Research and Plans for Coverage Measurement in the 2010 Census Interim Assessment (2007) / Chapter Skim
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3 Assessment of the Census Bureau’s Current Research Program for Coverage Evaluation in 2010
3 Assessment of the Census Bureau’s Current Research Program for Coverage Evaluation in 2010
Pages 32-67
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From page 32... ...
We introduce this by comparing the plans for the 2010 and 2000 censuses and then describing the limitations of Accuracy and Coverage Evaluation (A.C.E.) in measuring census component coverage error.
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. One motivation for this initiative was the recognition by the Census Bureau that many census errors and inefficiencies in 2000 resulted from errors in the Master Address File and in the information on the physical location of addresses.
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Finally, the national search for and field verification of duplicate enumerations should reduce the number of duplicates in the census, which may facilitate the estimation of component errors in the census and may also simplify the application of the net coverage error models used in dual-systems estimation (DSE)
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in 2000 for measuring census component coverage error: • Inadequate information collected as part of the census and the PES allowed too many mistakes in the A.C.E. final determination of Census Day residence.
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Consequently, this use of demographic analysis to modify A.C.E. estimates was not directly applicable to nonblack Hispanic males in 2000.3 • The approach taken to estimate net census coverage error relied on balancing erroneous enumerations against omissions in cases in which there was insufficient information for matching E- and P-sample cases.
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From page 37... ...
Furthermore, as we argue below, estimation of net census error also remains important for assessment of census component coverage error, specifically census omissions. PLANS FOR COVERAGE EVALUATION IN THE 2006 CENSUS TEST The goals for the 2006 test census relevant to coverage evaluation were as follows (U.S.
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Data needed to estimate coverage error (both net coverage error and components of coverage error) for persons living in housing units will be assembled by census operation to support the linkage of census component coverage error with specific census operations.
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We also examined preliminary ideas of the Census Bureau regarding the design of the CCM postenumeration survey and the current application of E-StARS to coverage measurement; E-StARS is the Census Bureau research program examining possible applications of merged, unduplicated lists of administrative records. All of these research efforts support the objective in 2010 of measuring census component coverage errors.
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To provide high-quality estimates of census component coverage errors in 2010, the Census Bureau needs to make progress on two fronts. First, it must reduce the inflated estimate of erroneous enumerations.
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. This is an important first step toward developing a feedback loop linking the measurement of census component coverage error to deficiencies in specific components of census processes.
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. For the 2000 census, this would have reclassified more than 2 million census enumerations from erroneous to correct enumerations, as well as a like number from P-sample omissions to matches, thereby greatly reducing the estimated number of census component coverage errors.
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Cases with insufficient information should be treated as having unknown or uncertain enumeration or match status. The term "erroneous" should be reserved for incorrect enumerations.
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Estimating various timing and resource issues through a census test will be difficult, since census tests involve field work for only a few counties, and there is typically no field validation of cases outside the test census area. RESEARCH ON MODELS FOR NET COVERAGE ERROR Even with a primary goal of estimating census component coverage error, there are still two important reasons to continue research on net coverage error models.
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One possibility would be to make a confidentiality-protected version of this database available at the Census Research Data Centers. Logistic Regression for Estimating Net Coverage Error The Census Bureau is examining the use of logistic regression modeling to estimate net census error, replacing the use of poststrata and synthetic estimation.
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. In theory, small-area estimates under logistic regression could improve on those provided through synthetic estimation by using more predictors to predict the probabilities of match and correct enumeration status, and hence reducing correlation bias.
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(1998) fit two separate logistic regression models.
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Another ˆ competing estimator replaces the correct enumeration probability pcej in these two alternatives by an indicator function for those individuals in the domain that had correct enumeration status, reducing the modeling to only the logistic regression model of match status. The problem with these two alternatives is that they are too sensitive to sampling variation.
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We hope to provide more guidance on these issues in our final report. Over the past two years, the Census Bureau has examined the use of logistic regression models to estimate net census error, focusing attention to date on the performance of six sets of explanatory variables for both the P-sample matches and the Esample correct enumerations.
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survey weights. The results of the Census Bureau's cross-validation comparison of the five alternative logistic regression models to the 2000 A.C.E.
