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  • 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. (xsd:string)
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  • 2010 (xsd:gyear)
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  • 2010 (xsd:gyear)
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  • 9780470284308 ()
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  • Complex surveys : a guide to analysis using R (xsd:string)
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  • book (en)
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  • GESIS-BIB (xsd:string)
  • Hoboken: Wiley, 2010.- XV, 276 S., graph. Darst. (xsd:string)
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