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Analyzing Complex Survey Data
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Analyzing Complex Survey Data

Second Edition
  • Eun Sul Lee - Oregon Health Sciences University, Portland , USA, University of Texas Health Science Center, Houston, USA
  • Ronald N. Forthofer - University of Texas Health Science Center, Houston, USA
Volume: 71
Other Titles in:
Social Research Methods
November 2005 | 104 pages | SAGE Publications, Inc
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This book examines ways to analyze complex surveys, and focuses on the problems of weights and design effects. This new edition incorporates recent practice of analyzing complex survey data, introduces the new analytic approach for categorical data analysis (logistic regression), reviews new software and provides an introduction to the model-based analysis that can be useful analyzing well-designed, relatively small-scale social surveys.
 
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

Good fit for a doctoral seminar on survey research. An increasingly important topic given the widespread reliance in many fields on secondary data from large, complex surveys.

Professor Gregg VanRyzin
Public Administration Dept, Rutgers University
August 1, 2010
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