Practical Tools for Designing and Weighting Survey Samples

Preface Acknowledgements
1 An Overview of Sample Design and Weighting 1.1 Background and Terminology 1.2 Chapter Guide
Part I Designing Single-Stage Sample Surveys 2 Project 1: Design a Single-Stage Personnel Survey 2.1 Specifications for the Study 2.2 Questions Posed by the Design Team 2.3 Preliminary Analyses 2.4 Documentation 2.5 Next Steps
3 Sample Design and Sample Size for Single-Stage Surveys 3.1 Determining a Sample Size for a Single-Stage Design 3.1.1 Simple Random Sampling 3.1.2 Stratified Simple Random Sampling 3.2 Finding Sample Sizes When Sampling with Varying Probabilities 3.2.1 Probability Proportional to Size Sampling 3.2.2 Regression Estimates of Totals 3.3 Other Methods of Sampling 3.4 Estimating Population Parameters from a Sample 3.5 Special Topics 3.5.1 Rare Characteristics 3.5.2 Domain Estimates 3.6 More Discussion of Design Effects 3.7 Software for Sample Selection 3.7.1 R Packages 3.7.2 SAS PROC SURVEYSELECT Exercises
4 Power Calculations and Sample Size Determination 4.1 Terminology and One-Sample Tests 4.2 Power in a One-Sample Test 4.3 Two-Sample Tests 4.3.1 Differences in Means 4.3.2 Differences in Proportions 4.3.3 Special Case: Relative Risk 4.3.4 Special Case: Effect Sizes 4.4 R Power Functions 4.5 Power and Sample Size Calculations in SAS. Exercises
5 Mathematical Programming 5.1 Multicriteria Optimization 5.2 Microsoft Excel Solver 5.3 SAS PROC NLP 5.4 SAS PROC OPTMODEL 5.5 R Alabama Package
< 6 Outcome Rates and Effect on Sample Size 6.1 Disposition Codes 6.2 Definitions of Outcome Rates 6.3 Sample Units with Unknown AAPOR Classification 6.4 Weighted Versus Unweighted Rates 6.5 Accounting for Sample Losses in Determining Initial Sample Size 6.5.1 Sample Size Inflation Rates at Work 6.5.2 Replicates Exercises
7 The Personnel Survey Design Project: One Solution 7.1 Overview of the Project 7.2 Formulate the Optimization Problem 7.2.1 Objective Function 7.2.2 Decision Variables 7.2.3 Optimization Parameters 7.2.4 Specified Survey Constraints 7.3 One Solution 7.3.1 Power Analyses 7.3.2 Optimization Results 7.4 Additional Sensitivity Analysis 7.5 Conclusion
Part II Multistage Designs 8 Project 2: Designing an Area Sample
9 Designing Multistage Samples 9.1 Types of PSUs 9.2 Basic Variance Results 9.2.1 Two-Stage Sampling 9.2.2 Nonlinear Estimators in Two-Stage Sampling 9.2.3 More General Two-Stage Designs 9.2.4 Three-Stage Sampling 9.3 Cost Functions and Optimal Allocations for Multistage Sampling 9.3.1 Two-Stage Sampling When Numbers of Sample PSUs and Elements per PSU Are Adjustable 9.3.2 Three-Stage Sampling When Sample Sizes Are Adjustable 9.3.3 Two- and Three-Stage Sampling with a Fixed Set of PSUs 9.4 Estimating Measures of Homogeneity and Variance Components 9.4.1 Two-Stage Sampling 9.4.2 Three-Stage Sampling 9.4.3 Using Anticipated Variances The lme4 R package has been updated so that the syntax in the 1st edition no longer works. We will revise the examples in this section for the new version of the package. 9.5 Stratification of PSUs 9.6 Identifying Certainties Exercises
10 Area Sampling 10.1 Census Geographic Units 10.2 Census Data and American Community Survey Data 10.3 Units at Different Stages of Sampling 10.3.1 Primary Sampling Units 10.3.2 Secondary Sampling Units 10.3.3 Ultimate Sampling Units 10.4 Examples of Area Probability Samples 10.4.1 Current Population Survey 10.4.2 National Survey on Drug Use and Health 10.4.3 Panel Arbeitsmarkt und Soziale Sicherung 10.5 Composite MOS for Areas 10.5.1 Designing the Sample from Scratch 10.5.2 Using the Composite MOS with an Existing PSU Sample 10.6 Effects of Population Change: The New Construction Issue 10.7 Special Address Lists 10.7.1 Allocations in ABS using Mathematical Programming Mathematical programming allows efficient allocations to be made to domains (e.g., age groups) using information on housing units that can be purchased from commercial list makers. Discussion and examples will be added to illustr