Applied Survey Data Analysis / Edition 1

by Steven G. Heeringa, Brady T. West, Patricia A. Berglund

Overview

Taking a practical approach that draws on the authors’ extensive teaching, consulting, and research experiences, Applied Survey Data Analysis provides an intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods.

After introducing a step-by-step process for approaching a survey analysis problem, the book presents the fundamental features of complex sample designs and shows how to integrate design characteristics into the statistical methods and software for survey estimation and inference. The authors then focus on the methods and models used in analyzing continuous, categorical, and count-dependent variables; event history; and missing data problems. Some of the techniques discussed include univariate descriptive and simple bivariate analyses, the linear regression model, generalized linear regression modeling methods, the Cox proportional hazards model, discrete time models, and the multiple imputation analysis method. The final chapter covers new developments in survey applications of advanced statistical techniques, including model-based analysis approaches.

Designed for readers working in a wide array of disciplines who use survey data in their work, this book also provides a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. A guide to the applied statistical analysis and interpretation of survey data, it contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s website: http://www.isr.umich.edu/src/smp/asda/

Product Details

ISBN-13:	9781420080667
Publisher:	Taylor & Francis
Publication date:	04/07/2010
Series:	Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences Series , #8
Edition description:	Older Edition
Pages:	487
Product dimensions:	6.30(w) x 9.30(h) x 1.20(d)

About the Author

Steve G. Heeringa is a research scientist in the Survey Methodology Program, the director of the Statistical and Research Design Group in the Survey Research Center, and the director of the Summer Institute in Survey Research Techniques at the University of Michigan’s Institute for Social Research.

Brady T. West is a doctoral student and research assistant in the Survey Research Center at the University of Michigan’s Institute for Social Research. He is also a statistical consultant in the Center for Statistical Consultation and Research.

Patricia A. Berglund is a senior research associate in the Youth and Social Indicators Program and Survey Methodology Program in the Survey Research Center at the University of Michigan’s Institute for Social Research.

Table of Contents

Applied Survey Data Analysis: Overview
Introduction A Brief History of Applied Survey Data Analysis Example Data Sets and Exercises

Getting to Know the Complex Sample Design
Introduction Classification of Sample Designs Target Populations and Survey Populations Simple Random Sampling: A Simple Model for Design-Based Inference Complex Sample Design Effects Complex Samples: Clustering and Stratification Weighting in Analysis of Survey Data Multistage Area Probability Sample Designs Special Types of Sampling Plans Encountered in Surveys

Foundations and Techniques for Design-Based Estimation and Inference
Introduction Finite Populations and Superpopulation Models Confidence Intervals for Population Parameters Weighted Estimation of Population Parameters Probability Distributions and Design-Based Inference Variance Estimation Hypothesis Testing in Survey Data Analysis Total Survey Error and Its Impact on Survey Estimation and Inference

Preparation for Complex Sample Survey Data Analysis
Introduction Analysis Weights: Review by the Data User Understanding and Checking the Sampling Error Calculation Model Addressing Item Missing Data in Analysis Variables Preparing to Analyze Data for Sample Subpopulations A Final Checklist for Data Users

Descriptive Analysis for Continuous Variables
Introduction Special Considerations in Descriptive Analysis of Complex Sample Survey Data Simple Statistics for Univariate Continuous Distributions Bivariate Relationships between Two Continuous Variables Descriptive Statistics for Subpopulations Linear Functions of Descriptive Estimates and Differences of Means Exercises

Categorical Data Analysis
Introduction A Framework for Analysis of Categorical Survey Data Univariate Analysis of Categorical Data Bivariate Analysis of Categorical Data Analysis of Multivariate Categorical Data Exercises

Linear Regression Models
Introduction The Linear Regression Model Four Steps in Linear Regression Analysis Some Practical Considerations and Tools Application: Modeling Diastolic Blood Pressure with the NHANES Data Exercises

Logistic Regression and Generalized Linear Models (GLMs) for Binary Survey Variables
Introduction GLMs for Binary Survey Responses Building the Logistic Regression Model: Stage 1, Model Specification Building the Logistic Regression Model: Stage 2, Estimation of Model Parameters and Standard Errors Building the Logistic Regression Model: Stage 3, Evaluation of the Fitted Model Building the Logistic Regression Model: Stage 4, Interpretation and Inference Analysis Application Comparing the Logistic, Probit, and Complementary Log-Log GLMs for Binary Dependent Variables Exercises

