Studies of Welfare Populations: Data Collection and Research Issues. Analyzing Weighted Survey Data

06/01/2002

Because estimates will be based on sample data, they will differ from figures that would have been obtained from complete enumeration of the universe. Results are subject to both sampling and nonsampling errors. Nonsampling errors include biases because of inaccurate reporting, measurement and processing errors, as well as errors because of nonresponse and incomplete sampling frames. Inaccurate or incomplete responses can occur due to misunderstanding or the misinterpretation of questions. Errors can also occur when responses are coded, edited, and entered into the database. Generally, the nonsampling errors cannot be measured readily but a number of quality assurance techniques are employed to reduce the frequency of such errors.

For the computation of sampling errors, most standard techniques used in statistical packages (e.g., SAS, SPSS, and others) assume that observations are independent and drawn using simple random sampling (SRS) selection methods and that all sampled cases participated in the survey. The estimates of variances for complex survey designs computed using standard statistical software packages that assume simple random sampling and a 100-percent response rate are biased. Once a sample departs from SRS and in the presence of nonresponse (especially in cases where nonresponse is rather high), new computational procedures are required in order to take into account the impact of survey design and nonresponse on statistical estimation. Two common approaches available for estimation of variances for complex survey data are Taylor linearization and replication. Using these procedures, factors such as stratification and the use of differential sampling rates to oversample a targeted subpopulation, and adjustments for nonresponse, can be reflected appropriately in estimates of sampling error. Wolter (1985) is a useful reference on the theory underlying variance estimation using replication and Taylor linearization methods. For information about relevant survey analysis software, visit http://www.amstat.org/sections/srms/links.html.

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