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Archive of posts filed under the Multilevel Modeling category.

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?

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). However, you have also talked about the problems with noisy measures and […]

John Cook: “Students are disturbed when they find out that Newtonian mechanics ‘only’ works over a couple dozen orders of magnitude. They’d really freak out if they realized how few theories work well when applied over two orders of magnitude.”

Following up on our post from this morning about scale-free parameterization of statistical models, Cook writes: The scale issue is important. I know you’ve written about that before, that models are implicitly fit to data over some range, and extrapolation beyond that range is perilous. The world is only locally linear, at best. Students are […]

From “Mathematical simplicity is not always the same as conceptual simplicity” to scale-free parameterization and its connection to hierarchical models

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. A (somewhat) complicated mathematical expression can give some clarity, as the reader can see how each part of the formula corresponds to a different […]

“Sources must lose credibility when it is shown they promote falsehoods, even more when they never take accountability for those falsehoods.”

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. Lots of people make […]

Job opening at the U.S. Government Accountability Office

Sam Portnow writes: I am a statistician at the U.S. Government Accountability Office, and we are hiring for a statistician. The full job announcement is below. Personally, I think our office is a really great place to do social science research within the federal government. ———————————————————————- The U.S. Government Accountability Office (GAO) has two vacancies […]

MRP and Missing Data Question

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 […]

Postdoc position in Bayesian modeling for cancer

Wesley Tansey writes: I’m recruiting a postdoc to join my lab at Memorial Sloan Kettering Cancer Center (tanseyw@mskcc.org). The role overlaps a lot with the interests of people on your blog. We’re specifically looking for people with experience in subset of the following: – Bayesian hierarchical models – Spatial statistical methods (e.g. Gaussian processes, trend […]

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. Before recent case surge in south Asia, we have seen stories claiming that the pandemic might have hit some low-income […]

Blast from the past

Paul Alper points us to this news article, The Secret Tricks Hidden Inside Restaurant Menus, which is full of fun bits: There is now an entire industry known as “menu engineering”, dedicated to designing menus that convey certain messages to customers, encouraging them to spend more and make them want to come back for a […]

size of bubbles in a bubble chart

(This post is by Yuling, not Andrew.) We like bubble charts. In particular, it is the go-to visualization template for binary outcomes (voting, election turnout, mortality…): stratify observations into groups, draw a scatter plot of proportions versus group feature, and use the bubble size to communicate the “group size”. To be concrete, below is a graph […]

Whassup with the weird state borders on this vaccine hesitancy map?

Luke Vrotsos writes: I thought you might find this interesting because it relates to questionable statistics getting a lot of media coverage. HHS has a set of county-level vaccine hesitancy estimates that I saw in the NYT this morning in this front-page article. It’s also been covered in the LA Times and lots of local […]

“The Multiverse of Methods: Extending the Multiverse Analysis to Address Data-Collection Decisions”

Jenna Harder writes: When analyzing data, researchers may have multiple reasonable options for the many decisions they must make about the data—for example, how to code a variable or which participants to exclude. Therefore, there exists a multiverse of possible data sets. A classic multiverse analysis involves performing a given analysis on every potential data […]

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 […]

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 […]

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