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Complex Survey Data Analysis with SAS

Taylor H. Lewis
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A01=Taylor H. Lewis
advanced survey data analysis methods
Author_Taylor H. Lewis
Auxiliary Variables
categorical data analysis
Category=JMB
Category=PBT
cluster sampling
Complex Survey
Complex Survey Data
Data Set
Discrete Time Hazards Model
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
imputation
Imputation Model
logistic regression
missing data techniques
Model Statement
Parameter DF Estimate Standard Error
Person Period Data Set
PPS Sampling
PROC LIFETEST
Proc Mi
Proc Sort Data
PROC SURVEYFREQ
PROC SURVEYLOGISTIC
PROC SURVEYMEANS
PROC SURVEYREG
PROC SURVEYSELECT
Proc Surveyselect Data
Regression Model
Replicate Weights
replication variance estimation
Std Error
stratified sampling
Stratified Simple Random Sampling
survey methodology
survival analysis
Taylor Series Linearization
Var Statement
variance estimation
weight adjustment
weighting

Product details

  • ISBN 9781032242002
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 13 Dec 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

Complex Survey Data Analysis with SAS® 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® 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.
About Taylor H. Lewis
Taylor H. Lewis

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