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Complex Surveys: A Guide to Analysis Using R
Abstract
A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presen…
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“To incorporate the raking weights in our analyses, we used the svyglm function from the survey R package (80,81).”
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Metadata
- Authors
- Lumley, T.
- Corresponding author
- Lumley, Thomas
- ORCID
- Lumley, Thomas
- Venue
- Wiley eBooks
- Publisher
- Wiley
- Published
- 2010-02-15
- OpenAlex
- W4254617165
- Cited by
- 647
- References
- 0
- Citations by year
- 2026: 96 · 2025: 67 · 2024: 52 · 2023: 50 · 2022: 57 · 2021: 58 · 2020: 40 · 2019: 24 · 2018: 33 · 2017: 23 · 2016: 23 · 2015: 32 · 2014: 23 · 2013: 18 · 2012: 40
- FWCI
- 6.98
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Wiley eBooks (publishedVersion)
- ResearchSpace (University of Auckland) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- survey sampling, stratified sampling, cluster sampling, post-stratification, double sampling, missing data, causal inference, probability weighting, survey weighting, regression models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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