Complex Surveys A Guide to Analysis Using R

ISBN-10: 0470284307
ISBN-13: 9780470284308
Edition: 2010
Authors: Thomas S. Lumley
List price: $128.95
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Description: 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  More...

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Book details

List price: $128.95
Copyright year: 2010
Publisher: John Wiley & Sons, Incorporated
Publication date: 1/26/2010
Binding: Paperback
Pages: 296
Size: 6.25" wide x 9.50" long x 0.75" tall
Weight: 1.166
Language: English

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
Preface
Acronyms
Basic Tools
Goals of inference
Population or process?
Probability samples
Sampling weights
Design effects
An introduction to the data
Real surveys
Populations
Obtaining the software
Obtaining R
Obtaining the survey package
Using R
Reading plain text data
Reading data from other packages
Simple computations
Exercises
Simple and Stratified sampling
Analyzing simple random samples
Confidence intervals
Describing the sample to R
Stratified sampling
Replicate weights
Specifying replicate weights to R
Creating replicate weights in R
Other population summaries
Quantiles
Contingency tables
Estimates in subpopulations
Design of stratified samples
Exercises
Cluster sampling
Introduction
Why clusters: the NHANES II design
Single-stage and multistage designs
Describing multistage designs to R
Strata with only one PSU
How good is the single-stage approximation?
Replicate weights for multistage samples
Sampling by size
Loss of information from sampling clusters
Repeated measurements
Exercises
Graphics
Why is survey data different?
Plotting a table
One continuous variable
Graphs based on the distribution function
Graphs based on the density
Two continuous variables
Scatterplots
Aggregation and smoothing
Scatterplot smoothers
Conditioning plots
Maps
Design and estimation issues
Drawing maps in R
Exercises
Ratios and linear regression
Ratio estimation
Estimating ratios
Ratios for subpopulation estimates
Ratio estimators of totals
Linear regression
The least-squares slope as an estimated population summary
Regression estimation of population totals
Confounding and other criteria for model choice
Linear models in the survey package
Is weighting needed in regression models?
Exercises
Categorical data regression
Logistic regression
Relative risk regression
Ordinal regression
Other cumulative link models
Loglinear models
Choosing models
Linear-association models
Exercises
Post-stratification, raking and calibration
Introduction
Post-stratification
Raking
Generalized raking, GREG estimation, and calibration
Calibration in R
Basu's elephants
Selecting auxiliary variables for non-response
Direct standardization
Standard error estimation
Exercises
Two-phase sampling
Multistage and multiphase sampling
Sampling for stratification
The case-control design
*Simulations: efficiency of the design-based estimator
Frequency matching
Sampling from existing cohorts
Logistic regression
Two-phase case-control designs in R
Survival analysis
Case-cohort designs in R
Using auxiliary information from phase one
Population calibration for regression models
Two-phase designs
Some history of the two-phase calibration estimator
Exercises
Missing data
Item non-response
Two-phase estimation for missing data
Calibration for item non-response
Models for response probability
Effect on precision
*Doubly-robust estimators
Imputation of missing data
Describing multiple imputations to R
Example: NHANES III imputations
Exercises
*Causal inference
IPTW estimators
Randomized trials and calibration
Estimated weights for IPTW
Double robustness
Marginal Structural Models
Analytic Details
Asymptotics
Embedding in an infinite sequence
Asymptotic unbiasedness
Asymptotic normality and consistency
Variances by linearization
Subpopulation inference
Tests in contingency tables
Multiple imputation
Calibration and influence functions
Calibration in randomized trials and ANCOVA
Basic R
Reading data
Plain text data
Data manipulation
Merging
Factors
Randomness
Methods and objects
*Writing functions
Repetition
Strings
Computational details
Linearization
Generalized linear models and expected information
Replicate weights
Choice of estimators
Hadamard matrices
Scatterplot smoothers
Quantiles
Bug reports and feature requests
Database-backed design objects
Large data
Setting up database interfaces
ODBC
DBI
Extending the package
A case study: negative binomial regression
Using a Poisson model
Replicate weights
Linearization
References
Author Index
Topic Index

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