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Survey Sampling and Analysis Procedures
- SURVEYMEANS — Means, totals, proportions, quantiles, and ratios from complex multistage survey designs
- SURVEYFREQ — One-way to n-way frequency and crosstabulation tables from complex multistage survey designs
- SURVEYLOGISTIC — Models with binary, ordinal, or nominal dependent variables for data sampled from complex survey designs
- SURVEYPHREG — Regression analysis based on the Cox proportional hazards model for sample survey data
- SURVEYREG — Linear regression analysis for for data sampled from complex survey designs
- SURVEYSELECT — Selects simple random samples or selects samples according to a complex multistage survey design
Survey Sampling and Analysis
Researchers often use sample survey methodology to obtain information about a large population by selecting and measuring a sample from that population. Due to variability among items, researchers apply scientific probability-based designs to select the sample. This reduces the risk of a distorted view of the population and enables statistically valid inferences to be made from the sample. The survey analysis procedures in SAS/STAT software properly analyze complex survey data by taking into account the sample design. These procedures can be used for multistage or single-stage designs, with or without stratification, and with or without unequal weighting.
Below are highlights of the capabilities of the SAS/STAT procedures that perform survey sampling and analysis:
- Sample selection
- descriptive statistics and t-tests
- linear regression
- Cox proportional hazards model
- logistic regression
- frequency table analysis including estimates of population totals, population proportions, and their standard errors
- compute design effects
- variances estimation methods include:
- Taylor series (linearization)
- balanced repeated replication (BRR)
- jackknife
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