Poststratification and Conditional Variance Estimation

Abstract

Poststratification estimation is a technique used in sample surveys to improve efficiency of estimators. Survey weights are adjusted to force the estimated numbers of units in each of a set of estimation cells to be equal to known population totals. The resulting weights are then used in forming estimates of means or totals of variables collected in the survey. For example, in a household survey the estimation cells may be based on age/race/sex categories of individuals, and the known totals may come from the most recent population census. Although the variance of a poststratified estimator can be computed over all possible sample configurations, inferences made conditionally on the achieved sample configuration are desirable. Theory and a simulation study using data from the U.S. Current Population Survey are presented to study both the conditional bias and variance of the poststratified estimator of a total. The linearization, balanced repeated replication, and jackknife variance estimators are also examined to determine whether they appropriately estimate the conditional variance.

KEY WORDS:

• Asymptotic properties
• Balanced repeated replication
• Jackknife variance estimation
• Linearization variance estimation
• Superpopulation model