daveasiegel.bsky.social
Professor of Political Science and Public Policy at Duke University. Associate Editor (Formal Theory) of the AJPS. daveasiegel.com
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🚩
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Great news!
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Thanks for this! I'd love to be added, if possible.
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Thanks for putting this together! Would love to be added.
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It's here: github.com/dasiegel/IRT-M
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Forgot the figure!
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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)
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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)
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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)
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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)
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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)
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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)
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There’s an accompanying R package with a vignette as well here: github.com/dasiegel/IRT-M. (2/8).
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Thanks, Charles!
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Thanks, Brad!
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Thank you!
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"Duke said its policy is to keep such investigations — even their existence — confidential, declining to comment further." In what way is this possibly good policy?
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You're welcome!
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Thanks! I'm glad it was of use to you.
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Thanks, Adam; that's kind of you! I hope you're doing well!
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Thanks, Seth! Hope you're doing well!
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Thanks!
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Please add me to polisky. And thanks for doing this!