The American Community Survey Summary of a Workshop (2001) / Chapter Skim
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5. Weighting and Imputation
5. Weighting and Imputation
Pages 25-33
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From page 25... ...
, mode bias, differences in nonresponse in individuals and households, so that housing unit counts agree with the totals on the master address file (two separate factors) , differences between marginal population totals and totals based on demographic analysis resulting from census undercoverage (referred to as a person post-stratification factor)
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From page 26... ...
At least one of the weighting factors results from the Census Bureau's decision to have a given month's estimates make use of data collected during that month, rather than data originating from the sample selected for that month. This was done to provide data that would likely have less mean square error, based on the reasoning that it was easier for the respondent to provide reliable information for the month of data collection (reducing recall bias)
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From page 27... ...
, which provides a technique that might be applicable to the ACS when using external information. 2Raking refers to a constant multiplicative adjustment within a row or a column so that the rows or columns of the revised table add up to a given marginal row or column total.
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From page 28... ...
While there are advantages, it does necessitate additional weighting, referred to as "variable monthly sampling weights,"3 which are used to address the biases that might be caused by the decision to base estimates on the data collected during a month. Consider a case in which the March mail response was 40 percent of the sample, the CATI response was 30 percent of the February panel, and the · .
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From page 29... ...
The question is whether variable monthly sampling weights can fix systematic variations in response rates.4 A second set of weighting factors that might benefit from an alternative approach is the noninterview weights. These are two-stage weights in which the first stage involves weighting all respondents to account for nonresponse and the second stage accounts for the different modes of response.
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From page 30... ...
In doing so, one essentially imputes complete records through weighting using the records corresponding to mail/CATI respondents more than the CAPI respondents, so it is necessary to reweight to remedy this. In the second stage, the mode bias factor downweights mail/CATI respondents by the ratio of the original weights across tracts to the weights taking nonresponse into account (see Table 5-51.
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From page 31... ...
Therefore, apply the weights of 1.10 = 110/100 and 1.20 = 120/100. TABLE 5-5 Example of Mode Bias Factor for a Pair of Census Tracts Weights Mode ( 1 )
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From page 32... ...
The idea of this weighting factor is to try to correct for undercoverage by controlling the ACS population counts to independent population estimates based on age, sex, race, and Hispanic origin at the county level. The key question is whether the population estimates provide better information than the ACS data at the county level and for demographic subgroups.
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From page 33... ...
First, as the ACS accumulates enough data over time, it might be possible, using some simple timeseries techniques, to model mail response (and other kinds of considerations) and thereby identify unusual differential mail response patterns to determine when certain weighting methods might have advantages.5 Second, the variable monthly sampling weights should be checked to see if there is an interaction of response mode with various characteristics, e.g., a vacancy rate.
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