Variance Estimation

  • Richard Valliant
  • Jill A. Dever
  • Frauke Kreuter
Part of the Statistics for Social and Behavioral Sciences book series (SSBS, volume 51)


In previous chapters we considered the variance of estimators in order to determine the sample size and allocation to the design strata. After the sample data are collected, estimates are made and their variances and standard errors (SEs) must be computed. An SE (square root of the estimated variance) is a basic measure of precision that can be used as a descriptive statistic, e.g., as part of a coefficient of variation (CV ), or for making inferences about population parameters via confidence intervals. Estimating SEs that faithfully reflect all sources of (or a significant portion of the) variability in a sample design and an estimator is our goal, but this can be complicated. This is especially true when several (random) weight adjustments described in Chaps 13 and 14 are used. For example, when an adjustment for nonresponse is applied and then weights are raked to population controls, both procedures contribute to the variance of an estimator in addition to the randomness due to selecting the initial sample itself.


Variance Estimator Simple Random Sample Linear Estimator Weight Adjustment Nonlinear Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Richard Valliant
    • 1
  • Jill A. Dever
    • 2
  • Frauke Kreuter
    • 3
  1. 1.University of MichiganAnn ArborUSA
  2. 2.RTI InternationalWashington, DCUSA
  3. 3.University of MarylandCollege ParkUSA

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