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Fitting H. F. Smith's Empirical Law to Cluster Variances for Use in Designing Multi-Stage Sample Surveys
Charles H. Proctor
Journal of the American Statistical Association
Vol. 80, No. 390 (Jun., 1985), pp. 294-300
Stable URL: http://www.jstor.org/stable/2287885
Page Count: 7
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A number of methods are examined for using data from a multistage sample survey to estimate H. Fairfield Smith's heterogeneity coefficient, b. Smith's empirical law holds that cluster variances are proportional to the -b power of cluster size. The most reasonable method for estimating b seems to be generalized least squares applied to the linear model obtained by taking logs and then adding a lack-of-fit variance. The methods allow for simple measurement errors and systematic fixed effects, all in a finite population context. There is, however, a supposition of nearly equal or balanced sizes of the nested units. The b value so estimated can be used to derive optimum elementary cluster size and optimum subsampling rates for the multi-stage sample design. These applications are illustrated by numerical examples.
Journal of the American Statistical Association © 1985 American Statistical Association