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To examine this, using the logistic regression model with only the main effects from the poststratification (model 3) , we formed 100 groups for the cross-validation.
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This raises the possibility that inclusion of the survey design variables as predictors may provide some benefits. Any predictors used in a logistic regression model must be available from census data to allow estimation of net census error nationally (at least in the form currently preferred by the Census Bureau)
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Of late, methods such as classification trees have been shown to have some applicability. One way to consider this research problem, broadened to encompass not only net coverage error modeling through use of logistic regression, but also census component coverage error measurement, is that these problems are essentially discriminant analysis problems.
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Revision II, these errors would be unlikely to balance. It is clear that a similar issue arises with the use of logistic regression models of both the match rate and the correct enumeration rate, but it is substantially more difficult to assess.
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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, 3-24
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PANEL COMMENTS ON THE RESEARCH ON LOGISTIC REGRESSION MODELING The immediate objective of the Census Bureau's research on logistic regression is to determine whether it is preferable to poststratification for estimation of net coverage error in 2010 for small domains. Part of this assessment must include whether the model can be relied on in a production environment.
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From page 57... ...
and apply it to the logistic regression models, attempting to identify unanticipated correlations between match rate or correct enumeration rate and the available predictors, using cross-validation to evaluate the resulting logistic regression models. With respect to model form, the Census Bureau has also carried out some preliminary work on a very different use of two logistic regression models to model census net coverage error (see Griffin, 2005b)
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From page 58... ...
Second, for ease of comparison, while there is likely to be no poststrata in 2010 due to the use of logistic regression modeling, the Census Bureau should consider release of estimates of net coverage error for the 2010 census for comparable aggregates to support the comparison of net coverage error from census to census. Recommendation 1: The Census Bureau should evaluate, for use in the 2010 census coverage measurement program, a broader range of models, most importantly logistic regression models, for net coverage error that includes variables in addition to those used to define the A.C.E.
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The first attempt compared interview rates and rates of erroneous enumerations and omissions in the two populations defined by the order of the interviews. This analysis was stratified by the various situations that result in a CFU interview, listed above.
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One could include the CFU interviews that occurred prior to the CCM interviews in the truncated census, in which case the net coverage error models could condition on whether a CFU interview was carried out prior to the CCM interview, which would remove any bias if the P-sample inclusion probabilities depended on the occurrence of the CFU interview (but not on its outcome -- for details, see Bell, 2005)
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. The Census Bureau believes that the best approach is to delay the CCM interviews until after all CFU interviews are completed (option 5)
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The Census Bureau did examine some alternative specifications for the design of the CCM PES, using simulation studies of the quality of the resulting net coverage error estimates and assessment of components of census coverage error, especially estimation of the number of omissions and erroneous enumerations at the national level and for 64 poststrata (see Fenstermaker, 2005)
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First, there is the primary objective put forward by the Census Bureau, which is the measurement of census component coverage errors at some unspecified level of geographic and demographic aggregation. Second, there remains the need to measure net coverage error at the level of the poststrata used in 2000 in order to facilitate comparison with the 2000 census.
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From page 64... ...
The panel strongly supports this research program, and we think that there is a real possibility that administrative records could and should be used in the 2010 census, either for coverage improvement, for nonresponse follow-up, or for coverage measurement. Potentially feasible uses in the 2010 census include • To improve or evaluate the quality of either the Master Address File or the address list of the postenumeration blocks.
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From page 65... ...
Administrative records could possibly be used to help identify situations in which field resolution is not needed, for example, by indicating which of a set of duplicates is at the proper residence. (Uses of E-StARS like this are being attempted in the 2006 census test.)
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for coverage improvement, nonresponse follow-up, or coverage measurement and comprehensively test those applications during the 2008 census dress rehearsal. If a process using administrative records improves on processes used in 2000, that process should be implemented in the 2010 census.
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However, given that the completeness of administrative systems and the capabilities of matching and processing administrative records has been growing, while cooperation with field operations has declined, these contrasting trends make it increasingly likely that administrative records can soon provide enumerations of quality at least as good as field follow-up for some housing units. Furthermore, unlike purely statistical adjustment methods, every census enumeration would correspond to a specific person for whom there is direct evidence of his or her residence and their characteristics.
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