Analyzing Complex Sample Survey Data in the SAS Software
This two-part, one-day short course, scheduled for 9/29/23, will provide participants with an overview of essential concepts underlying the statistical analysis of survey data collected from complex probability samples using the SAS software. The first part, scheduled from 8am – Noon, will establish fundamental concepts, touching on complex probability sample designs, survey weights, and appropriate variance estimation procedures. The second part, scheduled from 1pm – 5pm, will present a series of examples of both descriptive and multivariable model analyses using the SAS software. There will be several breaks allowing for questions and answers, and SAS code will be provided to all participants. The working schedule for the short course appears below:
Part I: Conceptual Background - Friday Morning
8:00 - 10:00 Complex Sample Designs, Weighting, and Design Effects in Survey Estimation and Inference
10:00 - 10:15 Break / Questions
10:15 - 11:45 Variance Estimation in Complex Samples: Taylor Series Linearization and Repeated Replication Techniques
11:45 - 12:00 Wrap-up / Questions
10:00 - 10:15 Break / Questions
10:15 - 11:45 Variance Estimation in Complex Samples: Taylor Series Linearization and Repeated Replication Techniques
11:45 - 12:00 Wrap-up / Questions
Part II: Applications in SAS - Friday Afternoon
1:00 - 2:00 Using Procedures in SAS for Variance Estimation and Inference for Descriptive Statistics
2:00 - 2:30 Percentiles and Subpopulation Analysis
2:30 - 2:45 Break / Questions
2:45 - 3:45 Analysis of Categorical Data
3:45 - 4:45 Regression Modeling
4:45 - 5:00 Wrap-up / Questions / Evaluations
2:00 - 2:30 Percentiles and Subpopulation Analysis
2:30 - 2:45 Break / Questions
2:45 - 3:45 Analysis of Categorical Data
3:45 - 4:45 Regression Modeling
4:45 - 5:00 Wrap-up / Questions / Evaluations
|
About the Presenter
Brady T. West is a Research Associate Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus. He is currently a member of the Technical Advisory Committee for the U.S. Bureau of Labor Statistics, the Director of the Junior Fellows Program of the Joint Program in Survey Methodology, and the outgoing chair of the Education Committee on the National Council of the American Association for Public Opinion Research (or AAPOR). He earned his PhD from the Michigan Program in Survey Methodology, located at the University of Michigan-Ann Arbor, in 2011. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. A first author or co-author of more than 120 peer-reviewed publications in survey methodology, statistics, and public health, he is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition, Chapman Hall/CRC Press, 2014), and a co-author of a second book entitled Applied Survey Data Analysis (with Steven Heeringa and Pat Berglund), the second edition of which was published by Chapman Hall/CRC Press in June of 2017.
|
