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KirkegaardEmil's profile
Emil O W Kirkegaard
Emil O W Kirkegaard
Emil O W Kirkegaard
@KirkegaardEmil

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Emil O W Kirkegaard

@KirkegaardEmil

#psychology #genomics #hbd #rstats #statistics #genomics #transhumanism #dataviz #openscience #psychometrics @OpenPsychJour

Denmark
emilkirkegaard.dk
Joined January 2012

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    1. Jordan Theriault‏ @j_theriault Oct 11
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      Has anyone else watched John Kruschke's lecture on ordinal analysis? https://media.dlib.indiana.edu/media_objects/mp48sh50s … Based on this paper: https://www.sciencedirect.com/science/article/pii/S0022103117307746 … Am I wrong in interpreting this to mean basically every Likert study is thrown into question, with no way to diagnose, short of remodeling?

      2 replies 7 retweets 22 likes
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    2. Tal Yarkoni‏ @talyarkoni Oct 12
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      Replying to @j_theriault

      lovely paper. the argument re: not using a metric model seems reasonable to me, though I think the paper is probably a bit too optimistic about the utility of the ordered-probit model, which is also clearly a crummy model of the DGP in many (perhaps most?) cases.

      1 reply 0 retweets 1 like
    3. Tal Yarkoni‏ @talyarkoni Oct 12
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      Replying to @talyarkoni @j_theriault

      e.g., look at Figures 5 and 6. that the metric model messes up is clear. but do we really want to conclude that if not for measurement error/limitations, we would observe a latent mu of 12.21 for movie 30? what would that even mean?

      2 replies 0 retweets 1 like
    4. Ruben C. Arslan‏ @rubenarslan Oct 12
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      Replying to @talyarkoni @j_theriault

      Is this the paper where they basically automatically looked for comparisons where ordinal would mismatch metric results? That didn't seem too convincing to me. If they are right, there would be tons of famous papers they could base this on.

      1 reply 0 retweets 1 like
    5. Ruben C. Arslan‏ @rubenarslan Oct 12
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      Replying to @rubenarslan @talyarkoni @j_theriault

      I've fit both ordinal and metric models to much of the Likert data I've worked with and the number of cases where inferences diverged much were few and much more circumscribed (e.g. spurious interaction effect when one variable has a strong main effect towards ceiling/floor)

      3 replies 0 retweets 2 likes
      Emil O W Kirkegaard‏ @KirkegaardEmil Oct 12
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      Replying to @rubenarslan @talyarkoni @j_theriault

      Calls for a phenome wide study in some large dataset. I agree with the experience.

      2:12 PM - 12 Oct 2019
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