Analyzing complex survey data, Issue 71 by Eun Sul Lee, Ron N. Forthofer, Ronald J. Lorimor

By Eun Sul Lee, Ron N. Forthofer, Ronald J. Lorimor

Social scientists are not easy extra analytic stories of social survey information with the intention to research various rising matters. Answering this desire, interpreting advanced Survey info bargains a good technique of reading and studying complicated surveys -- and the way to beat difficulties that regularly come up. It contains discussions at the offerings excited about variance estimates, basic random sampling with out alternative, stratified random sampling and two-stage cluster sampling, and descriptions the various desktop courses which are at the moment to be had.

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For the purpose of illustration, the expansion weight for these data at the household level could be calculated by dividing the number of households in the United States by 1,473. The expansion weight within the sampled household is the number of adults in the household. The product of these two weights gives the expansion weight for sample individuals. For an analysis of the GSS data, we need to focus only on the weight within the household, since each household has the same probability of selection.

This weight reflects the probability of selection of an individual in the sample while preserving the sample size. We further modified this weight by a poststratification adjustment in an attempt to make the sample composition the same as the population composition. This would improve the precision of estimates and could possibly reduce nonresponse and sample selection bias to the extent that it is related to the demographic composition. S. population by age, race, and sex. Column 1 is the 1984 population distribution by race, sex, and age, based on the Census Bureau's estimates.

It is not immediately evident how the formulas should be modified to adjust for other sampling designs. To better understand the need for adjustment to the variance formulas, we first examine the variance formula for a sample mean from the SRSWOR design. The familiar variance formula for a sample mean, (selecting a sample of n elements from a population of N elements by SRSWR where the population mean is ) in elementary statistics textbooks is This formula needs to be modified for the SRSWOR design, since the selection of an element is no longer independent of the selection of another element.

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