Coverage Measurement in the 2010 Census (2009) / Chapter Skim
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Appendix B: Logistic Regression for Modeling Match and Correct Enumeration Rates
Appendix B: Logistic Regression for Modeling Match and Correct Enumeration Rates
Pages 153-156
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From page 153... ...
and the limited amount of data for each, these effects are more naturally represented as random effects. By including these random effects in the logistic regression models, the Census Bureau could estimate the effects of individual offices on match and correct enumeration rates and obtain valid estimates of the contribution of variability across offices to uncertainty about coverage rates in each area.
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From page 154... ...
In this approach, both logistic regression models (for match rate and correct enumeration rate) have the following generic form: p log i , k = βi + µ k + α i , k , 1 − pi , k where bi is the fixed effect for ith poststratum membership, mk is a random effect for the kth local census office, and aik is model error.
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From page 155... ...
the census count plus people found in the P-sample who were omitted in the census for each block cluster, suggested that coarser but consistent poststrata may have provided more accurate estimates of net coverage error than finer poststratifications based on different E- and P-sample stratifications. However, for large blocks with proxy rates greater than 10 percent, the finer and inconsistent poststrata performed better.
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From page 156... ...
However, this should be relatively straightforward in either SAS or R, which are two standard statistical software systems that the Census Bureau uses. Loss Function or Objective Functions for Assessing Fit of Models. Another complication is that the current loss function underlying the fitting of the coefficients of these logistic regression models is implicit in the separate likelihood equations for the two models and is therefore somewhat dis connected from the ultimate goal, which is to predict the population size or, what amounts to the same thing, net coverage error.
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