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5.  The sampling and weighting evaluation program

5.1  Sampling bias
5.2  Evaluation of weighting procedures
5.3  Sample estimate and population count consistency
5.4  Sampling variance

The sampling and weighting evaluation program was designed to determine the effect of sampling and weighting on the quality of census sample data. Four studies in all were carried out to help measure the quality of the census sample data and estimates, and to provide information for the planning of future censuses. These studies involved:

  • an examination of sampling bias

  • an evaluation of weighting procedures

  • an evaluation of sample estimate to population count consistency

  • determining the standard errors for various 20% sample characteristics.

Each of these studies is described briefly below, with their results being presented in Chapters 6, 7, 8 and 9.

5.1  Sampling bias

This study assessed differences between estimates based on initial weights and known population counts. These differences are of interest for two reasons: first, their possible usefulness in identifying biases in the census household sample selected in the field; and second, they may indicate a possible negative impact of non-response on census sample questions. Biases in short form characteristics are corrected through calibration during the weighting procedure. If long form characteristics are correlated with short form characteristics, their biases should also be reduced through calibration.

5.2  Evaluation of weighting procedures

The objective of this study was to evaluate the performance of the Pseudo-optimal Regression estimator. This was done by examining the level of agreement between sample estimates (based on the final weights) and population counts for all the WA-level constraints. Any inconsistencies were explained through assessment of the number and type of constraints discarded at the WA level and the reasons for their being discarded. In addition, the distribution of census weights was studied.

5.3  Sample estimate and population count consistency

This study examined the level of agreement between sample estimates (based on the final weights) and population counts for the basic characteristics used as constraints. This was done for various geographic areas.

5.4  Sampling variance

The standard error (the square root of the variance) of an estimate is a measure of its precision. Estimates of standard errors for estimators using simple weights of 5 and assuming simple random sampling are relatively quick to calculate. However, estimates of standard errors for census estimators taking into account the sample design and estimation techniques used are time consuming to calculate. Adjustment factors were calculated which represent the ratios of the estimates of the standard errors for census estimates to the simple estimates of the standard errors. An estimate of the standard error of a census estimate for any characteristic in any geographic area can then be obtained by multiplying the simple estimate of the standard error by the appropriate adjustment factor.

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