Elementary Survey Sampling, 7th Edition

Richard L. Scheaffer, William Mendenhall, III, R. Lyman Ott, Kenneth G. Gerow

Copyright 2012
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Elementary Survey Sampling 7th Edition by Richard L. Scheaffer/William Mendenhall, III/R. Lyman Ott/Kenneth G. Gerow

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Overview
Meet the Authors
What's New
Features
Table of Contents
Pricing

Overview

ELEMENTARY SURVEY SAMPLING introduces students to the design and analysis of sample surveys via a practical, engaging approach. First, this introductory text begins with brief chapters focused on the important role that sample surveys play in the modern world. Then, each successive chapter builds on this foundation. These chapters start with the problem, describe the methodology needed for solving the problem, and provide the details of the estimation procedure using a compact presentation of the necessary formulas. Each chapter then works out the practical example in full detail. Finally, at the end of each chapter, ELEMENTARY SURVEY SAMPLING includes a wealth of exercises that enable students to continue practicing and to stretch their grasp of the content. The text includes a complete package of interactive statistical tools for implementing all the calculations; text examples are built in to the tools.

Meet the Authors

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Richard L. Scheaffer

Richard L. Scheaffer, Professor Emeritus of Statistics, University of Florida, received his Ph.D. in statistics from Florida State University. Accompanying a career of teaching, research and administration, Dr. Scheaffer has led efforts on the improvement of statistics education throughout the school and college curriculum. Co-author of five textbooks, he was one of the developers of the Quantitative Literacy Project that formed the basis of the data analysis strand in the curriculum standards of the National Council of Teachers of Mathematics. He also led the task force that developed the AP Statistics Program, for which he served as Chief Faculty Consultant. Dr. Scheaffer is a Fellow and past president of the American Statistical Association, a past chair of the Conference Board of the Mathematical Sciences, and an advisor on numerous statistics education projects.

William Mendenhall, III

Dr. Mendenhall served in the Navy in the Korean War and obtained a Ph.D. in Statistics at North Carolina State University. After receiving his Ph.D , he was a professor in the Mathematics Department at Bucknell University in Pennsylvania before moving to Gainesville in 1963 where he was the first chairman of the Department of Statistics at the University of Florida. Dr. Mendenhall published articles in some of the top statistics journals, such as Biometika and Technometrics, however, he is more widely known for his prolific textbook career. He authored or co-authored approximately 13 statistics textbooks and several books about his childhood.

R. Lyman Ott

Lyman Ott earned his Bachelor's degree in Mathematics and Education and Master's degree in Mathematics from Bucknell University, and Ph.D in Statistics from the Virginia Polytechnic Institute. After two years working in statistics in the pharmaceutical industry, Dr. Ott became assistant professor in the Statistic Department at the University of Florida in 1968 and was named associate professor in 1972. He joined Merrell-National laboratories in 1975 as head of the Biostatistics Department and then head of the company's Research Data Center. He later became director of Biomedical Information Systems, Vice President of Global Systems and Quality Improvement in Research and Development, and Senior Vice President Business Process Improvement and Biometrics. He retired from the pharmaceutical industry in 1998, and now serves as consultant and Board of Advisors member for Abundance Technologies, Inc. Dr. Ott has published extensively in scientific journals and authored or co-authored seven college textbooks including Basic Statistical Ideas for Managers, Statistics: A Tool for the Social Sciences and An Introduction to Statistical Methods and Data Analysis. He has been a member of the Industrial Research Institute, the Drug Information Association and the Biometrics Society. In addition, he is a Fellow of the American Statistical Association and received the Biostatistics Career Achievement Award from the Pharmaceutical research and Manufacturers of America in 1998. He was also an All-American soccer player in college and is a member of the Bucknell University Athletic Hall of Fame.

Kenneth G. Gerow

Ken Gerow, Professor of Statistics at the University of Wyoming, earned a Bachelor's degree in Wildlife Biology (minor, Statistics) from the University of Guelph. He has been on the faculty at U.W. ever since completing his Ph.D. in Statistics at Cornell University. He has devoted his career to empowering biologists with statistical concepts and tools. His publications are in a wide range of biological disciplines and journals, with much of his work being directed towards fisheries science. A distaste for making students do arithmetic (rather than thinking about science and valid use of appropriate statistical tools) led to the creation of the interactive Excel tools that accompany this text.

What's New

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Integrates new software: By using software that significantly decreases the need for long arithmetic, the authors have allowed students to focus less on arithmetic, and instead on the valid use of appropriate statistical tools.
New Icons: Excel Tools icons have been added to the boxed equations and example solutions, so as to direct students to use Excel when appropriate.
More concise data: The data for examples and exercises is now more concise and is also incorporated with the electronic supplements.
Updated Appendices: the Appendices for this edition have been updated to be more concise and user-friendly.

