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Complex Survey Data Analysis with SAS (Paperback)
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Complex Survey Data Analysis with SAS (Paperback)
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Complex Survey Data Analysis with SAS (R) is an invaluable resource
for applied researchers analyzing data generated from a sample
design involving any combination of stratification, clustering,
unequal weights, or finite population correction factors. After
clearly explaining how the presence of these features can
invalidate the assumptions underlying most traditional statistical
techniques, this book equips readers with the knowledge to
confidently account for them during the estimation and inference
process by employing the SURVEY family of SAS/STAT (R) procedures.
The book offers comprehensive coverage of the most essential
topics, including: Drawing random samples Descriptive statistics
for continuous and categorical variables Fitting and interpreting
linear and logistic regression models Survival analysis Domain
estimation Replication variance estimation methods Weight
adjustment and imputation methods for handling missing data The
easy-to-follow examples are drawn from real-world survey data sets
spanning multiple disciplines, all of which can be downloaded for
free along with syntax files from the author's website:
http://mason.gmu.edu/~tlewis18/. While other books may touch on
some of the same issues and nuances of complex survey data
analysis, none features SAS exclusively and as exhaustively.
Another unique aspect of this book is its abundance of handy
workarounds for certain techniques not yet supported as of SAS
Version 9.4, such as the ratio estimator for a total and the
bootstrap for variance estimation. Taylor H. Lewis is a PhD
graduate of the Joint Program in Survey Methodology at the
University of Maryland, College Park, and an adjunct professor in
the George Mason University Department of Statistics. An avid SAS
user for 15 years, he is a SAS Certified Advanced programmer and a
nationally recognized SAS educator who has produced dozens of
papers and workshops illustrating how to efficiently and
effectively conduct statistical analyses using SAS.
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