R Companion for Sampling Design and Analysis, Third Edition

The R Companion for Sampling: Design and Analysis, designed to be read alongside Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (SDA; 2022, CRC Press), shows how to use functions in base R and contributed packages to perform calculations for the examples in SDA. No prior experience with R is needed. Chapter 1 tells you how to obtain R and RStudio, introduces basic features of the R statistical software environment, and helps you get started with analyzing data. Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA, with code, output, and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors. R features and functions are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book, you will know how to use R to select and analyze almost any type of probability sample. All R code and data sets used in this book are available online to help you develop your skills analyzing survey data from social and public opinion research, public health, crime, education, business, agriculture, and ecology.

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 Simple Probability Samples Selecting a Simple Random Sample Computing Statistics from an SRS Additional Code for Exercises Summary, Tips, and Warnings 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 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 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 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 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 Nonresponse How R Functions Treat Missing Data Poststratification and Raking Imputation Summary, Tips, and Warnings 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 Categorical Data Analysis in Complex Surveys Contingency Tables and Odds Ratios Chi-Square Tests Loglinear Models Summary, Tips, and Warnings 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 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 A Data Set Descriptions Bibliography Index

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.