Applied Survey Data Analysis
Authors: 
Steve G. Heeringa, Brady T. West, and Patricia A. Berglund 
Publisher: 
Chapman & Hall/CRC 
Copyright: 
2010 
ISBN13: 
9781420080667 
Pages: 
462; hardcover 
Price: 
$69.50 



Comment from the Stata technical group
Applied Survey Data Analysis is an intermediatelevel, exampledriven
treatment of current methods for complex survey data. It will appeal to
researchers of all disciplines who work with survey data and have basic
knowledge of applied statistical methodology for standard (nonsurvey) data.
The authors begin with some history and by discussing some widely used survey
datasets, such as the National Health and Nutrition Examination Survey
(NHANES). They then follow with the basic concepts of survey data: sampling
plans, weights, clustering, prestratification and poststratification, design
effects, and multistage samples. Discussion then turns to the types of
variance estimators: Taylor linearization, jackknife, bootstrap, and
balanced and repeated replication.
The middle sections of the text provide
indepth coverage of the types of analyses that can be performed with survey
data, including means and proportions, correlations, tables, linear
regression, regression with limited dependent variables (including logit and
Poisson), and survival analysis (including Cox regression). Two final
chapters are devoted to advanced topics, such as multiple imputation,
Bayesian analysis, and multilevel models. The appendix provides overviews
of popular statistical software, including Stata.
Table of contents
Preface
1. Applied Survey Data Analysis: Overview
1.1 Introduction
1.2 A Brief History of Applied Survey Data Analysis
1.2.1 Key Theoretical Developments
1.2.2 Key Software Developments
1.3 Example Data Sets and Exercises
1.3.1 The National Comorbidity Survey Replication (NCSR)
1.3.2 The Health and Retirement Study (HRS)—2006
1.3.3 The National Health and Nutrition Examination Survey (NHANES)—2005, 2006
1.3.4 Steps in Applied Survey Data Analysis
1.3.4.1 Step 1: Definition of the Problem and Statement of the Objectives
1.3.4.2 Step 2: Understanding the Sample Design
1.3.4.3 Step 3: Understanding Design Variables, Underlying Constructs, and Missing Data
1.3.4.4 Step 4: Analyzing the Data
1.3.4.5 Step 5: Interpreting and Evaluating the Results of the Analysis
1.3.4.6 Step 6: Reporting of Estimates and Inferences from the Survey Data
2. Getting to Know the Complex Sample Design
2.1 Introduction
2.1.1 Technical Documentation and Supplemental Literature Review
2.2 Classification of Sample Designs
2.2.1 Sampling Plans
2.2.2 Inference from Survey Data
2.3 Target Populations and Survey Populations
2.4 Simple Random Sampling: A Simple Model for DesignBased Inference
2.4.1 Relevance of SRS to Complex Sample Survey Data Analysis
2.4.2 SRS Fundamentals: A Framework for DesignBased Inference
2.4.3 An Example of DesignBased Inference under SRS
2.5 Complex Sample Design Effects
2.5.1 Design Effect Ratio
2.5.2 Generalized Design Effects and Effective Sample Sizes
2.6 Complex Samples: Clustering and Stratification
2.6.1 Clustered Sampling Plans
2.6.2 Stratification
2.6.3 Joint Effects of Sample Stratification and Clustering
2.7 Weighting in Analysis of Survey Data
2.7.1 Introduction to Weighted Analysis of Survey Data
2.7.2 Weighting for Probabilities of Selection
2.7.3 Nonresponse Adjustment Weights
2.7.3.1 Weighting Class Approach
2.7.3.2 Propensity Cell Adjustment Approach
2.7.4 Poststratification Weight Factors
2.7.5 Design Effects Due to Weighted Analysis
2.8 Multistage Area Probability Sample Designs
2.8.1 Primary Stage Sampling
2.8.2 Secondary Stage Sampling
2.8.3 Third and Fourth Stage Sampling of Housing Units and Eligible Respondents
2.9 Special Types of Sampling Plans Encountered in Surveys
3. Foundations and Techniques for DesignBased Estimation and Inference
3.1 Introduction
3.2 Finite Populations and Superpopulation Models
3.3 Confidence Intervals for Population Parameters
3.4 Weighted Estimation of Population Parameters
3.5 Probability Distributions and DesignBased Inference
