Complex Surveys : A Guide to Analysis Using R

by Thomas Lumley (Univ. of Washington, ; USA )

• ISBN13: 9780470580059
• ISBN10: 0470580054
• Format: eBook
• Copyright: 2010-03-01
• Publisher: Wiley

Summary

A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.

Table of Contents

Acknowledgments

Preface

Acronyms

Basic Tools

Goals of inference

An introduction to the data

Obtaining the software

Using R

Exercises

Simple and Stratified sampling

Analysing simple random samples

Stratified sampling

Replicate weights

Other population summaries

Estimates in subpopulations

Design of stratified samples

Exercises

Cluster sampling

Introduction

Describing multistage designs to R

Sampling by size

Repeated measurements

Exercises

Graphics

Why is survey data different?

Plotting a table

One continuous variable

Two continuous variables

Conditioning plots

Maps

Exercises

Ratios and linear regression

Ratio estimation

Linear regression

Is weighting needed in regression models?

Categorical data regression 109

Logistic regression 110

Ordinal regression 117

Loglinear models 123

Poststratification, raking and calibration

Introduction

Poststratification

Raking

Generalized raking, GREG estimation, and calibration

Basu's elephants

Selecting auxiliary variables for nonresponse

Exercises

Twophase sampling

Multistage and multiphase sampling

Sampling for stratification

The case-control design

Sampling from existing cohorts

Using auxiliary information from phase one

Exercises

Missing data

Item nonresponse

Twophase estimation for missing data

Imputation of missing data

Exercises

Causal inference

IPTW estimators

Marginal Structural Models

Analytic details

Asymptotics

Variances by linearization

Tests in contingency tables

Multiple imputation

Calibration and influence functions

Calibration in randomized trials and ANCOVA

Basic R

Reading data

Data manipulation

Randomness

Methods and objects

Writing functions

Computational details

Linearization

Replicate weights

Scatterplot smoothers

Quantiles

Bug reports and feature requests

Databasebacked design objects

Large data

Setting up database interfaces

Extending the survey package

A case study: negative binomial regression

Using a Poisson model

Replicate weights

Linearization

References

Author Index

Topic Index

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