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By Rossini M.

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In a few cases, the choice may depend on the type of statistic to be estimated or the sample design used, as in the paired selection design. 44 The formula-based Taylor series approximation (linearization) is perhaps the most widely used method of variance estimation for complex surveys because it is found in most available software. It may be preferable to the replication-based methods (BRR and JRR) for practical reasons, but, as discussed in Chapter 4, it is not applicable for the median or other percentiles and nonparametric statistics.

If the amount of item nonresponse is not trivial, then ignoring missing values would set too many weights to zero, and the original weighting scheme would be destroyed. This can lead to bias, and it will no longer be possible to refer accurately to the target population. One method of handling this problem is to inflate the weights of the observations without missing values to compensate for the ignored observations. When performing this type of adjustment, it generally is assumed that there is no systematic pattern among the subjects with missing values, but this assumption may not be valid.

Ut of the parameter U can be estimated by the following simple variance estimator (Kalton, 1983, p. 51): v(" u) = (ui − u")2 /t(t − 1) (4:1) This estimator can be applied to any sample statistic obtained from independent replicates of any sample design. In applying this variance estimator, 10 replicates are recommended by Deming (1960), and a minimum of 4 by others (Sudman, 1976) for descriptive statistics. An approximate estimate of the standard error can be calculated by dividing the range in the replicate estimates by the number of replicates when the number of replicates is between 3 and 13 (Kish, 1965, p.

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