1st Edition

R Companion for Sampling
Design and Analysis, Third Edition

  • Available for pre-order. Item will ship after November 25, 2021
ISBN 9781032135946
November 25, 2021 Forthcoming by Chapman and Hall/CRC
214 Pages 24 B/W Illustrations

USD $34.95

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Book Description

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.

Table of Contents

  1. Getting Started
  2. 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

  3. Simple Probability Samples
  4. Selecting a Simple Random Sample

    Computing Statistics from an SRS

    Additional Code for Exercises

    Summary, Tips, and Warnings

  5. Stratified Sampling
  6. 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

  7. Ratio and Regression Estimation
  8. Ratio Estimation

    Regression Estimation

    Domain Estimation


    Ratio Estimation with Stratified Sampling

    Model-Based Ratio and Regression Estimation

    Summary, Tips, and Warnings

  9. Cluster Sampling with Equal Probabilities
  10. 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

  11. Sampling with Unequal Probabilities
  12. 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

  13. Complex Surveys
  14. 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

  15. Nonresponse
  16. How R Functions Treat Missing Data

    Poststratification and Raking


    Summary, Tips, and Warnings

  17. Variance Estimation in Complex Surveys
  18. Replicate Samples and Random Groups

    Constructing Replicate Weights

    Balanced Repeated Replication



    Replicate Weights and Nonresponse Adjustments

    Using Replicate Weights from a Survey Data File

    Summary, Tips, and Warnings

  19. Categorical Data Analysis in Complex Surveys
  20. Contingency Tables and Odds Ratios

    Chi-Square Tests

    Loglinear Models

    Summary, Tips, and Warnings

  21. Regression with Complex Survey Data
  22. 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

  23. 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


A Data Set Descriptions



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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.