• In what ways did the procedures differ from those used in the 1990 or earlier censuses? In particular, what effects did new additions to the census process have on the level of census error?

  • Were the procedures completed in a timely fashion?

  • Did any evidence of systematic problems arise during their implementation?

  • What parts of the procedure, if any, should be changed in order to improve the 2010 and later censuses?

Assessment of External Validity Measures

Demographic analysis, the Accuracy and Coverage Evaluation (A.C.E.) Program, and possibly the Census 2000 Supplementary Survey are the primary external measures for comparison with 2000 census estimates to gauge the overall level of error. Accordingly, the quality of these sources of information must be assessed prior to their use. Particular attention must be paid to the types of error intrinsic to these measures, to their underpinning assumptions and their validity, and to the possible interpretations of discrepancies between these measures and census counts. Since these external validity measures are the result of a complex set of operations and procedures—like the census itself—the effectiveness of those procedures should be subjected to the same scrutiny as the census process, as outlined in the preceding section.

Assessment of Types of Errors

Using external validation, a crucial task is to assess the amount of net undercount and gross coverage error for various demographic groups at various levels of geographic aggregation. An important question is whether any patterns of net undercount are affected when the census results examined are used as levels (counts), as shares (proportions), or as changes in counts or shares. It is also important to assess the error in estimates on the basis of the characteristics information collected on the census long form and how that error varies with level of geographic aggregation. One technique for this latter analysis is external validation from administrative records and other sources; another is a detailed component error analysis, attempting to sort out errors due to such sources as proxy response, imputation for item nonresponse, and sampling error.

Geographic Patterning of Error and Systematic Bias

The census data need to be carefully examined to identify patterns or clusters of either net undercount or net overcount, both to inform data users and to consider remedies for future censuses. Such patterns, if they correspond

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