Analyzing Complex Survey Data

by Eun Sul Lee, Ronald N. Forthofer

Overview

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.

Product Details

ISBN-13:	9781506320434
Publisher:	SAGE Publications
Publication date:	09/22/2005
Series:	Quantitative Applications in the Social Sciences , #71
Sold by:	Barnes & Noble
Format:	eBook
Pages:	104
File size:	2 MB

About the Author

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)
Ron Forthofer has a B.S. in mathematics from the University of Dayton, and an M.S. in mathematical statistics and a Ph.D. in biostatistics from the University of North Carolina at Chapel Hill.  He is a retired professor of biostatistics from the University of Texas School of Public Health in Houston.  He also spent time with the National Center for Health Statistics and with Hoechst Pharmaceutical in Germany.  Since Ron retired and moved to Colorado in 1991, he has been an activist for peace and social justice, working on health care, trade issues, international peace, and Social Security.  In his spare time, he ran for Congress in 2000 and for Governor in 2002 for the Green Party.

Table of Contents

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