(EDITED FOR CLARITY) I have been looking around, and I can't seem the find the answer to my question. Apologies if it has been hiding in plain sight.
In order to obtain school district-level information, I have been using American Community Survey data obtained from American FactFinder. Variables I am pulling include demographic characteristics such as median income and percent of citizens over 65 years old. Five-year estimate American Community Survey data are provided for all geographic regions, whereas one-year estimate data are not provided for smaller regions. As such, there are estimates from 2005-2009, 2006-2010, etc. I understand that these data represent reliable estimates for geographies over a period and should not be used as estimates for a single year of data. (Also, note that I am not thinking yet about the standard errors in this question.)
How, then, can we use these data in data analyses where other data sources are available at the year level? Note that I do not want to use the one-year estimates because of reliability and availability reasons.
PART A: For example, if we want to use median income in a model for 2009, and we use the five-year estimates from ACS, which year would we use? 2005-2009? 2006-2010?
PART B: Let's say we use 2005-2009. I know from the documentation that we should not pretend as if any of the period data represent data from a single year. Would it be accurate to say, in this example model, that using 2005-2009 estimate median income data as a covariate would be "controlling for approximated median income data from the last five year period"?
PART C: AND, if that is the case, we are missing out on larger year-to-year changes, since the five-year estimates smooth the data trends. In this case, should we maybe then run limited models using one-year estimates as robustness checks?
(Does anyone have any resources with people using census data in this way?)
Note: I have thus far been downloading data from American Factfinder because I need it at the school district level.