Exploring Complex Survey Data Analysis Using R

Stephanie Zimmer, PhD, is a senior survey statistician with 10 years experience in survey sampling and design, survey weighting and analysis, and data management. She is an expert statistical programmer in R, SAS, and SUDAAN. She earned her PhD in Statistics from Iowa State University and her BS in Statistics from NC State. After earning the RStudio Tidyverse Trainer certification, she co-taught two courses on tidy survey analysis in R. She is currently a Senior Research Statistician at RTI International.Rebecca J. Powell, PhD, is the Director of Data Management and Survey Programming at Fors Marsh. Her research interests focus on visual design of questionnaires and contact materials. Dr. Powell is a Certified RStudio Tidyverse Trainer and has taught courses and webinars on data management in R and tidy survey analysis in R at both AAPOR and MAPOR. She has a PhD in Survey Research and Methodology from the University of Nebraska-Lincoln and a BS and MS in Applied Statistics from Rochester Institute of Technology.Isabella Velásquez is a content strategist, data enthusiast, and author. Her experience spans data collection, grantmaking, strategy development, and survey instrument development. She earned her MSc in Analytics and BA in Economics and East Asian Languages and Civilizations from the University of Chicago. She currently works as a Sr. Product Marketing Manager at Posit Software, PBC.

"I see this book being invaluable to any researcher who encounters a “survey weights” column in their dataset and isn’t sure what to do with it: in part because the authors provide code for handling the weights properly but also because they stress the importance of learning how the data were collected and the impacts that sampling mechanism has on the downstream analysis. Beyond the survey statistics newbie, I also believe the book is useful to a researcher or student who has developed a methodological understanding of survey statistics and is now looking to put these topics into practice on complex survey data.[...] My favorite aspect of the book are the closing vignettes where the authors apply the concepts covered in the book to two open access datasets. In fact, the authors spend over 50 pages walking the reader through potential analyses of these data! Instead of skimming over the complexities, they devote space to helping the reader understand the layers of complexity often found in survey work and the nuances of these data, especially in terms of the estimands and survey weights."-Kelly S. McConville in The American Statistician, March 2026

• Part 1: Introduction 1. Introduction 2. Overview of surveys 3. Survey data documentation Part 2: Analysis 4. Getting started 5. Descriptive analyses 6. Statistical testing 7. Modeling Part 3: Reporting 8. Communication of results 9. Reproducible research Part 4: Real life data 10. Sample designs and replicate weights 11. Missing data 12. Successful survey analysis recommendations Part 5: Vignettes 13. National Crime Victimization Survey Vignette 14. AmericasBarometer Vignette Appendix A. Importing survey data into R B. ANES derived variable codebook C. RECS derived variable codebook D. Exercise solutions Bibliography Index