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.

Table of Contents:
Features and Examples of Complex Surveys Introduction Definitions and Terminology of Sample Surveys Overview of SAS/STAT Procedures Available to Analyze Survey Data Four Features of Complex Surveys Examples of Complex Surveys Summary Drawing Random Samples Using PROC SURVEYSELECTIntroduction Fundamental Sampling Techniques Statified Sampling Cluster Sampling Summary Analyzing Continuous Variables Using PROC SURVEYMEANS Introduction Totals Means Ratios Quantiles Summary Analyzing Categorical Variables Using PROC SURVEYFREQ Introduction Univariate Analyses Bivariate Analyses Multiway Tables Summary Fitting Linear Refression Models Using PROC SURVEYREG Introduction Linear Regression in a Simple Random Sampling Setting Linear Regression with Complex Survey Data Testing for a Reduced Model Computing Unit-Level Statistics Summary Fitting Logistic Regression Models Using PROC SURVERYLOGISTIC Introduction Logistic Regression in a Simple Random Sampling Setting Logistic Regression with Complex Survey Data Testing for a Reduced Model and Adequate Model Fit Computing Unit-Level Statistics Customizing Odds Ratios Extensions for Modeling Variables with More than Two Outcomes Summary Survival Analysis with Complex Survey Data Introduction foundations of Survival Analysis Survival Analysis with Complex Survey Data Summary Domain Estimation Introduction Definitions and an Example Data Set Risk in Subsetting a Complex Survey Data Set Domain Estimation using Domain-Specific Weights Domain Estimation for Alternative Statistics Significance Testing for Domain Mean Differences Degress of Freedom Adjustments Summary Replication Techniques for Variance Estimation Introduction More Details Regarding Taylor Series Linearization Balanced Repeated Replication Fay's Variant to BRR Jackknife Bootstrap Replication with Liner Models Replication as a Tool for Estimating Variances of Complex Point Estimates Degrees of Freedom Adjustments Summary Weight Adjustment MethodsIntroduction Definitions and Missing Data Assumptions Adjustment Cell Method Propensity Cell Method Poststratification Raking Summary Imputation Methods Introduction Definitions and a Brief Taxonomy of Imputation Techniques Multiple Imputation as a Way to Incorporate Missing Data Uncertainty Univariate Missingness Multivariate Missingness Inferences from Multiply Imputed Data Accounting for Features of the Complex Survey Data during the Imputation Modeling and Analysis Stages Summary References Index