I’m curious if anyone has quick thoughts/guides on how best to test whether demographic estimates across geographic units are statistically significant. If this question is a bit too broad in scope I totally understand. Thank you!
As an example, let’s assume I pull ACS data with county-level indicators and information for respondent race. Imagine I use data from the 2019 ACS subset to Virginia. I find that County A has 25.7% individuals who identify as black or African American (using the RACBLK variable). County B is at 28%. This is obviously a made up example, but I’m particularly interested in when estimates like these are close.
If I wanted to test whether the difference in demographics b/w these two counties is significant – what method(s) may be best suited given the structure of ACS data (specifically the IPUMS extracts)? My thoughts:
T-test: I am using the survey package (Survey Data Analysis with R) I know there is a svyttest() function, and I know it would be a 2 sample unpaired test, but I can’t quite wrap my head around the best way to set up the call for comparing sub-populations of the same larger dataset.
Boot-strap type approach: Another thought I had was to kind of boot strap it, where I take a large number of random samples from each county and compare the distribution of means from these samples that way (accounting for person weight) through a t-test manually.
Z-score: I was also referred to the following link - Statistical Testing Tool - which seems to substantively fit perfect for what I’m looking for. However this requires you’re pulling data from Census tables, doesn’t seem to work as easily for ACS extracts. Furthermore, for cases where we’re creating our own geographic units Id’ still want to be able to test across units.
Any/all thoughts would be appreciated.