Variance Estimation

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


Estimating variances and standard errors (SEs) that faithfully reflect all sources of variability in a sample design and an estimator is the goal, but this can be complicated. This is especially true when several (random) weight adjustments are made like nonresponse adjustment and calibration. Several alternative methods of variance estimation that will be covered in this chapter—exact formulas, linearization, and replication variance estimators (jackknife, balanced repeated replication, and bootstrap). We summarize the methods along with some of their strengths and weaknesses, including how easily each can account for different sources of variability. The last two sections of this chapter discuss some specialized topics—combining PSUs or strata for variance estimation and ways of handling certainty PSUs when estimating variances.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Richard Valliant
    • 1
    • 2
  • Jill A. Dever
    • 3
  • Frauke Kreuter
    • 2
    • 4
  1. 1.University of MichiganAnn ArborUSA
  2. 2.University of MarylandCollege ParkUSA
  3. 3.RTI InternationalWashington, DCUSA
  4. 4.University of MannheimMannheimGermany

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