@LJZigerell @KirkegaardEmil btw- NAEP vs single-parenthood vs (male) suspension rates by state and by racepic.twitter.com/mUZAJPGJH8
@RCAFDM @LJZigerell looks like NAEP isn't the whole explanation. Try redoing the S analysis with the new variable.
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@KirkegaardEmil@LJZigerell yeah that's why I asked if you had S-factor data by race & state earlier. btw-i ran this analysis at a school+ -
@RCAFDM@LJZigerell And I gave you the data. ;) However, I did not publish my study of the race-state stuff yet, so I'd appreciate it if -
@RCAFDM@LJZigerell you let me publish that one first. :P#firstmoveradvantage
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Thanks. Twitter will use this to make your timeline better. UndoUndo
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@KirkegaardEmil@LJZigerell btw found this correlation w/ national aggregates to be shockingly goodpic.twitter.com/3v7r9HsOuK
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@RCAFDM@LJZigerell Yes, aggregate-level stats often go to near 1. In this case, I'd say both load strongly on S and have common causes. -
@KirkegaardEmil@LJZigerell probably, though i'm pretty sure you wouldn't find this w/ income, poverty rates, education, etc though -
@RCAFDM@LJZigerell I'm sure you could write a script that does the same analysis for all variable pairs with available data. ;)
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