weighting adjustments was calculated separately for households by type (single-family unit, apartment, other) within each individual block cluster. Mover status was not a factor for reweighting.

For Census Day, what could have been a relatively large noninterview adjustment for outmover households in a block cluster was spread over all interviewed Census Day households in the cluster for each of the three housing types. Consequently, adjustments to the weights for interviewed households were quite low, which had the benefit of minimizing the increase in the variance of A.C.E. estimates due to differences among weights: 52 percent of the weights were not adjusted at all because all occupied households in the adjustment cell were interviewed; for another 45 percent of households, the weighting adjustment was between 1.0 and 1.2 (Cantwell et al., 2001:Table 2; see also “Variance Estimates,” below).

MISSING AND UNRESOLVED DATA

Another important aspect of A.C.E. data quality is the extent of missing and unresolved data in the P-sample and the E-sample and the effectiveness of imputation procedures to supply values for missing and unresolved variables. Understanding the role of imputation necessitates understanding the designation of the E-sample and the treatment of certain cases in the matching.

As noted above, the E-sample excluded whole person imputations in the census, defined as people with only one short-form characteristic (which could be name). Matching was performed on the P-sample and E-sample, using only reported information. During the course of matching, it was determined that some cases lacked enough reported data for matching and follow-up when a more stringent criterion was applied than that used to exclude whole person imputations from the E-sample. Cases in the P-sample and E-sample lacking name and at least two other short-form characteristics could not be matched. Such cases were retained in both the E- and the P-samples; in the E-sample they were coded as erroneous enumerations and in the P-sample they were not yet assigned a final match status.

After all matching and follow-up had been completed, the next step was item imputation. Missing characteristics were imputed separately for each item in the P-sample records (including those records that lacked enough reported data for matching). Imputations for missing characteristics in the E-sample records (including those records that lacked name and at least two other short-form characteristics) were obtained from those on the census data file (see Appendix A). Then, match probabilities and Census Day residence probabilities were imputed for unresolved P-sample cases, including those that were set aside in the matching, and correct enumeration probabilities were imputed for unresolved E-sample cases. E-sample cases set aside in the matching were assigned a correct enumeration probability of zero.



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