Applied Survey Data Analysis

Applied Survey Data Analysis

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Features

    • Demonstrates how design characteristics, such as stratification, clustering, and weighting, are easily incorporated into the statistical methods and software for survey estimation and inference
    • Presents many methods and models for survey data analysis, including the linear regression, generalized linear, Cox proportional hazards, and discrete time models
    • Explores developments in advanced statistical techniques, such as multilevel analysis of survey data
    • Supplies advice and recommendations based on the authors’ experiences as well as current thinking on best practices
    • Uses theory boxes to develop or explain a fundamental theoretical concept underlying statistical methods
    • Includes practical exercises that reinforce application of the methods
    • Offers software code, brief technical reports, links to example survey data sets, and more on the book’s website

    Summary

    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/

    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

    Author Bio(s)

    Editorial Reviews

    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

    Downloads / Updates

    Resource OS Platform Updated Description Instructions
    Cross Platform April 01, 2010 Code, datasets and useful links click on http://www.isr.umich.edu/src/smp/asda/

     
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