Estimating Erroneous Enumerations in the U.S. Decennial Census Using Four Lists
Biemer, P. P. (2004). Estimating Erroneous Enumerations in the U.S. Decennial Census Using Four Lists. In .
In this paper we present a method adjusting the US decennial census day counts using a system of four lists (Census, PES, MER, ARL). The primary concern is developing a method that can detect erroneous enumerations that are present in the data. Failure to account for erroneous enumerations in the lists will result in overestimation of the census day residents. We use four lists that are indicators of residency for obtaining estimates of erroneous enumerations present lists. The method is similar to detecting heterogeneity in a capture-recapture experiment. The erroneous enumerations are assumed to come from a >non-resident= population that is rostered on the four lists with different probabilities then the actual census day >resident= population. Using these four lists, Latent Class Analysis and the software package lEM we were able to fit several reasonable models that reflect likely census populations. We discuss the assumptions required for the inference to be valid and indicate the robustness of these models to the violation of these assumptions. The results from a simulation study will be presented that show when our method is reliable and situations in which it fails.