By Dr. Eun Sul Lee, Dr. Ronald N. Forthofer
This booklet examines how you can learn advanced surveys, and specializes in the issues of weights and layout results. This re-creation accommodates contemporary perform of interpreting advanced survey information, introduces the hot analytic strategy for specific information research (logistic regression), stories new software program and gives an advent to the model-based research that may be valuable studying well-designed, fairly small-scale social surveys.
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Extra resources for Analyzing Complex Survey Data (Quantitative Applications in the Social Sciences)
Consulting and technical assistance are available on the SAS Web site. Stata is a general-purpose statistical program package that includes a survey analysis module. It is available from Stata Corporation, College Station, Texas. Its survey analysis component supports a variety of analytical procedures including svymean, svytotal, svyprop (proportion), svyratio (ratio estimation), svytab (two-way tables), svyregress (regression), svylogit (logistic regression), svymlogit (multinomial logistic regression), svypois (Poisson regression), svyprobit (probit models), and others.
When stratification is used and the stratum identification is available, we need to make sure that at least two PSUs are available in each stratum. Otherwise, it is not possible to estimate the variance. If only one cluster is selected from each stratum, it is necessary to pair the strata to form pseudostrata. Pairing the strata requires good understanding of the sample design. An example of a particular strategy for pairing, using the GSS, was presented in Chapter 3. In the absence of any useful information from the data document, a random pairing may be acceptable.
The BRR method solves this problem by repeating the process of forming half-sample replicates, selecting different units from different strata. The pseudo-replicated half samples then contain some common units, and this introduces dependence between replicates, which complicates the estimation. One solution, which leads to unbiased estimates of variance for linear statistics, is to balance the formation of pseudo-replicates by using an orthogonal matrix (Plackett & Burman, 1946). The full balancing requires that the size of the matrix be a multiple of four and the number of replicates be greater than or equal to the number of strata.