Research Methods and Statistics Arena

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• Price: $79.95 $71.96
• Hardback: 487 pages
• Also available in e-Book
• Published: April 2010
• ISBN: 978-1-4200806-6-7
• Publisher: Chapman & Hall

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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 Biography

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.