Applied Statistics and the SAS Pgmg Lang. by R. Cody, J. Smith

By R. Cody, J. Smith

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1 1 2 2 1 1 1 2 2 1 2 2 1 1 1 2 2 2 1 1 2 2 2 1 1 2 2 1 1 2 1 1 2 The fundamental results illustrated here for SRS are that, for a sample of size n, unbiased estimates of the population value and the standard error of the sample estimate can be computed. For samples of reasonably large size, a confidence interval for the population parameter of interest can be derived. As our discussion transitions to more complex samples and more complex statistics we will build on this basic framework for constructing confidence © 2010 by Taylor and Francis Group, LLC Getting to Know the Complex Sample Design 23 intervals for population parameters, adapting each step to the features of the sample design and the analysis procedure.

Therefore, survey data producers must develop statistical models of the conditional probability that a sample element will be an observed case. In general terms, the nonresponse adjustment factor, wnr, in the analysis weight is the reciprocal of the estimated conditional probability that the sample case responds. The objective in applying nonresponse factors in survey weights is to attenuate bias due to differential nonresponse across sample elements.

For an SRS sample design, the fpc factor arises from the algebraic derivation of the expected sampling variance of a survey statistic over all possible WOR samples of size n that could be selected from the population of N elements (see Cochran, 1977). 0. As a result, the fpc can be safely ignored in the estimation of the standard error of the sample estimate. Since complex samples may also employ sampling without replacement at one or more stages of selection, in theory, variance estimation for these designs should also include finite population corrections.

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Applied Statistics and the SAS Pgmg Lang. by R. Cody, J. Smith
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