Hello, new to the forum
I’m exploring data in the ACS 5-year survey data. I thought I’d look at the census tract-level, but notice that in many tables the data has large margins of error, with a coefficient of variation 50% or more (screenshot).
When looking at ACS 5-year data, what’s the smallest geographic slice you look at? Seems like the zip code is where margin of error comes within a tolerable range? Of course would depend on the specific table. Tables that include multi-dimensional demographic slicing probably still have a high MOE at the zip code level.
Thanks,
As someone new and not into stats etc. I pulled some key measures ACS1 by county and found most had data (not -99999999 ). However, there were some key race measures (Black and Hispanic for example) with a lot of -999999. I think there actually was a state that was missing one or more races for the ACS1. I think S2701_C01_017E and S2701_C01_023E stood out the most. I assume that the ACS5 will resolve most of these issues at the county and lower levels. I'm going to compare ACS1 and ACS5 for the 900 counties in ACS1 to see what the differences are. I will use around 70 measures. When I'm done, I'll paste the 70 measures ACS1 vs ACS5 in a discussion here (just for reference in case anyone wants to see it). I could also do something with margin or errors, but for all the Public Health websites and more, they just take whatever the data values are without regard to margin of error, so I think I will too. No heavy lifting, just using the published numbers. Not that I could if I tried, not in your league but I'm Ok with that.
These are a few of the measures and what percent of the 900 counties have data for ACS1 (not -9999999). So later perhaps 4 columns with percent with data and the actual numbers by diff ACS1 vs ACS5 Top level, no mix and match (by Age by Race, by Poverty)