Replication Procedures

A class of variance estimation methods uses replication. These methods repeatedly estimate a statistic using a slightly different sample. Each sample is called a "replicate." These methods estimate the variance of a target statistic as a function of the empirical variation among estimates from the replicates.

If you are interested in the details of a specific statistical model, rather than the variance estimation method, you can see the procedure directly. Currently AM offers replication variance estimation for the following procedures:

In many data sets, replicates are embodied in a set of replicate weights. These weights adjust the effective sample to correspond to the appropriate replicates. Variance estimates may be obtained by repeatedly reestimating the statistic, each time using a different replicate weight.

There are three general replication procedures that are in wide use:

Jackknife variance estimation typically proceeds by dropping one primary sampling unit for each replicate. In complex designs, the sampling weights associated with the dropped units are then redistributed in a fashion that makes sense given the design. For example, in a stratified design, the weights would then be redistributed within the strata.

Balanced repeated replication divides the set of primary sampling units into an exhaustive set of half-sample replicates. Again, the exact procedures depend on the sample design.

Bootstrap replication resamples from the sample with replacement mimicking the original sample design. 
Click on the Overview tab to read an overview of this statistical analysis.Click on the Details tab to learn more about the details of this analysis.Select the References tab to retrieve the references for this analysis.Select the How To tab to learn how to conduct this analysis.Select the In NAEP tab to see how this procedure applies in NAEP.