R Companion for Sampling Design and Analysis, Third Edition

1. Getting Started

Obtaining the Software

Installing R packages

R Basics

Reading Data into R

Saving Output

Integrating R Output into LATEX Documents

Missing Data

Summary, Tips, and Warnings

2. Simple Probability Samples

Selecting a Simple Random Sample

Computing Statistics from an SRS

Additional Code for Exercises

Summary, Tips, and Warnings

3. Stratified Sampling

Allocation Methods

Selecting a Stratified Random Sample

Computing Statistics from a Stratified Random Sample

Estimating Proportions from a Stratified Random Sample

Additional Code for Exercises

Summary, Tips, and Warnings

4. Ratio and Regression Estimation

Ratio Estimation

Regression Estimation

Domain Estimation

Poststratification

Ratio Estimation with Stratified Sampling

Model-Based Ratio and Regression Estimation

Summary, Tips, and Warnings

5. Cluster Sampling with Equal Probabilities

Estimates from One-Stage Cluster Samples

Estimates from Multi-Stage Cluster Samples

Model-Based Design and Analysis for Cluster Samples

Additional Code for Exercises

Summary, Tips, and Warnings

6. Sampling with Unequal Probabilities

Selecting a Sample with Unequal Probabilities

Sampling With Replacement

Sampling Without Replacement

Selecting a Two-stage Cluster Sample

Computing Estimates from an Unequal-Probability Sample

Estimates from With-Replacement Samples

Estimates from Without-Replacement Samples

Summary, Tips, and Warnings

7. Complex Surveys

Selecting a Stratified Two-Stage Sample

Estimating Quantiles

Computing Estimates from Stratified Multistage Samples

Univariate Plots from Complex Surveys

Scatterplots from Complex Surveys

Additional Code for Exercises

Summary, Tips, and Warnings

8. Nonresponse

How R Functions Treat Missing Data

Poststratification and Raking

Imputation

Summary, Tips, and Warnings

9. Variance Estimation in Complex Surveys

Replicate Samples and Random Groups

Constructing Replicate Weights

Balanced Repeated Replication

Jackknife

Bootstrap

Replicate Weights and Nonresponse Adjustments

Using Replicate Weights from a Survey Data File

Summary, Tips, and Warnings

10. Categorical Data Analysis in Complex Surveys

Contingency Tables and Odds Ratios

Chi-Square Tests

Loglinear Models

Summary, Tips, and Warnings

11. Regression with Complex Survey Data

Straight Line Regression in an SRS

Linear Regression for Complex Survey Data

Multiple Linear Regression

Using Regression to Compare Domain Means

Logistic Regression

Additional Resources and Code

Summary, Tips, and Warnings

12. Additional Topics for Survey Data Analysis

Two-Phase Sampling

Contents iii

Estimating the Size of a Population

Ratio Estimation of Population Size

Loglinear Models with Multiple Lists

Small Area Estimation

Summary

Biography

Yan Lu is Associate Professor of Statistics at the University of New Mexico. Her research interests include survey sampling, mixed models, nonparametric regression, and data mining. Recent publications develop new statistical methods for combining data from multiple surveys, selecting probability samples from massive data streams, and applying nonparametric regression to survey data.

Sharon L. Lohr, the author of Measuring Crime: Behind the Statistics, has published widely about survey sampling and statistical methods for education, public policy, law, and crime. She is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute, and has received the Gertrude M. Cox, Morris Hansen, and Deming Awards. Formerly Dean’s Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a statistical consultant and writer.