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9780470284308

Complex Surveys A Guide to Analysis Using R

by
  • ISBN13:

    9780470284308

  • ISBN10:

    0470284307

  • Edition: 1st
  • Format: Paperback
  • Copyright: 2010-03-01
  • Publisher: Wiley
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Supplemental Materials

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Summary

Survey analysis remains the bread-and-butter of sociological research. Highlighting three main areas of interest-calibration estimators, two-phase designs, and fitting of regression models to survey data-Complex Surveys is the first book to describe the use of R in survey analysis in order to meticulously demonstrate new and efficient analyses of survey research methods in the health and social sciences. Written for applied statisticians and sophisticated users of statistics in the health and social sciences, the text employs large data sets throughout to illustrate the need for, and utility of, the R software system.

Author Biography

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

Acknowledgmentsp. xi
Prefacep. xiii
Acronymsp. xv
Basic Toolsp. 1
Goals of inferencep. 1
Population or process?p. 1
Probability samplesp. 2
Sampling weightsp. 3
Design effectsp. 6
An introduction to the datap. 6
Real surveysp. 7
Populationsp. 8
Obtaining the softwarep. 9
Obtaining Rp. 10
Obtaining the survey packagep. 10
Using Rp. 10
Reading plain text datap. 10
Reading data from other packagesp. 12
Simple computationsp. 13
Exercisesp. 14
Simple and Stratified samplingp. 17
Analyzing simple random samplesp. 17
Confidence intervalsp. 19
Describing the sample to Rp. 20
Stratified samplingp. 21
Replicate weightsp. 23
Specifying replicate weights to Rp. 25
Creating replicate weights in Rp. 25
Other population summariesp. 28
Quantilesp. 28
Contingency tablesp. 30
Estimates in subpopulationsp. 32
Design of stratified samplesp. 34
Exercisesp. 36
Cluster samplingp. 39
Introductionp. 39
Why clusters: the NHANES II designp. 39
Single-stage and multistage designsp. 41
Describing multistage designs to Rp. 42
Strata with only one PSUp. 43
How good is the single-stage approximation?p. 44
Replicate weights for multistage samplesp. 46
Sampling by sizep. 46
Loss of information from sampling clustersp. 50
Repeated measurementsp. 51
Exercisesp. 54
Graphicsp. 57
Why is survey data different?p. 57
Plotting a tablep. 58
One continuous variablep. 62
Graphs based on the distribution functionp. 62
Graphs based on the densityp. 65
Two continuous variablesp. 67
Scatterplotsp. 67
Aggregation and smoothingp. 70
Scatterplot smoothersp. 71
Conditioning plotsp. 72
Mapsp. 73
Design and estimation issuesp. 73
Drawing maps in Rp. 76
Exercisesp. 80
Ratios and linear regressionp. 83
Ratio estimationp. 84
Estimating ratiosp. 84
Ratios for subpopulation estimatesp. 85
Ratio estimators of totalsp. 85
Linear regressionp. 90
The least-squares slope as an estimated population summaryp. 90
Regression estimation of population totalsp. 92
Confounding and other criteria for model choicep. 97
Linear models in the survey packagep. 98
Is weighting needed in regression models?p. 104
Exercisesp. 105
Categorical data regressionp. 109
Logistic regressionp. 110
Relative risk regressionp. 116
Ordinal regressionp. 117
Other cumulative link modelsp. 122
Loglinear modelsp. 123
Choosing modelsp. 124
Linear-association modelsp. 129
Exercisesp. 132
Post-stratification, raking and calibrationp. 135
Introductionp. 135
Post-stratificationp. 136
Rakingp. 139
Generalized raking, GREG estimation, and calibrationp. 141
Calibration in Rp. 143
Basu's elephantsp. 149
Selecting auxiliary variables for non-responsep. 152
Direct standardizationp. 154
Standard error estimationp. 154
Exercisesp. 154
Two-phase samplingp. 157
Multistage and multiphase samplingp. 157
Sampling for stratificationp. 158
The case-control designp. 159
*Simulations: efficiency of the design-based estimatorp. 161
Frequency matchingp. 164
Sampling from existing cohortsp. 164
Logistic regressionp. 165
Two-phase case-control designs in Rp. 167
Survival analysisp. 170
Case-cohort designs in Rp. 171
Using auxiliary information from phase onep. 174
Population calibration for regression modelsp. 175
Two-phase designsp. 178
Some history of the two-phase calibration estimatorp. 181
Exercisesp. 182
Missing datap. 185
Item non-responsep. 185
Two-phase estimation for missing datap. 186
Calibration for item non-responsep. 186
Models for response probabilityp. 189
Effect on precisionp. 190
*Doubly-robust estimatorsp. 192
Imputation of missing datap. 193
Describing multiple imputations to Rp. 195
Example: NHANES III imputationsp. 196
Exercisesp. 200
*Causal inferencep. 203
IPTW estimatorsp. 204
Randomized trials and calibrationp. 204
Estimated weights for IPTWp. 207
Double robustnessp. 211
Marginal Structural Modelsp. 211
Analytic Detailsp. 217
Asymptoticsp. 217
Embedding in an infinite sequencep. 217
Asymptotic unbiasednessp. 218
Asymptotic normality and consistencyp. 220
Variances by linearizationp. 221
Subpopulation inferencep. 221
Tests in contingency tablesp. 223
Multiple imputationp. 224
Calibration and influence functionsp. 225
Calibration in randomized trials and ANCOVAp. 226
Basic Rp. 231
Reading datap. 231
Plain text datap. 231
Data manipulationp. 232
Mergingp. 232
Factorsp. 233
Randomnessp. 233
Methods and objectsp. 234
*Writing functionsp. 235
Repetitionp. 236
Stringsp. 238
Computational detailsp. 239
Linearizationp. 239
Generalized linear models and expected informationp. 240
Replicate weightsp. 240
Choice of estimatorsp. 240
Hadamard matricesp. 241
Scatterplot smoothersp. 242
Quantilesp. 242
Bug reports and feature requestsp. 244
Database-backed design objectsp. 245
Large datap. 245
Setting up database interfacesp. 247
ODBCp. 247
DBIp. 248
Extending the packagep. 249
A case study: negative binomial regressionp. 249
Using a Poisson modelp. 250
Replicate weightsp. 251
Linearizationp. 253
Referencesp. 257
Author Indexp. 269
Topic Indexp. 271
Table of Contents provided by Ingram. All Rights Reserved.

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