Analyzing Complex Survey Data

Series Editor's Introduction
Acknowledgments
1. Introduction
2. Sample Design and Survey Data
Types of Sampling
The Nature of Survey Data
A Different View of Survey Data
3. Complexity of Analyzing Survey Data
Adjusting for Differential Representation: The Weight
Developing the Weight by Poststratification
Adjusting the Weight in a Follow-Up Survey
Assessing the Loss or Gain in Precision: The Design Effect
The Use of Sample Weights for Survey Data Analysis
4. Strategies for Variance Estimation
Replicated Sampling: A General Approach
Balanced Repeated Replication
Jackknife Repeated Replication
The Bootstrap Method
The Taylor Series Method (Linearization)
5. Preparing for Survey Data Analysis
Data Requirements for Survey Analysis
Importance of Preliminary Analysis
Choices of Method for Variance Estimation
Available Computing Resources
Creating Replicate Weights
Searching for Appropriate Models for Survey Data Analysis
6. Conducting Survey Data Analysis
A Strategy for Conducting Preliminary Analysis
Conducting Descriptive Analysis
Conducting Linear Regression Analysis
Conducting Contingency Table Analysis
Conducting Logistic Regression Analysis
Other Logistic Regression Models
Design-Based and Model-Based Analyses
7. Concluding Remarks
Notes
References
Index
About the Authors

Experience & Interest
Teaching and research in public health (Survey design and analysis in mental health and nutrition, demographic analysis of infant mortality and reproductive health, biostatical and epidemiologic analysis of cancer registry data and environmental surveys, statistical methods in epidemiology)