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.
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.
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
