@LJZigerell @KirkegaardEmil btw- NAEP vs single-parenthood vs (male) suspension rates by state and by racepic.twitter.com/mUZAJPGJH8
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@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.
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@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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@KirkegaardEmil@LJZigerell btw i compared national aggregates w/ some other predictors ( NAEP, $$, and parent ed)pic.twitter.com/YNvwohatzZ
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@RCAFDM@LJZigerell Looks like there is some group factor variance. Pure S factor model implies r=1. -
@KirkegaardEmil@LJZigerell do you know of a good tool to do fuzzy string matching in R? - End of conversation
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