GLMs for Multinomial, Ordinal, and Count Variables
Introduction Analyzing Survey Data Using Multinomial Logit Regression Models Logistic Regression Models for Ordinal Survey Data Regression Models for Count Outcomes Exercises

Survival Analysis of Event History Survey Data
Introduction Basic Theory of Survival Analysis
(Nonparametric) Kaplan–Meier Estimation of the Survivor Function Cox Proportional Hazards Model Discrete Time Survival Models Exercises

Multiple Imputation: Methods and Applications for Survey Analysts
Introduction Important Missing Data Concepts An Introduction to Imputation and the Multiple Imputation Method Models for Multiply Imputing Missing Data Creating the Imputations Estimation and Inference for Multiply Imputed Data Applications to Survey Data Exercises

Advanced Topics in the Analysis of Survey Data
Introduction Bayesian Analysis of Complex Sample Survey Data Generalized Linear Mixed Models (GLMMs) in Survey Data Analysis Fitting Structural Equation Models to Complex Sample Survey Data Small Area Estimation and Complex Sample Survey Data Nonparametric Methods for Complex Sample Survey Data

References

Appendix: Software Overview

What People are Saying About This

From the Publisher

the authors do an admirable job of striking a balance between statistical theory and practical advice and analysis. The authors provide excellent coverage of each aspect of the survey analysis process … This book is an excellent general resource and if the reader is left wanting on a topic the authors never fail to provide an ample set of citations and references to a wide variety of notable texts on the topic in question. … an excellent and helpful addition to the desk of any analyst, researcher, or student with a general background in statistics who is dealing with the special challenges and demands of complex survey data.
—Gregory Holyk, Journal of Official Statistics, Vol. 27, 2011

Overall, the book is clearly written and easy to follow, and well equipped with real data examples and a book website. The program codes used in the example are also available, mostly written in Stata. I like the presentations with real survey examples and, in particular, the unified four-step approach to the regression analysis in different models. Anyone working on survey data analysis would find the book very helpful and instructive. The book website seems to be a good complement, with additional resources on this book.
—Jae-Kwang Kim, The American Statistician, November 2011

The book is well-written by authors who have over 60 years of combined teaching and consultation experience in survey methodology and research techniques. It is excellent for reference, with 12 structured chapters coherently organised, providing intermediate-level statistical overview of techniques used in analysing complex survey data. … It provides analysts with a framework of how to plan and conduct analysis of survey data, familiarise with terminologies used and understand common complex sample design features of clustering, stratification and weighting. … it is an excellent reference book for Stata users and the accompanying website provides useful resources and updated information. I feel that the book seamlessly links theory with practical applications of the statistical methods and helps the reader to develop an understanding of the framework of thinking required to effectively analyse complex survey data sets. …
—E.C. Abraham, AQMeNtion Newsletter, April 2011

… there is a wealth of instruction here. The writing style is expansive, keeping mathematics in check, and the material is well organized clearly into appropriate sections. I think that the book would serve any budding survey practitioner well: armed with the knowledge and practical skills covered herein, plus some real-life experience of course, one could reasonably claim to be well qualified in the subject.
—International Statistical Review (2010), 78, 3

Editorial Reviews

"Anyone analyzing survey data, even once, should have a copy of this book. The book has something for everyone. It is a solid, yet accessible introduction to analyzing data from complex sample surveys (i.e., those with stratification and clustering), a statistical text of the highest caliber, and a reference for experienced analysts and statisticians. The authors are masterful instructors on the topic, and leaders in the field of survey methodology at the University of Michigan's world-renowned Institute for Social Research and Survey Research Center. Their profound understanding of the topic, and talent for describing it shines through vividly in the text. One of my favorite parts remains section 1.2 "A Brief History of Applied Survey Data Analysis", which is split into "Key Theoretical Developments" and "Key Software Developments". The historical context provided in those sections helps motivate the technical material that follows. My other favorite parts of this book are the presentations of analysis code and output from various programs, and their "Theory Boxes", which tie specific analysis steps and code to the statistical theory behind them. Among the numerous updates to this edition, I think readers will find the new content on model diagnostics and testing goodness-of-fit (GOF) to be extremely helpful, as this is an area of complex sample survey analysis that can be difficult to translate from standard regression analysis. Throughout, the authors make it a point to describe analyses in discrete steps that can help direct even the most complex analyses."
--Matt Jans, Senior Associate/Scientist, Abt Associates

"This is an excellent book to use for a graduate level applied statistics course teaching public health students how to analyze complex survey data. Each chapter is clearly written with a nice balance of theoretical background and practical guidance on survey data analytical issues as illustrated by many relevant real-data examples.

From the Publisher