Survey Data Analysis Made Easy With SAS

Population-based, representative surveys often incorporate complex methods in data collection, such as oversampling, weighting, stratification or clustering. If survey procedures are not used in data analyses, results will provide incorrect estimates and may overstate the results of significance testing. SAS procedures, such as PROC SURVEYFREQ and PROC SURVEYMEANS, make it easy to adjust for the complex sample design and weighting of representative surveys to obtain the correct percentages, confidence intervals, means, odds ratios and other statistics from complex survey data. In this introductory presentation, attendees will learn why it is necessary to use survey procedures when analyzing stratified or cluster sample surveys, what key survey design features are incorporated in the SAS code, and how to generate estimates using SAS survey procedures. This presentation will provide sample survey procedure code, and explain how to interpret the output from each survey procedure. Using examples from two publicly available surveys with different design elements (the National Health and Nutrition Examination Survey and the California Health Interview Survey), this presentation will demonstrate the following SAS survey procedures: PROC SURVEYFREQ, PROC SURVEYMEANS, PROC SURVEYLOGISTIC and PROC SURVEYRE
PRESENTER:
Melanie Dove, University of California, Davis
SESSION ID: 1086
TOPIC: Analytics: Sampling, Survey Research
INDUSTRY: Non-Industry Specific
AUDIENCE: Epidemiologist, Researcher, Statistician/Biostatistician
SKILL LEVEL: Intermediate
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