Features

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Bridges the gap between classroom and practice in two significant ways: First, select sections on weights in unequal probability sampling. Then, they expanded the treatment of nonresponse in Chapter 11, including the use of imputation as a technique for handling some types of nonresponse.
Emphasizes two cutting-edge sampling techniques: the bootstrap, a modern technique for establishing margins of error and confidence intervals in complex designs, and an adaptive sampling technique for improving estimates while the field work is in progress.
Incorporates compatible computations: ELEMENTARY SURVEY SAMPLING’s computations are compatible with modern statistical software, using much fewer hand calculation formulas.
Utilizes detailed simulations: Key statistical concepts are demonstrated using step-by-step simulations.
Emphasizes practical aspects: topics include sources of errors in surveys, methods of data collection, designing questionnaires, and guidelines for planning surveys.
Expands Student Understanding: The "Experiences with Real Data" sections at the end of most chapters enable students to work with both large and small projects, some requiring computations to be handled by a computer, giving them valuable experience.

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1. INTRODUCTION.
2. ELEMENTS OF THE SAMPLING PROBLEM.
Introduction. Technical Terms. How to Select the Sample: The Design of the Sample Survey. Sources of Errors in Surveys. Designing a Questionnaire. Planning a Survey. Summary.
3. SOME BASIC CONCEPTS OF STATISTICS.
Introduction. Summarizing Information in Populations and Samples: The Infinite Population Case. Summarizing Information in Populations and Samples: The Finite Population Case. Sampling Distributions. Covariance and Correlation. Estimation. Summary.
4. SIMPLE RANDOM SAMPLING.
Introduction. How to Draw a Simple Random Sample. Estimation of a Population Mean and Total. Selecting the Sample Size for Estimating Population Means and Totals. Estimation of a Population Proportion. Comparing Estimates. Summary.
5. STRATIFIED RANDOM SAMPLING.
Introduction. How to Draw a Stratified Random Sample. Estimation of a Population Mean and Total. Selecting the Sample Size for Estimating Population Means and Totals. Allocation of the Sample. Estimation of a Population Proportion. Selecting the Sample Size and Allocating the Sample to Estimate Proportions. Additional Comments on Stratified Sampling. An Optimal Rule for Choosing Strata. Stratification after Selection of the Sample. Double Sampling for Stratification. Summary.
6. RATIO, REGRESSION, AND DIFFERENCE ESTIMATION.
Introduction. Surveys that Require the Use of Ratio Estimators. Ratio Estimation Using Simple Random Sampling. Selecting the Sample Size. Ratio Estimation in Stratified Random Sampling. Regression Estimation. Difference Estimation. Relative Efficiency of Estimators. Summary.
7. SYSTEMATIC SAMPLING.
Introduction. How to Draw a Systematic Sample. Estimation of a Population Mean and Total. Estimation of a Population Proportion. Selecting the Sample Size. Repeated Systematic Sampling. Further Discussion of Variance Estimators. Summary.
8. CLUSTER SAMPLING.
Introduction. How to Draw a Cluster Sample. Estimation of a Population Mean and Total. Equal Cluster Sizes; Comparison to Simple Random Sampling. Selecting the Sample Size for Estimating Population Means and Totals. Estimation of a Population Proportion. Selecting the Sample Size for Estimating Proportions. Cluster Sampling Combined with Stratification. Cluster Sampling with Probabilities Proportional to Size. Summary.
9. TWO-STAGE CLUSTER SAMPLING.
Introduction. How to Draw a Two-Stage Cluster Sample. Unbiased Estimation of a Population Mean and Total. Ratio Estimation of a Population Mean. Estimation of a Population Proportion. Sampling Equal-Sized Clusters. Two-Stage Cluster Sampling with Probabilities Proportional to Size. Summary.
10. ESTIMATING THE POPULATION SIZE.
Introduction. Estimation of a Population Size Using Direct Sampling. Estimation of a Population Size Using Inverse Sampling. Choosing Sample Sizes for Direct and Inverse Sampling. Estimating Population Density and Size from Quadrat Samples. Estimating Population Density and Size from Stocked Quadrats. Adaptive Sampling. Summary.
11. SUPPLEMENTAL TOPICS.
Introduction. Interpenetrating Subsamples. Estimation of Means and Totals over Subpopulations. Random-Response Model. Use of Weights in Sample Surveys. Adjusting for Nonresponse. Imputation. Selecting the Number of Callbacks. The Bootstrap. Summary.
12. SUMMARY.
Summary of the Designs and Methods. Comparisons among the Designs and Methods.
Appenidices.
References and Bibliography Tables. Derivation of Some Main Results. Macros for MINITAB. Macros for SAS. Data Sets.
Selected Answers.
Index.