@LJZigerell @KirkegaardEmil btw- NAEP vs single-parenthood vs (male) suspension rates by state and by racepic.twitter.com/mUZAJPGJH8
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@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.
@KirkegaardEmil @LJZigerell btw found this correlation w/ national aggregates to be shockingly goodpic.twitter.com/3v7r9HsOuK
@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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