jaybenjeff.bsky.social
A non-practicing intellectual, stats modeler, fan of measurement & psychometrics, and #rstats nerd at @unlincoln.bsky.social
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๐
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Perfect! ๐๐ผ
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โ Pisces (Feb 19 - Mar 20) โ {quarto}
Dreamy, adaptable, sentimental. Loves a good narrative, but sometimes gets lost in the details. Might spend hours formatting a document instead of finishing the analysis. ๐โจ (4/13)
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Thanks for this explanation!
Can you think of an example where the \(x) approach would be more appropriate over the ~function() approach?
I often rely on the latter but would love to know when I need to resort to \(x) ๐
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Yep, I too struggle with this! One thing I find myself frequently doing is asking OneDrive to โkeep on deviceโ any active Rproj files.. otherwise I sit there and wait for it to manually pull down.. ๐ฅฒ
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๐
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Rumor has it, thereโs likely not substantial differences between the two ยฏ\_(ใ)_/ยฏ
As far as Iโve read: clinimetrics are more concerned with cumulative indices, causal-indicator models, and measurement models with uncorrelated items.. Basically what formative models are for psychometricians.
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Okay โ wow โ a lot to unpack in that finale but the goosebumps that escaped my body when [redacted name] appeared on the [spoiler] in the cafeteria.. ๐ซจ
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Silo! ๐ผ๏ธ
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Agree! Though, it still takes a bit of time to download larger files with Qualtrics ๐
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@tkaiser.science et al. (2021). The distinction between psychometric and clinimetric approaches will not move the field forward. doi.org/10.31234/osf...
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This comment is /exactly/ what my first impression has been.
Thank you for sharing and +1 for open access!
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Stepping back from growth modeling, pre/post changes in a latent variable context are very common via an interaction term predictor (post*Tx group), i.e., moderation.
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Seems like you could fit a growth/spline/change score model and enter a group membership variable (0 = control) as a predictor of growth effect after the intervention. Right?
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Necessary updates!
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The goal was to identify all the things that could explain a null/non-significant finding in a survey experiment *other than* a theory/hypothesis being incorrect. If you can rule out these 7 "alternative explanations", your null results become much more informative. ๐