Dave Siegel

Probably should've tagged this thread: #Polisky #PolScience

This was very much a group effort. Most of my co-authors aren't here, but I'll tag the amazing @margaretfoster.bsky.social. We hope this tool can be widely useful, and are happy to answer any questions you might have! (7/7)

Right now one needs to convert to binary input data to use IRTM, but we're also working on extensions to other data types. We provide a vignette in the package to walk the user through IRTM's use. (6/7)

Because you can run IRTM on large datasets with complex loading constraints, it opens up theoretically-driven measurement to a lot more contexts. We focus on the importance of matching measurement to theory in the paper, but are working on making clearer the range of uses in ongoing work. (5/7)

It's also user-friendly, requiring little coding or package knowledge: just give it input data and a constraint matrix for the pre-specification, and it will provide individual-level posterior distributions over latent dimensions. No need to learn any package-specific syntax. (4/7)

IRTM trades off some generality in specification, as packages such as brms or blavaan can achieve, for substantially increased speed: it runs on data with 10k respondents and 200+ items with 4 latent dimensions in about 10 minutes, regardless of the complexity of the pre-specified connections. 3/7

IRTM is a Bayesian implementation of an Item Response Theory model in a similar vein to other constrained approaches such as BCFA or BSEM: it allows the user to pre-specify connections between items/questions and theoretically-coherent latent dimensions. (2/7)

Happy to see that this is now out. The R package associated with the project, IRTM, can now be found on CRAN. Or can you still download it from my github. We'll continue to update it over time. This is a brief thread about what IRTM can do. 🧵 (1/7)

From our new issue: "Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models" by Marco Morucci, Margaret Foster, Kaitlyn Webster, So Jin Lee, and David Siegel. #APSRNewIssue www.cambridge.org/core/journal...

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Great news!

Thanks for this! I'd love to be added, if possible.

Thanks for putting this together! Would love to be added.

if you want a break from the election, we've posted a revision of our paper on pundits and ideological coalitions. bigger emphasis in this draft on the point that political ideologies aren't quite the same thing as political philosophies osf.io/xfy8r

I said this in my first-year grad research methods class, but it bears repeating. There are people who will tell you that you are not smart enough to understand something. Those people either don't understand the nature of intelligence, don't have your best interests at heart, or both. Ignore them.

Paper title page (too long for alt text)

Figure 2: Effect of Women's Numbers on the Female-to-Male Ratio of Words Spoken

Just accepted at JOP w/ Daniel Naftel @kshoub.bsky.social, @jarededgerton.bsky.social, Mallory Wagner, and Skyler Cranmer. We find that patterns of gender inequality observed in formal deliberative settings extend to informal televised political discussions. www.journals.uchicago.edu/doi/10.1086/...

GitHub - dasiegel/IRT-M: IRT-M R package IRT-M R package. Contribute to dasiegel/IRT-M development by creating an account on GitHub.

It's here: github.com/dasiegel/IRT-M

Distributions of latent perceptions of threats, grouped by attention to media.

Forgot the figure!

This is NSF-funded work, joint with Marco Morucci, Margaret Foster, Katie Webster, and So Jin Lee. None of whom are on Bluesky yet. (8/8)

We think the framework has the potential to help a lot of measurement problems. We’re working right now to extend the input data from dichotomous to multichotomous and continuous as well. It may be of interest to #econsky as well. (7/8)

There’s a lot more in the paper. For example, the figure shows how IRT-M can produce estimates of abstract concepts (sense of "threat", by the media sources that they trust) in data not designed to measure the concepts (the Eurobarometer survey). (6/8)

The package does the rest. The latent dimensions it captures can be correlated, and IRT-M discovers any such correlation from the data. The supervised steps ensure that the measures remain consistent across time and space. (5/8)

Then, researchers identify data sources and assess how the latent dimensions would show up in the data, which can depend on context. Then, they construct a constraint matrix that encodes dependencies between items in the data and the latent dimensions. (4/8)

We call this framework IRT-M, and it makes it easier for researchers to construct, measure, and present subtle or abstract concepts in their data. It’s a semi-supervised method. First, researchers identify theoretically meaningful latent dimensions. (3/8)

GitHub - dasiegel/IRT-M: IRT-M R package IRT-M R package. Contribute to dasiegel/IRT-M development by creating an account on GitHub.

There’s an accompanying R package with a vignette as well here: github.com/dasiegel/IRT-M. (2/8).

I’m pleased to announce to #polisky our forthcoming APSR article, “Measurement that Matches Theory.” In it, we introduce a Bayesian IRT framework that allows users to identify latent dimensions with substantive theoretical meaning. The paper is open access for now here: tinyurl.com/48ktz5jc. (1/8)🧵

Can money buy control of Congress? Can a political party spend enough across electoral campaigns to garner a majority within the U.S. Congress? Prior research on campaign spending minimizes the importance of campaign heterogeneity and ...

In case you don't want to watch the debate, but do want to think about elections, you can head over to journals.plos.org/plosone/arti... and check out our new paper on campaign spending (#polisky). We estimate the effects of money at the levels of both the individual campaign and the whole Congress.

Post-January 6th deplatforming reduced the reach of misinformation on Twitter - Nature Difference-in-differences analysis indicates that the decision by Twitter to deplatform 70,000 users following the events at the US Capitol on 6 January 2021 had wider effects on the spread of mi...

New paper in Nature looking at the direct *and spillover* effects of Twitter deplatforming ~70k accounts following January 6th:
- less misinfo for remaining users who had followed deplatformed users to circulate
- some remaining users voluntarily exited
www.nature.com/articles/s41...

Thanks, Charles!

Thanks, Brad!