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| == Methods == === Weighting Class Adjustments === Commonly used for non-response bias adjustments. === Calibration === Commonly used for coverage bias adjustments. ---- |
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Post-stratified estimates are ratios of two linear estimates, and therefore are ''nonlinear'' estimates. === Linearization methods === Variances of post-stratified estimates are generally computed as the variance of the Taylor linearized approximation. Many statistical programming tools use a linear substitute method. === Replication methods === Replication estimates of variance include balanced repeated replication (BRR) and jackknife. === Degrees of freedom === |
Post-Stratification
Post-stratification is the adjustment of weights such that weighted estimates of characteristics match known population parameters. Because these characteristics were not available for sample design, the inverse probability of selection does not reflect them.
Contents
Methods
Weighting Class Adjustments
Commonly used for non-response bias adjustments.
Calibration
Commonly used for coverage bias adjustments.
Variance
Post-stratified estimates are ratios of two linear estimates, and therefore are nonlinear estimates.
Linearization methods
Variances of post-stratified estimates are generally computed as the variance of the Taylor linearized approximation.
Many statistical programming tools use a linear substitute method.
Replication methods
Replication estimates of variance include balanced repeated replication (BRR) and jackknife.
Degrees of freedom
The degrees of freedom for post-stratified variance is commonly approximated as the number of PSUs (n) minus the number of strata (H). This formally is a special case of the Satterthwaite approximation.