Complex Surveys A Guide to Analysis Using R

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ISBN: 9780470284308 | 0470284307
Cover: Paperback
Copyright: 3/1/2010

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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.

Acknowledgments	p. xi
Preface	p. xiii
Acronyms	p. xv
Basic Tools	p. 1
Goals of inference	p. 1
Population or process?	p. 1
Probability samples	p. 2
Sampling weights	p. 3
Design effects	p. 6
An introduction to the data	p. 6
Real surveys	p. 7
Populations	p. 8
Obtaining the software	p. 9
Obtaining R	p. 10
Obtaining the survey package	p. 10
Using R	p. 10
Reading plain text data	p. 10
Reading data from other packages	p. 12
Simple computations	p. 13
Exercises	p. 14
Simple and Stratified sampling	p. 17
Analyzing simple random samples	p. 17
Confidence intervals	p. 19
Describing the sample to R	p. 20
Stratified sampling	p. 21
Replicate weights	p. 23
Specifying replicate weights to R	p. 25
Creating replicate weights in R	p. 25
Other population summaries	p. 28
Quantiles	p. 28
Contingency tables	p. 30
Estimates in subpopulations	p. 32
Design of stratified samples	p. 34
Exercises	p. 36
Cluster sampling	p. 39
Introduction	p. 39
Why clusters: the NHANES II design	p. 39
Single-stage and multistage designs	p. 41
Describing multistage designs to R	p. 42
Strata with only one PSU	p. 43
How good is the single-stage approximation?	p. 44
Replicate weights for multistage samples	p. 46
Sampling by size	p. 46
Loss of information from sampling clusters	p. 50
Repeated measurements	p. 51
Exercises	p. 54
Graphics	p. 57
Why is survey data different?	p. 57
Plotting a table	p. 58
One continuous variable	p. 62
Graphs based on the distribution function	p. 62
Graphs based on the density	p. 65
Two continuous variables	p. 67
Scatterplots	p. 67
Aggregation and smoothing	p. 70
Scatterplot smoothers	p. 71
Conditioning plots	p. 72
Maps	p. 73
Design and estimation issues	p. 73
Drawing maps in R	p. 76
Exercises	p. 80
Ratios and linear regression	p. 83
Ratio estimation	p. 84
Estimating ratios	p. 84
Ratios for subpopulation estimates	p. 85
Ratio estimators of totals	p. 85
Linear regression	p. 90
The least-squares slope as an estimated population summary	p. 90
Regression estimation of population totals	p. 92
Confounding and other criteria for model choice	p. 97
Linear models in the survey package	p. 98
Is weighting needed in regression models?	p. 104
Exercises	p. 105
Categorical data regression	p. 109
Logistic regression	p. 110
Relative risk regression	p. 116
Ordinal regression	p. 117
Other cumulative link models	p. 122
Loglinear models	p. 123
Choosing models	p. 124
Linear-association models	p. 129
Exercises	p. 132
Post-stratification, raking and calibration	p. 135
Introduction	p. 135
Post-stratification	p. 136
Raking	p. 139
Generalized raking, GREG estimation, and calibration	p. 141
Calibration in R	p. 143
Basu's elephants	p. 149
Selecting auxiliary variables for non-response	p. 152
Direct standardization	p. 154
Standard error estimation	p. 154
Exercises	p. 154
Two-phase sampling	p. 157
Multistage and multiphase sampling	p. 157
Sampling for stratification	p. 158
The case-control design	p. 159
*Simulations: efficiency of the design-based estimator	p. 161
Frequency matching	p. 164
Sampling from existing cohorts	p. 164
Logistic regression	p. 165
Two-phase case-control designs in R	p. 167
Survival analysis	p. 170
Case-cohort designs in R	p. 171
Using auxiliary information from phase one	p. 174
Population calibration for regression models	p. 175
Two-phase designs	p. 178
Some history of the two-phase calibration estimator	p. 181
Exercises	p. 182
Missing data	p. 185
Item non-response	p. 185
Two-phase estimation for missing data	p. 186
Calibration for item non-response	p. 186
Models for response probability	p. 189
Effect on precision	p. 190
*Doubly-robust estimators	p. 192
Imputation of missing data	p. 193
Describing multiple imputations to R	p. 195
Example: NHANES III imputations	p. 196
Exercises	p. 200
*Causal inference	p. 203
IPTW estimators	p. 204
Randomized trials and calibration	p. 204
Estimated weights for IPTW	p. 207
Double robustness	p. 211
Marginal Structural Models	p. 211
Analytic Details	p. 217
Asymptotics	p. 217
Embedding in an infinite sequence	p. 217
Asymptotic unbiasedness	p. 218
Asymptotic normality and consistency	p. 220
Variances by linearization	p. 221
Subpopulation inference	p. 221
Tests in contingency tables	p. 223
Multiple imputation	p. 224
Calibration and influence functions	p. 225
Calibration in randomized trials and ANCOVA	p. 226
Basic R	p. 231
Reading data	p. 231
Plain text data	p. 231
Data manipulation	p. 232
Merging	p. 232
Factors	p. 233
Randomness	p. 233
Methods and objects	p. 234
*Writing functions	p. 235
Repetition	p. 236
Strings	p. 238
Computational details	p. 239
Linearization	p. 239
Generalized linear models and expected information	p. 240
Replicate weights	p. 240
Choice of estimators	p. 240
Hadamard matrices	p. 241
Scatterplot smoothers	p. 242
Quantiles	p. 242
Bug reports and feature requests	p. 244
Database-backed design objects	p. 245
Large data	p. 245
Setting up database interfaces	p. 247
ODBC	p. 247
DBI	p. 248
Extending the package	p. 249
A case study: negative binomial regression	p. 249
Using a Poisson model	p. 250
Replicate weights	p. 251
Linearization	p. 253
References	p. 257
Author Index	p. 269
Topic Index	p. 271
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Complex Surveys A Guide to Analysis Using R

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