E-raamat: Complex Survey Data Analysis with SAS [Taylor & Francis e-raamat]

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 SURVEYSELECT
Introduction
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 Methods
Introduction
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