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Archive of posts filed under the Multilevel Modeling category.

Where does Mister P draw the line?

Bill Harris writes: Mr. P is pretty impressive, but I’m not sure how far to push him in particular and MLM [multilevel modeling] in general. Mr. P and MLM certainly seem to do well with problems such as eight schools, radon, or the Xbox survey. In those cases, one can make reasonable claims that the […]

Applied regression and multilevel modeling books using Stan

Edo Navot writes: Are there any plans in the works to update your book with Prof. Hill on hierarchical models to a new edition with example code in Stan? Yes, we are planning to break it up into 2 books and do all the modeling for both books in Stan. It’s waiting on some new […]

“Best Linear Unbiased Prediction” is exactly like the Holy Roman Empire

Dan Gianola pointed me to this article, “One Hundred Years of Statistical Developments in Animal Breeding,” coauthored with Guilherme Rosa, which begins: Statistical methodology has played a key role in scientific animal breeding. Approximately one hundred years of statistical developments in animal breeding are reviewed. Some of the scientific foundations of the field are discussed, […]

A quick one

Fabio Rojas asks: Should I do Bonferroni adjustments? Pros? Cons? Do you have a blog post on this? Most social scientists don’t seem to be aware of this issue. My short answer is that if you’re fitting mutlilevel models, I don’t think you need multiple comparisons adjustments; see here.

My talk at MIT this Thursday

When I was a student at MIT, there was no statistics department. I took a statistics course from Stephan Morgenthaler and liked it. (I’d already taken probability and stochastic processes back at the University of Maryland; my instructor in the latter class was Prof. Grace Yang, who was super-nice. I couldn’t follow half of what […]

Collaborative filtering, hierarchical modeling, and . . . speed dating

Jonah Sinick posted a few things on the famous speed-dating dataset and writes: The main element that I seem to have been missing is principal component analysis of the different rating types. The basic situation is that the first PC is something that people are roughly equally responsive to, while people vary a lot with […]

Social networks spread disease—but they also spread practices that reduce disease

I recently posted on the sister blog regarding a paper by Jon Zelner, James Trostle, Jason Goldstick, William Cevallos, James House, and Joseph Eisenberg, “Social Connectedness and Disease Transmission: Social Organization, Cohesion, Village Context, and Infection Risk in Rural Ecuador.” Zelner follows up: This made me think of my favorite figure from this paper, which […]

Statistical analysis on a dataset that consists of a population

This is an oldie but a goodie. Donna Towns writes: I am wondering if you could help me solve an ongoing debate? My colleagues and I are discussing (disagreeing) on the ability of a researcher to analyze information on a population. My colleagues are sure that a researcher is unable to perform statistical analysis on […]

Instead of worrying about multiple hypothesis correction, just fit a hierarchical model.

Pejman Mohammadi writes: I’m concerned with a problem in multiple hypothesis correction and, despite having read your article [with Jennifer and Masanao] on not being concerned about it, I was hoping I could seek your advice. Specifically, I’m interested in multiple hypothesis testing problem in cases when the test is done with a discrete finite […]

Why I don’t use the terms “fixed” and “random” (again)

A couple months ago we discussed this question from Sean de Hoon: In many cross-national comparative studies, mixed effects models are being used in which a number of slopes are fixed and the slopes of one or two variables of interested are allowed to vary across countries. The aim is often then to explain the […]