Calculating MOEs from derived ACS estimates

Over the years, we have heard from a lot of people interested in getting an easy-to-use tool that would help people calculate margins of error from derived ACS data (e.g., data combined across categories or geographies). There are several organizations that have developed some basic applications that might be useful. Here are the links:

sdcclearinghouse.wordpress.com/.../
www.psc.isr.umich.edu/.../
pad.human.cornell.edu/.../
fyi.uwex.edu/.../
www.demography.state.mn.us/.../StatisticalCalculationsMenu.xls

If you are using a different application in your organization, feel free to post it here.
Parents
  • Yes, I think those were the suggestions of the Census Bureau person. 1. use the PUMS or 2. Request a special tabulation (fee based).

    The only other alternative I could come up with was, when I have some groups I want to aggregate, find some other tables that present that aggregate group and the variable is sort of similar, and see what the MOE is for that table.

    So, for example, if I want to aggregate children under 5 below 185% of poverty (presented at american factfinder as several age groups), look at the tables for children under 5 below 100% of poverty, which is already aggregated by the ACS into one group and has an MOE with the table. The MOEs won't be the same, but might be similar.
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  • Yes, I think those were the suggestions of the Census Bureau person. 1. use the PUMS or 2. Request a special tabulation (fee based).

    The only other alternative I could come up with was, when I have some groups I want to aggregate, find some other tables that present that aggregate group and the variable is sort of similar, and see what the MOE is for that table.

    So, for example, if I want to aggregate children under 5 below 185% of poverty (presented at american factfinder as several age groups), look at the tables for children under 5 below 100% of poverty, which is already aggregated by the ACS into one group and has an MOE with the table. The MOEs won't be the same, but might be similar.
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