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Articles by Multilevel Modeling
PhD position at the University of Iceland: Improved 21st century projections of sub-daily extreme precipitation by spatio-temporal recalibration
PhD position at the University of Iceland: Improved 21st century projections of sub-daily extreme precipitation by spatio-temporal recalibration Posted by Andrew on 30 September 2021, 12:34 am Birgir Hrafnkelsson writes: In 2016 you kindly helped me with advertising a PhD position at the University of Iceland. The successful candidate saw the ad on your blog. In 2019, he graduated with his PhD and received the Laplace Prize from ASA SBSS for one of his papers.
Adjusting for stratification and clustering in coronavirus surveys
Adjusting for stratification and clustering in coronavirus surveys Posted by Andrew on 2 September 2021, 9:26 am Someone who wishes to remain anonymous writes: I enjoyed your blog posts and eventual paper/demo about adjusting for diagnostic test sensitivity and specificity in estimating seroprevalence last year. I’m wondering if you had any additional ideas about adjustments for sensitivity and specificity in the presence of complex survey designs.
Claim of police shootings causing low birth weights in the neighborhood
Under the subject line, “A potentially dubious study making the rounds, re police shootings,” Gordon Danning links to this article, which begins: Police use of force is a controversial issue, but the broader consequences and spillover effects are not well understood. This study examines the impact of in utero exposure to police killings of unarmed blacks in the residential environment on black infants’ health.
How to reconcile that I hate structural equation models, but I love measurement error models and multilevel regressions, even though these are special cases of structural equation models? " Statistical Modeling, Causal Inference, and Social Science
How to reconcile that I hate structural equation models, but I love measurement error models and multilevel regressions, even though these are special cases of structural equation models? Posted by Andrew on 6 July 2021, 9:29 am Andy Dorsey writes: I’m a graduate student in psychology. I’m trying to figure out what seems to me to be a paradox: One issue you’ve talked about in the past is how you don’t like structural equation modeling (e.g., your blog post here).
From “Mathematical simplicity is not always the same as conceptual simplicity” to scale-free parameterization and its connection to hierarchical models " Statistical Modeling, Causal Inference, and Social Science
From “Mathematical simplicity is not always the same as conceptual simplicity” to scale-free parameterization and its connection to hierarchical models Posted by Andrew on 3 July 2021, 9:58 am I sent the following message to John Cook: This post popped up, and I realized that the point that I make (“Mathematical simplicity is not always the same as conceptual simplicity.
“Sources must lose credibility when it is shown they promote falsehoods, even more when they never take accountability for those falsehoods.”
“Sources must lose credibility when it is shown they promote falsehoods, even more when they never take accountability for those falsehoods.” Posted by Andrew on 24 June 2021, 9:51 am So says Michigan state senator Ed McBroom, in a quote reminiscent of the famous dictum by Daniel Davies, “Good ideas do not need lots of lies told about them in order to gain public acceptance.” I agree with both quotes. It’s kind of a Bayesian thing, or a multilevel modeling thing.
Statistical Modeling, Causal Inference, and Social Science
Andy Timm writes: I’m curious if you have any suggestions for dealing with item nonresponse when using MRP. I haven’t seen anything particularly compelling in a literature review, but it seems like this has to have come up. It seems like a surprisingly large number of papers just go for a complete cases analysis, or don’t mention clearly how missingness in predictors was handled. I can generally see how that could make sense, but I’m not sure if that does in my case.
Estimating excess mortality in rural Bangladesh from surveys and MRP
(This post is by Yuling, not by/reviewed by Andrew) Recently I (Yuling) have contributed to a public heath project with many great collaborates: The goal is to understand the excess mortality in potential relevance to Covid-19.
Cancer patients be criming? Some discussion and meta-discussion of statistical modeling, causal inference, and social science:
1. Meta-story Someone pointed me to a news report of a statistics-based research claim and asked me what I thought of it. I read through the press summary and the underlying research paper. At this point, it’s natural to anticipate one of two endpoints: I like the paper, or I don’t. The results seem reasonable and well founded, or the data and analysis seem flawed. One tricky point here is that there’s an asymmetry. A bad paper can be obviously bad in ways that a good paper can’t be obviously good.
