This course focuses on designing business and household surveys and analyzing data collected under complex survey designs. The course addresses the SAS procedures POWER, SURVEYSELECT, SURVEYMEANS, SURVEYFREQ, SURVEYREG, SURVEYLOGISTIC, and SURVEYIMPUTE. In addition, the graphing procedures GPLOT, SGPLOT, and SGPANEL are also covered.

Learn how to

• calculate sample sizes using the POWER procedure
• select complex samples using the SURVEYSELECT procedure
• estimate descriptive statistics using the SURVEYMEANS procedure
• estimate frequencies and percentages using the SURVEYFREQ procedure
• fit general linear models using the SURVEYREG procedure
• fit logistic regression models using the SURVEYLOGISTIC procedure
• handle missing value adjustment using SURVEYIMPUTE procedure
• implement balanced repeated replication, Taylor series and jackknife variance estimation
• perform domain analysis
• assess fitted linear/logistic regression models.

Who should attend

Survey statisticians, biostatisticians, epidemiologists, data analysts, and/or social scientists who design, and analyze data from, complex probability surveys

Formats available	Standard Duration
Classroom:	3.0 days
Live Web Classroom:	6 half-day session(s)	System Requirements

Before attending this course, you should

• be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS® Programming 1: Essentials course.
• have a basic understanding of macro variables
• have completed a course in statistics that covers linear regression and logistic regression. You can gain this experience by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression and Statistics 2: ANOVA and Regression courses.

This course addresses SAS/STAT software.

Survey Design

• simple random sampling
• stratified random sampling
• probability proportional to size sampling
• Chromy's minimal replacement sampling
• two-stage cluster sampling

Estimating Descriptive Statistics

• expansion (Horvitz-Thompson) estimation
• regression estimation
• ratio estimation

Analytical Uses of Survey Data

• linear regression and ANOVA
• contingency table analysis
• binary logistic regression
• domain analysis
• model assessment