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Complex Surveys: A Guide to Analysis Using R
BY Thomas S. Lumley
MATHEMATICS & STATISTICS / SURVEY RESEARCH METHODS & SAMPLING /
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 presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems.
Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.
THOMAS LUMLEY, PHD, is Associate Professor of Biostatistics at the University of Washington. He has published numerous journal articles in his areas of research interest, which include regression modeling, clinical trials, statistical computing, and survey research. Dr. Lumley created the survey package that currently accompanies the R software package, and he is also coauthor of Biostatistics: A Methodology for the Health Sciences, Second Edition, published by Wiley.
TABLE OF CONTENTS:
1 Basic Tools.
1.1 Goals of inference.
1.2 An introduction to the data.
1.3 Obtaining the software.
1.4 Using R.
2 Simple and Stratified sampling.
2.1 Analysing simple random samples.
2.2 Stratified sampling.
2.3 Replicate weights.
2.4 Other population summaries.
2.5 Estimates in subpopulations.
2.6 Design of stratified samples.
3 Cluster sampling.
3.2 Describing multistage designs to R.
3.3 Sampling by size.
3.4 Repeated measurements.
4.1 Why is survey data different?
4.2 Plotting a table.
4.3 One continuous variable.
4.4 Two continuous variables.
4.5 Conditioning plots.
5 Ratios and linear regression.
5.1 Ratio estimation.
5.2 Linear regression.
5.3 Is weighting needed in regression models?
6 Categorical data regression 109.
6.1 Logistic regression 110.
6.2 Ordinal regression 117.
6.3 Loglinear models 123.
7 Poststratification, raking and calibration.
7.4 Generalized raking, GREG estimation, and calibration.
7.5 Basuí»s elephants.
7.6 Selecting auxiliary variables for nonresponse.
8 Twophase sampling.
8.1 Multistage and multiphase sampling.
8.2 Sampling for stratification.
8.3 The case-control design.
8.4 Sampling from existing cohorts.
8.5 Using auxiliary information from phase one.
9 Missing data.
9.1 Item nonresponse.
9.2 Twophase estimation for missing data.
9.3 Imputation of missing data.
10 Causal inference.
10.1 IPTW estimators.
10.2 Marginal Structural Models.
Appendix A: Analytic details.
A.2 Variances by linearization.
A.3 Tests in contingency tables.
A.4 Multiple imputation.
A.5 Calibration and influence functions.
A.6 Calibration in randomized trials and ANCOVA.
Appendix B: Basic R.
B.1 Reading data.
B.2 Data manipulation.
B.4 Methods and objects.
B.5 Writing functions.
Appendix C: Computational details.
C.2 Replicate weights.
C.3 Scatterplot smoothers.
C.5 Bug reports and feature requests.
Appendix D: Databasebacked design objects.
D.1 Large data.
D.2 Setting up database interfaces.
Appendix E: Extending the survey package.
E.1 A case study: negative binomial regression.
E.2 Using a Poisson model.
E.3 Replicate weights.