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Archive of posts filed under the Multilevel Modeling category.

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 […]

“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 […]

Probabilistic feature analysis of facial perception of emotions

With Michel Meulders, Paul De Boeck, and Iven Van Mechelen, from 2005 (but the research was done several years earlier): According to the hypothesis of configural encoding, the spatial relationships between the parts of the face function as an additional source of information in the facial perception of emotions. The paper analyses experimental data on […]

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 […]

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 […]

The accidental experiment that saved 700 lives

Paul Alper sends along this news article by Sarah Kliff, who writes: Three years ago, 3.9 million Americans received a plain-looking envelope from the Internal Revenue Service. Inside was a letter stating that they had recently paid a fine for not carrying health insurance and suggesting possible ways to enroll in coverage. . . . […]

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 […]

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 […]

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 […]

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 […]

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 […]

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, […]