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An Application of Balanced Repeated Replication (Brr) Variance Estimation To Program Evaluation

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  • Edward S. Cavin

    (Center for Naval Analyses)

  • James C. Ohls

    (Mathematica Policy Research)

Abstract

Program evaluations often are based on data drawn from samples of observation units that are not simple random samples. In particular, samples often may be clustered to maximize the number of observations that can be obtained, given project cost constraints. However, clustering gives rise to well-known problems in estimating the variance of sample estimates. The most common procedure is to apply a scalar "design effect" multiplier more or less judgmentally to inflate sample variances. An alternative procedure, as reported in this article, is to construct balanced repeated replication (BRR) estimates of sample variances. BRR estimates of variance are constructed from orthogonally-weighted subsamples of the data, and can provide better estimates of variances from complex samples than other methods.

Suggested Citation

  • Edward S. Cavin & James C. Ohls, 1990. "An Application of Balanced Repeated Replication (Brr) Variance Estimation To Program Evaluation," Evaluation Review, , vol. 14(2), pages 206-213, April.
    • Handle: RePEc:sae:evarev:v:14:y:1990:i:2:p:206-213
    DOI: 10.1177/0193841X9001400207
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