3.5.1 Sampling Distributions of Survey Estimates
3.5.2 Degrees of Freedom for t under Complex Sample Designs
3.6 Variance Estimation
3.6.1 Simplifying Assumptions Employed in Complex Sample Variance Estimation
3.6.2 The Taylor Series Linearization Method
3.6.2.1 TSL Step 1
3.6.2.2 TSL Step 2
3.6.2.3 TSL Step 3
3.6.2.4 TSL Step 4
3.6.2.5 TSL Step 5
3.6.3 Replication Methods for Variance Estimation
3.6.3.1 Jackknife Repeated Replication
3.6.3.2 Balanced Repeated Replication
3.6.3.3 The Bootstrap
3.6.4 An Example Comparing the Results from TSL, JRR, and BRR Methods
3.7 Hypothesis Testing in Survey Data Analysis
3.8 Total Survey Error and Its Impact on Survey Estimation and Inference
3.8.1 Variable Errors
3.8.2 Biases in Survey Data
4. Preparation for Complex Sample Survey Data Analysis
4.1 Introduction
4.2 Analysis Weights: Review by the Data User
4.2.1 Identification of the Correct Weight Variables for the Analysis
4.2.2 Determining the Distribution and Scaling of the Weight Variables
4.2.3 Weighting Applications: Sensitivity of Survey Estimates to the Weights
4.3 Understanding and Checking the Sampling Error Calculation Model
4.3.1 Stratum and Cluster Codes in Complex Sample Survey Data Sets
4.3.2 Building the NCSR Sampling Error Calculation Model
4.3.3 Combining Strata, Randomly Grouping PSUs, and Collapsing Strata
4.3.4 Checking the Sampling Error Calculation Model for the Survey Data Set
4.4 Addressing Item Missing Data in Analysis Variables
4.4.1 Potential Bias Due to Ignoring Missing Data
4.4.2 Exploring Rates and Patterns of Missing Data Prior to Analysis
4.5 Preparing to Analyze Data for Sample Subpopulations
4.5.1 Subpopulation Distributions across Sample Design Units
4.5.2 The Unconditional Approach for Subclass Analysis
4.5.3 Preparation for Subclass Analyses
4.6 A Final Checklist for Data Users
5. Descriptive Analysis for Continuous Variables
5.1 Introduction
5.2 Special Considerations in Descriptive Analysis of Complex Sample Survey Data
5.2.1 Weighted Estimation
5.2.2 Design Effects for Descriptive Statistics
5.2.3 Matching the Method to the Variable Type
5.3 Simple Statistics for Univariate Continuous Distributions
5.3.1 Graphical Tools for Descriptive Analysis of Survey Data
5.3.2 Estimation of Population Totals
5.3.3 Means of Continuous, Binary, or Interval Scale Data
5.3.4 Standard Deviations of Continuous Variables
5.3.5 Estimation of Percentiles and Medians of Population Distributions
5.4 Bivariate Relationships between Two Continuous Variables
5.4.1 X–Y Scatterplots
5.4.2 Product Moment Correlation Statistic (r)
5.4.3 Ratios of Two Continuous Variables
5.5 Descriptive Statistics of Subpopulations
5.6 Linear Functions of Descriptive Estimates and Differences of Means
5.6.1 Differences of Means for Two Subpopulations
5.6.2 Comparing Means over Time
5.7 Exercises
6. Categorical Data Analysis
6.1 Introduction
6.2 A Framework for Analysis of Categorical Survey Data
6.2.1 Incorporating the Complex Design and PseudoMaximum Likelihood
6.2.2 Proportions and Percentages
6.2.3 CrossTabulations, Contingency Tables, and Weighted Frequencies
6.3 Univariate Analysis of Categorical Data
6.3.1 Estimation of Proportions for Binary Variables
6.3.2 Estimation of Category Proportions for Multinomial Variables
6.3.3 Testing Hypotheses Concerning a Vector of Population Proportions
6.3.4 Graphical Display for a Single Categorical Variable
6.4 Bivariate Analysis of Categorical Data
6.4.1 Response and Factor Variables
6.4.2 Estimation of Total, Row, and Column Proportions for TwoWay Tables
6.4.3 Estimating and Testing Differences in Subpopulation Proportions
6.4.4 ChiSquare Tests of Independence of Rows and Columns
6.4.5 Odds Ratios and Relative Risks
6.4.6 Simple Logistic Regression to Estimate the Odds Ratio
6.4.7 Bivariate Graphical Analysis
6.5 Analysis of Multivariate Categorical Data
6.5.1 The Cochran–Mantel–Haenszel Test
6.5.2 LogLinear Models for Contingency Tables
6.6 Exercises
7. Linear Regression Models
7.1 Introduction
7.2 The Linear Regression Model
