Applied Survey Data Analysis

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.

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