of the transmission of the virus, and estimated effects of behaviors and policies that have been tried to limit the spread of the disease. All this is […]

“Inferring the effectiveness of government interventions against COVID-19”

John Salvatier points us to this article by Jan Brauner et al. that states: We gathered chronological data on the implementation of NPIs [non-pharmaceutical interventions, i.e. policy or behavioral interventions] for several European, and other, countries between January and the end of May 2020. We estimate the effectiveness of NPIs, ranging from limiting gathering sizes, […]

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond

Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal write: Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as Markov chain Monte Carlo algorithms struggle with the geometry of the resulting posterior distribution and can be prohibitively slow. […]

Further formalization of the “multiverse” idea in statistical modeling

Cristobal Young and Sheridan Stewart write: Social scientists face a dual problem of model uncertainty and methodological abundance. . . . This ‘uncertainty among abundance’ offers spiraling opportunities to discover a statistically significant result. The problem is acute when models with significant results are published, while those with non-significant results go unmentioned. Multiverse analysis addresses […]

Mister P for the 2020 presidential election in Belarus

An anonymous group of authors writes: Political situation Belarus is often called the “last dictatorship” in Europe. Rightly so, Aliaskandr Lukashenka has served as the country’s president since 1994. In the 26 years of his rule, Lukashenka has consolidated and extended his power, which is today absolute. Rigging referendums has been an effective means of […]

You don’t need a retina specialist to know which way the wind blows

Jayakrishna Ambati writes: I am a retina specialist and vision scientist at the University of Virginia. I am writing to you with a question on Bayesian statistics. I am performing a meta analysis of 5 clinical studies. In addition to a random effects meta analysis model, I am running Bayesian meta analysis models using half […]

Bayesian Workflow

Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Paul-Christian Bürkner, Lauren Kennedy, Jonah Gabry, Martin Modrák, and I write: The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and […]

Merlin and me talk on the Bayesian podcast about forecasting the election

Alex Androrra interviewed us, and I guess it makes sense to post the link before the election is over. A couple months ago, Alex interviewed Jennifer, Aki, and me to talk about our book, Regression and Other Stories. I can’t figure out how to directly link to that; you’ll have to follow the above link, […]

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