7.2.1 The Standard Linear Regression Model
7.2.2 Survey Treatment of the Regression Model
7.3 Four Steps in Linear Regression Analysis
7.3.1 Step 1: Specifying and Refining the Model
7.3.2 Step 2: Estimation of Model Parameters
7.3.2.1 Estimation for the Standard Linear Regression Model
7.3.2.2 Linear Regression Estimation for Complex Sample Survey Data
7.3.3 Step 3: Model Evaluation
7.3.3.1 Explained Variance and Goodness of Fit
7.3.3.2 Residual Diagnostics
7.3.3.3 Model Specification and Homogeneity of Variance
7.3.3.4 Normality of the Residual Errors
7.3.3.5 Outliers and Influence Statistics
7.3.4 Step 4: Inference
7.3.4.1 Inference Concerning Model Parameters
7.3.4.2 Prediction Intervals
7.4 Some Practical Considerations and Tools
7.4.1 Distribution of the Dependent Variable
7.4.2 Parameterization and Scaling for Independent Variables
7.4.3 Standardization of the Dependent and Independent Variables
7.4.4 Specification and Interpretation of Interactions and Nonlinear Relationships
7.4.5 ModelBuilding Strategies
7.5 Application: Modeling Diastolic Blood Pressure with the NHANES Data
7.5.1 Exploring the Bivariate Relationships
7.5.2 Naïve Analysis: Ignoring Sample Design Features
7.5.3 Weighted Regression Analysis
7.5.4 Appropriate Analysis: Incorporating All Sample Design Features
7.6 Exercises
8. Logistic Regression and Generalized Linear Models for Binary Survey Variables
8.1 Introduction
8.2 Generalized Linear Models for Binary Survey Responses
8.2.1 The Logistic Regression Model
8.2.2 The Probit Regression Model
8.2.3 The Complementary Log–Log Model
8.3 Building the Logistic Regression Model: Stage 1, Model Specification
8.4 Building the Logistic Regression Model: Stage 2, Estimation of Model Parameters and Standard Errors
8.5 Building the Logistic Regression Model: Stage 3, Evaluation of the Fitted Model
8.5.1 Wald Tests of Model Parameters
8.5.2 Goodness of Fit and Logistic Regression Diagnostics
8.6 Building the Logistic Regression Model: Stage 4, Interpretation and Inference
8.7 Analysis Application
8.7.1 Stage 1: Model Specification
8.7.2 Stage 2: Model Estimation
8.7.3 Stage 3: Model Evaluation
8.7.4 Stage 4: Model Interpretation/Inference
8.8 Comparing the Logistic, Probit, and Complementary Log–Log GLMs for Binary Dependent Variables
8.9 Exercises
9. Generalized Linear Models for Multinomial, Ordinal, and Count Variables
10. Survival Analysis of Event History Survey Data
11. Multiple Imputation: Methods and Applications for Survey Analysts
11.1 Introduction
11.2 Important Missing Data Concepts
11.2.1 Sources and Patterns of ItemMissing Data in Surveys
11.2.2 ItemMissing Data Mechanisms
11.2.3 Implications of ItemMissing Data for Survey Data Analysis
11.2.4 Review of Strategies to Address ItemMissing Data in Surveys
11.3 An Introduction to Imputation and the Multiple Imputation Method
11.3.1 A Brief History of Imputation Procedures
11.3.2 Why the Multiple Imputation Method?
11.3.3 Overview of Multiple Imputation and MI Phases
11.4 Models for Multiply Imputing Missing Data
11.4.1 Choosing the Variables to Include in the Imputation Model
11.4.2 Distributional Assumptions for the Imputation Model
11.5 Creating the Imputations
11.5.1 Transforming the Imputation Problem to Monotonic Missing Data
11.5.2 Specifying an Explicit Multivariate Model and Applying Exact Bayesian Posterior Simulation Methods
11.5.3 Sequential Regression or “Chained Regressions”
11.6 Estimation and Inference for Multiply Imputed Data
11.6.1 Estimators for Population Parameters and Associated Variance Estimators
11.6.2 Model Evaluation and Inference
11.7 Applications to Survey Data
11.7.1 Problem Definition
11.7.2 The Imputation Model for the NHANES Blood Pressure Example
11.7.3 Imputation of the ItemMissing Data
11.7.4 Multiple Imputation Estimation and Inference
11.7.4.1 Multiple Imputation Analysis 1: Estimation of Mean Diastolic Blood Pressure
11.7.4.2 Multiple Imputation Analysis 2: Estimation of the Linear Regression Model for Diastolic Blood Pressure
11.8 Exercises
12. Advanced Topics in the Analysis of Survey Data