Webinar: An introduction to Bayesian multilevel modeling with brms
Webinar: An introduction to Bayesian multilevel modeling with brms Posted by Eric Novik on 13 April 2021, 3:31 pm 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.
Some issues when using MRP to model attitudes on a gun control attitude question on a 1-4 scale
Some issues when using MRP to model attitudes on a gun control attitude question on a 1–4 scale Posted by Andrew on 8 April 2021, 9:15 am 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 from multiple weeks (dynamic) Do you know of any example...
Statistical Modeling, Causal Inference, and Social Science
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. Two natural state-level predictors are Republican vote share and percent rural. These variables are also highly correlated.
Statistical Modeling, Causal Inference, and Social Science
The 5-sigma rule in physics Posted by Andrew on 14 March 2021, 9:24 am 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 about 5-sigma.
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
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 Posted by Andrew on 13 March 2021, 9:16 am 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.
Statistical Modeling, Causal Inference, and Social Science
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 tends to ramp up when there are symptomatic outbreaks, which prevents the data from being representative of the population as a whole.
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 . . .
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.” " Statistical Modeling, Causal Inf
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.” Posted by Andrew on 3 February 2021, 9:02 am In recent weeks we’ve been hearing a lot about the priority of vaccinations. Should we be vaccinating older people first? Essential workers?
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 noisy and effect sizes are small. (More here.) Saying it that way, it’s obvious: Bayesian methods are calibrated if you average over the prior.
Statistical Modeling, Causal Inference, and Social Science
Hierarchical stacking Posted by Yuling Yao on 25 January 2021, 2:00 pm (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 full-Bayesian hierarchical modeling.
Routine hospital-based SARS-CoV-2 testing outperforms state-based data in predicting clinical burden.
Routine hospital-based SARS-CoV-2 testing outperforms state-based data in predicting clinical burden. Posted by Andrew on 13 January 2021, 9:06 am 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.
Statistical Modeling, Causal Inference, and Social Science
“They adjusted for three hundred confounders.” Posted by Andrew on 12 January 2021, 9:39 am 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.
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. " Statistical Modeling,
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. Posted by Andrew on 10 January 2021, 9:26 am 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.
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond Posted by Andrew on 1 December 2020, 11:05 am 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.
Further formalization of the “multiverse” idea in statistical modeling
Further formalization of the “multiverse” idea in statistical modeling Posted by Andrew on 24 November 2020, 9:36 am 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.
Statistical Modeling, Causal Inference, and Social Science
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 consolidating power.
You don’t need a retina specialist to know which way the wind blows
You don’t need a retina specialist to know which way the wind blows Posted by Andrew on 15 November 2020, 9:12 am 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 normal priors.
Merlin and me talk on the Bayesian podcast about forecasting the election
Merlin and me talk on the Bayesian podcast about forecasting the election Posted by Andrew on 2 November 2020, 7:42 am 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, click on Episodes, and scroll down.
Between-state correlations and weird conditional forecasts: the correlation depends on where you are in the distribution
Yup, here’s more on the topic, and this post won’t be the last, either . . . Jed Grabman writes: I was intrigued by the observations you made this summer about FiveThirtyEight’s handling of between-state correlations. I spent quite a bit of time looking into the topic and came to the following conclusions. In order for Trump to win a blue state, either: 1.) That state must swing hard to the right or 2.) The nation must have an swing toward Trump or 3.) A combination of the previous two factors.
Faster than ever before: Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation
Faster than ever before: Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation Posted by Andrew on 10 June 2020, 9:38 am 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.
Why do a within-person rather than a between-person experiment?
Zach Horne writes: A student of mine was presenting at the annual meeting of the Law and Society Association. She sent me this note after she gave her talk: I presented some research at LSA which used a within subject design. I got attacked during the Q&A session for using a within subjects design and a few people said it doesn’t mean much unless I can replicate it with a between subjects design and it has no ecological validity.
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