Usage Note 22861: Procedures for the design and analysis of survey data based on complex sampling designs
SAS/STAT^{®} software includes several procedures for survey data:
 PROC SURVEYSELECT provides a variety of methods for selecting probabilitybased random samples as well as samples according to a complex multistage design that includes stratification, clustering, and unequal probabilities of selection.
 PROC SURVEYMEANS computes estimates of survey population totals and means, estimates of their variances, confidence limits, and other descriptive statistics.
 PROC SURVEYFREQ produces oneway to nway frequency and crosstabulation tables for survey data. These tables include estimates of totals and proportions (overall, row percentages, column percentages) and the corresponding standard errors.
 PROC SURVEYREG performs regression analysis for sample survey data, fitting linear models and computing regression coefficients and the covariance matrix.
 PROC SURVEYLOGISTIC performs logistic regression on data that arises from a survey sampling scheme. Variances of the regression parameters and odds ratios are computed using a Taylor expansion approximation.
 Beginning in SAS 9.2 TS2M3, PROC SURVEYPHREG performs regression analysis based on the Cox proportional hazards model for sample survey data. Cox’s semiparametric model is widely used in the analysis of survival data to estimate hazard rates when explanatory variables are available.
 Beginning in SAS 9.4 TS1M3, PROC SURVEYIMPUTE imputes missing values of an item in a sample survey by replacing them with observed values from the same item. Imputation methods include single and multiple hotdeck imputation and fully efficient fractional imputation. Donor selection techniques include simple random selection with or without replacement, probability proportional to weights selection, and approximate Bayesian bootstrap selection.
The SURVEYMEANS, SURVEYFREQ, SURVEYREG, SURVEYLOGISTIC, and SURVEYPHREG procedures can incorporate survey sample designs including designs with stratification, clustering, and unequal weighting.
See this note which discusses and illustrates a method for fitting a Poisson model to count data that are sampled from a finite population using a complex survey design.
Operating System and Release Information
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For software releases that are not yet generally available, the Fixed
Release is the software release in which the problem is planned to be
fixed.
Type:  Usage Note 
Priority:  low 
Topic:  SAS Reference ==> Procedures ==> SURVEYREG SAS Reference ==> Procedures ==> SURVEYLOGISTIC SAS Reference ==> Procedures ==> SURVEYMEANS SAS Reference ==> Procedures ==> SURVEYFREQ Analytics ==> Survey Sampling and Analysis Analytics ==> Regression Analytics ==> Missing Value Imputation Analytics ==> Descriptive Statistics Analytics ==> Categorical Data Analysis SAS Reference ==> Procedures ==> SURVEYSELECT SAS Reference ==> Procedures ==> SURVEYPHREG SAS Reference ==> Procedures ==> SURVEYIMPUTE Analytics ==> Survival Analysis

Date Modified:  20190212 10:44:59 
Date Created:  20021216 10:56:36 