The American Community Survey Summary of a Workshop (2001) / Chapter Skim
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7. Calibration of the Long Form to ACS Output
7. Calibration of the Long Form to ACS Output
Pages 41-46
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From page 41... ...
, and the long-form output in 2000. The development of this calibration model will be challenging, since not only will individual-level matching of the long form to the ACS not be possible in 2000 due to the designs of the long form and the ACS data collections, but also the ACS sample sizes prior to full implementation will be substantially smaller than for the full implementation.
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From page 42... ...
This issue was not directly addressed at the workshop, except that it was pointed out that, with calibrated long-form data, examining mean square errors for the ACS relative to the long form, possibly at some temporal or geographically aggregated level within various categories, could provide substantial information about the bias of the ACS. This was an approach taken in the panel study of small-area estimates of poverty.
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From page 43... ...
In remote sensing, one is comparing the differences between a map based on remote sensing and ground truth, both of which are subject to measurement error, features on the map are also subject to substantial sampling variance, and there are some covariates to assist in understanding differences between ground truth and the map. There is also stratification, clustering, nonresponse, mode and instrument differences, a temporal displacement problem, and spatial displacement, all of which make the analogy relatively useful.
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From page 44... ...
Development of the calibration model could be considered as a long-form missing data problem in which the long-form data for the 700,000 housing units are all missing, or one could think of it as an ACS missing data problem, in which one has 700,000 of the 3 million ACS questionnaires for 2000, and all of the associated long-form responses are also missing.2 Considering this as a missing data problem leads one to consider some sort of imputation approach at some level of geographic aggregation, attempting to mimic the joint distributional properties of the long-form and ACS responses. It would be useful to attempt this at the lowest possible level of geographic aggregation, so that users could have flexibility in aggregating the estimates.
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From page 45... ...
Therefore, one would have to accomplish the modeling of this factor at some higher level of aggregation. The fitting of such a model would likely require computational methods, such as Markov chain Monte Carlo sampling, to estimate posterior means, posterior variances, and posterior quartiles and to replicate posterior predictions: that is, make multiple imputations.
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From page 46... ...
Finally, whatever is done, uncertainty measures are needed, and the entire process needs to be thoroughly documented. FINAL POINTS Some proposed calibration models demonstrated the complexities faced by the Census Bureau in developing such a model to link the long form and the ACS.
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