With large health surveys becoming increasingly available for public use, researchers with little experience in survey methods are often faced with analyzing data from surveys to address scientific and programmatic questions. This practical book provides statistical techniques for use in survey analysis, making health surveys accessible to statisticians, biostatisticians, epidemiologists, and health researchers. The authors clearly explain the theory and methods of survey analysis along with real–world applications. They draw on their work at the National Institutes of Health as well as up–to–date information from across the literature to present:
- The sampling background necessary to understand health surveys.
- The application of such techniques as t–tests, linear regression, logistic regression, and survival analysis to survey data.
- The use of sample weights in survey data analysis.
- Dealing with complications in variance estimation in large health surveys.
- Applications involving cross–sectional, longitudinal, and multiple cross–sectional surveys, and the use of surveys to perform population– based case–control analyses.
- Guidance on the correct use of statistical methods found in software packages.
- Extensive bibliography.
Statistical Analysis with Survey Data.
Sample Weights and Imputation.
Additional Issues in Variance Estimation.
Analysis of Longitudinal Surveys.
Analyses Using Multiple Surveys.
Population–Based Case–Control Studies.
A strength of this book is the exercises at the ends of each chapter [...]. This makes the book ideal either for independent study or for a survey methods course at final year undergraduate or postgraduate level. Since the book is based on the two authors′ many years of experience working in this field, it shows high levels of sound judgement and good advice. (Biometrics, 2000)
The analytic components of books about survey sampling often restrict their discussion to the analysis of relatively simple parameters of interest, such as population means and linear regression slopes. On the other hand, texts not devoted to survey sampling rarely discuss in detail the effect of complex sample designs on data analysis. This book provides a great service to the health research community by tying together the tools of modern statistical analysis and survey research in one package. Korn and Graubard write clearly and concisely, using well–chosen examples to illuminate potentially confusing concepts. (JASA, March 2001)
If you are involved in analyzing data from very large surveys, then you will want to buy this book. Statisticians who enjoy expanding their horizons in statistics by reading practical and useful statistics books should also get a copy of the book. More and more of this health–survey data becomes available all the time, so perhaps data analysis no longer needed in industry can find a new home somewhere analyzing health–survey data. (Technometrics, May 2001, Vol. 42, No. 4)
"...provides a great service to the health research community by tying together...tools of modern statistical analysis and survey research...." (Journal of the American Statistical Association)