Complex Surveys

Acknowledgments. Preface. Acronyms. 1 Basic Tools. 1.1 Goals of inference. 1.2 An introduction to the data. 1.3 Obtaining the software. 1.4 Using R. Exercises. 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. Exercises. 3 Cluster sampling. 3.1 Introduction. 3.2 Describing multistage designs to R. 3.3 Sampling by size. 3.4 Repeated measurements. Exercises. 4 Graphics. 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. 4.6 Maps. Exercises. 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. 7.1 Introduction. 7.2 Poststratification. 7.3 Raking. 7.4 Generalized raking, GREG estimation, and calibration. 7.5 Basu's elephants. 7.6 Selecting auxiliary variables for nonresponse. Exercises. 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. Exercises. 9 Missing data. 9.1 Item nonresponse. 9.2 Twophase estimation for missing data. 9.3 Imputation of missing data. Exercises. 10 Causal inference. 10.1 IPTW estimators. 10.2 Marginal Structural Models. Appendix A: Analytic details. A.1 Asymptotics. 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.3 Randomness. B.4 Methods and objects. B.5 Writing functions. Appendix C: Computational details. C.1 Linearization. C.2 Replicate weights. C.3 Scatterplot smoothers. C.4 Quantiles. 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. E.4 Linearization. References. Author Index. Topic Index.