New piece from me @chriswarshaw.bsky.social , @devincaughey.bsky.social , and Bernard Fraga.

Story: though we've entered a high turnout era, turnout gaps between racial groups have reemerged.

Nerdy story: Using publicly available data, we've generated new, reliable turnout estimates over time.

Helpful data on our current situation:

www.cato.org/blog/politic...

The logical dependence in the outcome would seem to rule out many standard methods for panel causal inference. In other contexts one would fit a survival model, but that sort of thing isn't too common in panel CI. Thoughts? Maybe PanelMatch would work because it matches on lagged Y?

(Trying the sort of thing that used to work on Twitter):

Suppose you have panel data with a binary treatment and an outcome that indicates whether something has happened yet (e.g., whether a policy has been passed). How would you estimate treatment effects? 1/2

#polisky

Currently in FirstView: “MODGIRT: Multidimensional Dynamic Scaling of Aggregate Survey Data” by Elissa Berwick and @devincaughey.bsky.social. While traditional public opinion models are restricted to unidimensional latent traits, MODGIRT allows for a multidimensional structure of preferences.

100 years ago Saturday the NYT ran a story that did not hold up well at all.
www.nytimes.com/1924/12/21/a...

Re methods research, Stephen Jessee and @jerzakconnor.bsky.social have a working paper on dealing with measurement error in latent variables. It too deprecates MOC and proposes an IV-based approach instead.

Put differently: even the best scholars produce bad work, and even more often produce work that is not a good fit for a given outlet. A paper is not a person.

Yeah I hear you on me(). MO is faster. The advantage of both over MOC is allowing other variables in the analysis model to inform estimates of the missing values rather than assuming them to be independent as MOC does (see the appendix to Treier and Jackman).

It’s true! Though I have come to the conclusion that MOC is more assumption-dependent than is commonly realized, and it is often better to use @mattblackwell.bsky.social et al.’s “multiple overimputation” or to use Bayesian measurement-error model, which brms makes pretty easy.

I am Gen X (barely) and have said “on accident” as long as I can remember. Didn’t realize it was nonstandard until I was made fun of for it it as an adult.

Three positions at MIT between social sciences (broadly considered) and computing. For two (Sloan and Urban Studies & Planning) apply ASAP as review of applications has begun.

What makes a great comic strip work? For the week of Halloween, an appreciative breakdown of a classic monster tale from Gary Larson’s The Far Side, with more in-depth critical analysis than you probably want or would think possible!

Theoretically either is sufficient to drive the approximate bias to zero, but given that this is impossible in practice, it’s definitely better to do both. Also, doing it for the outcome has the additional advantage of reducing variance.

For a theoretical treatment see Sarndal and Lundstrom’s 2006 textbook, which shows that the approximate bias under calibration is zero only if the outcome or inverse response probabilities are a linear function of the auxiliary vector. In raking the aux vec is the marginal dist; in PS the joint.

I mean short “I”

On my first listen I was pretty sure that it was just an aborted mispronunciation of “migrants” with a short a (as in “immigration”), but after a second listen it sure does sound a lot like something else.

If you’re looking for more inspiration, you can check out my “political science scope and methods” syllabus, which has a few sessions on philosophy of (social) science: devincaughey.github.io/files/syllab....

For general overviews, I like Godfrey-Smith’s textbook and the Cartwright and Montuschi Philosophy of Social Science edited volume.

Looks great! And just in time to make my syllabus for the fall.

I’m honestly a little surprised bc my impression was that speed improvements from within-chain parallelization were nonmonotonic in the number of cores due to the fixed costs of each additional core, and 64 is way beyond the
point where I thought performance declined. But maybe that’s outdated?

64!

Coming in 2024! Cover art day is a good day.

There are also blurbs and you can even order a copy: tinyurl.com/4e35mjef

Intro chapter: www.ocf.berkeley.edu/~gailmard/em...

🚨Forthcoming Must Read!!!

The Political Development of American Debt Relief
Emily Zackin and Chloe N. Thurston (@chloethurston.bsky.social)

Available now for pre-order, May 2024 UChicago Press Polisky press.uchicago.edu/ucp/books/bo...