The Survey Procedures


The SURVEYSELECT procedure provides methods for probability sample selection. The SURVEYMEANS, SURVEYFREQ, SURVEYREG, and SURVEYLOGISTIC procedures provide statistical analyses for sample survey data. The following sections contain brief descriptions of these procedures. See the chapters on these procedures for more detailed information.

PROC SURVEYSELECT

The SURVEYSELECT procedure provides a variety of methods for selecting probability-based random samples. The procedure can select a simple random sample or a sample according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection. With probability sampling, each unit in the survey population has a known, positive probability of selection. This property of probability sampling avoids selection bias and enables you to use statistical theory to make valid inferences from the sample to the survey population.

PROC SURVEYSELECT provides methods for both equal probability sampling and sampling with probability proportional to size (PPS). In PPS sampling, a unit s selection probability is proportional to its size measure. PPS sampling is often used in cluster sampling, where you select clusters (groups of sampling units) of varying size in the first stage of selection. Available PPS methods include without replacement, with replacement, systematic, and sequential with minimum replacement. The procedure can apply these methods for stratified and replicated sample designs.

PROC SURVEYMEANS

The SURVEYMEANS procedure produces estimates of population means and totals from sample survey data. You can use PROC SURVEYMEANS to compute the following statistics:

  • estimates of population means, with corresponding standard errors and t tests

  • estimates of population totals, with corresponding standard deviations and t tests

  • estimates of proportions for categorical variables , with standard errors and t tests

  • ratio estimates of population means and proportions, and their standard errors

  • confidence limits for population means, totals, and proportions

  • data summary information

It is common practice to compute statistics for subpopulations or domains, in addition to computing statistics for the entire study population. Formation of these subpopulations may be unrelated to the sample design, so the domain sample sizes may actually be random variables. Domain analysis takes into account this variability, using the entire sample when estimating the variance of domain estimates. This is also known as subgroup analysis, subpopulation analysis, or subdomain analysis. For more information on domain analysis, refer to Lohr (1999) and Cochran (1977).

You can use the SURVEYMEANS procedure to perform domain analysis to compute the following statistics:

  • domain (subpopulation) estimates of means, with corresponding standard errors and t tests

  • domain (subpopulation) estimates of totals, with corresponding standard deviations and t tests

  • proportion estimates within domains for categorical variables, with standard errors and t tests

  • confidence limits for domain statistics

PROC SURVEYFREQ

The SURVEYFREQ procedure produces one-way to n -way frequency and crosstabulation tables from sample survey data. These tables include estimates of population totals, population proportions (overall proportions, and also row and column proportions), and corresponding standard errors. Confidence limits, coefficients of variation, and design effects are also available. The procedure also provides a variety of options to customize your table display.

For one-way frequency tables, PROC SURVEYFREQ provides Rao-Scott chi-square goodness-of-fit tests, which are adjusted for the sample design. You can test a null hypothesis of equal proportions for a one-way frequency table, or you can input other null hypothesis proportions for the test. For two-way frequency tables, PROC SURVEYFREQ provides design-adjusted tests of independence, or no association, between the row and column variables. These tests include the Rao-Scott chi-square test, the Rao-Scott likelihood -ratio test, the Wald chi-square test, and the Wald loglinear chi-square test.

PROC SURVEYREG

The SURVEYREG procedure fits linear models for survey data and computes regression coefficients and their variance-covariance matrix. The procedure allows you to specify classification effects using the same syntax as in the GLM procedure. The procedure also provides hypothesis tests for the model effects, for any specified estimable linear functions of the model parameters, and for custom hypothesis tests for linear combinations of the regression parameters. The procedure also computes the confidence limits of the parameter estimates and their linear estimable functions.

PROC SURVEYLOGISTIC

The SURVEYLOGISTIC procedure investigates the relationship between discrete responses and a set of explanatory variables for survey data. The procedure fits linear logistic regression models for discrete response survey data by the method of maximum likelihood, incorporating the sample design into the analysis. The SURVEYLOGISTIC procedure enables you to use categorical classification variables (also known as CLASS variables) as explanatory variables in an explanatory model, using the familiar syntax for main effects and interactions employed in the GLM and LOGISTIC procedures.

The following link functions are available for regression in PROC SURVEYLOGISTIC: the cumulative logit function (CLOGIT), the generalized logit function (GLOGIT), the probit function (PROBIT), and the complementary log-log function (CLOGLOG). The procedure performs maximum likelihood estimation of the regression coefficients with either the Fisher-scoring algorithm or the Newton-Raphson algorithm. Variances of the regression parameters and the odds ratios are computed with a Taylor expansion approximation ; refer to Binder (1983) and Morel (1989).




SAS.STAT 9.1 Users Guide (Vol. 1)
SAS/STAT 9.1 Users Guide, Volumes 1-7
ISBN: 1590472438
EAN: 2147483647
Year: 2004
Pages: 156

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net